prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What is the future direction mentioned in the conclusion?

Read the following text and answer briefly.

Context 1: Paper Info

Title: Is In-hospital Meta-information Useful for Abstractive Discharge Summary Generation?
Publish Date: 10 Mar 2023
Author List: Mamoru Komachi (from Tokyo Metropolitan University), Takashi Okumura (from Kitami Institute of Technology), Hiromasa Horiguchi (from National Hospital Organization), Yuji Matsumoto

Figure

Fig  1 Example of part of a discharge summary which is a dummy we created 
Fig  2  Overview of our proposed method A new feature embedding layer encoding hospital, physician, disease, and length of stay is added to the standard transformer architecture The figure shows an example of hospital embedding 
Statistics of our data for experiment 
of summarization models with different meta-information The best results are highlighted in bold Each score is the average of three models with different seeds The BS and BR indicate BERTScore and BLEURT, respectively 
Statistics on the number of cases handled by physicians C/P denotes Cases/Physician, which indicates how many cases an individual physician has Method of Grouping Physician IDs A most naive method of mapping physician IDs to features is without any grouping process The data contains 4,846 physicians, so |M | was set to 4,846 However it caused our model's training to be unstable This might be due to the many physician IDs appearing for the first time in the test time Table

abstract

During the patient's hospitalization, the physician must record daily observations of the patient and summarize them into a brief document called "discharge summary" when the patient is discharged  Automated generation of discharge summary can greatly relieve the physicians' burden, and has been addressed recently in the research community 
Most previous studies of discharge summary generation using the sequenceto-sequence architecture focus on only inpatient notes for input  However, electric health records (EHR) also have rich structured metadata (e g , hospital, physician, disease, length of stay, etc ) that might be useful  This paper investigates the effectiveness of medical meta-information for summarization tasks 
We obtain four types of meta-information from the EHR systems and encode each meta-information into a sequence-to-sequence model  Using Japanese EHRs, meta-information encoded models increased ROUGE-1 by up to 4 45 points and BERTScore by 3 77 points over the vanilla Longformer  Also, we found that the encoded meta-information improves the precisions of its related terms in the outputs 
Our results showed the benefit of the use of medical meta-information 

INTRODUCTION

Clinical notes are written daily by physicians from their consults and are used for their own decision-making or coordination of treatment  They contain a large amount of important data for machine learning, such as conditions, laboratory tests, diagnoses, procedures, and treatments  While invaluable to physicians and researchers, the paperwork is burdensome for physicians ,  
Discharge summaries, a subset of these, also play a crucial role in patient care, and are used to share information between hospitals and physicians (see an example in Figure )  It is created by the physician as a summary of notes during hospitalization at the time of the patient's discharge, which is known to be very time-consuming 
Researchers have begun to apply automatic summarization techniques to address this problem -   Previous studies used extractive or abstractive summarization methods, but most of them focused on only progress notes for inputs  Properly summarizing an admission of a patient is a quite complex task, and requires various meta-information such as the patient's age, gender, vital signs, laboratory values and background to specific diseases 
Therefore, discharge summary generation needs more medical meta-information, than similar but narrower tasks such as radiology report generation  However, what kind of meta-information is important for summarization has not been investigated, even though it is critical not only for future research on medical summarization but also for the policy of data collection infrastructure 
In this paper, we first reveal the effects of meta-information on neural abstractive summarization on admissions  Our model is based on an encoder-decoder transformer with an additional feature embedding layer in the encoder (Figure )  Hospital, physician, disease, and length of stay are used as meta-information, and each feature is embedded in the vector space 
For experiments, we collect progress notes, discharge summaries and coded information from the electronic health record system, which are managed by a largest multi-hospital organization in Japan  Our main contributions are as follows: • We found that a transformer encoding meta-information generates higher quality summaries than the vanilla one, and clarified the benefit of using meta-information for medical summarization tasks 
• We found that a model encoding disease information can produce proper disease and symptom words following the source  In addition, we found that the model using physician and hospital information can generate symbols that are commonly written in the summary  • We are the first to apply the abstractive summarization method to generate Japanese discharge summaries 
In the studies of summarization of medical documents, it is common to retrieve key information such as disease, examination result, or medication from EHRs -   Other researchs more similar to our study targeted to help physicians get the point of medical documents quickly by generating a few key sentences -  
Studies generating contextualized summaries can be categorized by the type of model inputs and architectures  Some studies produced a whole discharge summary using structured data for input - The sensitivity of the gram stain for bacterial meningitis is about 60%, and the sensitivity of the culture is not high either 
Also, the glucose in the cerebrospinal fluid would have been slightly lower  Although no definitive diagnosis could be made, bacterial meningitis was the most suspicious disease  The causative organism was assumed to be MRSA, and vancomycin and meropenem (meningitis dose) were used to cover a wide range of enteric bacteria 
a whole discharge summary from free-form inpatient records -   The free-form data is more challenging since it is noisier than structured data  In inputting of the free-form data, extractive summarization methods, which extract sentences from the source, are commonly used , -   On the other hands, an encoder-decoder model was used for abstractive summarization , , with a limited number of studies 
The various issues in the abstractive generation of discharge summary would be studied in the future  Studies using medical meta-information have long been conducted on a lot of tasks -   In abstractive summarization on discharge summary,  developed a model incorporating similarity of progress notes and information of the record author 
They presented an idea of integrating meta-information into the abstractive summarization model on medical documents, but did not reveal how meta-information would affect the quality of the summaries  Our method is based on the encoder-decoder transformer model  The transformer model is known for its high performance and has been widely used in recent studies, thus it is suitable for our purpose 
As shown in Figure , the standard input to a transformer's encoder is created by a token sequence T = [t 0 , t 1 ,    , t i ] and position sequence P = [p 0 , p 1 ,    , p i ], where i is the maximum input length  The token and position sequences are converted into token embeddings E T and positional embeddings E P by looking up the vocabulary tables 
The sum of E T and E P is input into the model  In this paper, we attempt to encode meta-information to feature embeddings  We follow the segment embeddings of BERT and the language embeddings of XLM , which provide additional information to the model  It is not a new idea but is suitable for our validation 
Our method is formulated as follows: Let M be feature type, M ∈ {Vanilla, Hospital, Physician, Disease, Length of stay}, since we set five types of features  Feature embeddings E M is created by looking up the feature table where m j is featue value (e g , pysician ID, disease code, etc ) and |M | is the maximum number of differences in a feature 
In our study, |M | is set to four different values depending on features  Specifically, they are as follows  a) Hospital: As shown in Table , the data includes five hospital records  They were obtained mechanically from the EHR system  b) Physician: Physicians are also managed by IDs in the EHR systems  We hashed the physician IDs into 485 groups containing 10 people each 
Specifically, as a naive strategy, we shuffled and listed the cases within each hospital, and hashed them into groups in the order of appearance of the physician IDs  So each group has the information about the relevance of the hospitals  The reason for employing a grouping strategy is described in Appendix A 
c) Disease: Two types of disease information exist in our EHRs: disease names and disease codes called ICD-10   We did not use any disease names in the inputs for our experiment  Instead, we encoded diseases with the first three letters of the ICD-10 code, because they represent well the higher level concept 
The initial three letters of the ICD-10 codes are arranged in the order of an alphabetic letter, a digit, and a digit, so there are a total of 2,600 ways to encode a disease  In our data, some ICD-10 codes were missing, although all disease names were systematically obtained from the EHR system  For such cases, we converted the disease names into ICD-10 codes using MeCab with the J-MeDic (MANBYO 201905) dictionary 
Also, diseases can be divided into primary and secondary diseases, but we only deal with the primary diseases  d) Length of stay: The length of stay can be obtained mechanically from the EHR system and the maximum value was set to 1,000 days  We set |M | for vanilla, hospital, physician, disease, and length of stay to 1, 5, 485, 2,600, and 1,000, respectively  
The vanilla embedding is prepared for the baseline in our experiment and to equalize the total number of parameters with the other models  The input to our model is the sum of E T , E P and E M   We also prepare an extra model with all features for our experiments  This takes all four feature embeddings (hospital, physician, disease, and length of stay) added to the encoder 

Datasets and Metrics

We evaluated our proposed method on a subset of data from National Hospital Organization (NHO), the largest multiinstitutional organization in Japan  The statistics of our data are shown in Table  , which includes 24,630 cases collected from five hospitals  Each case includes a discharge summary and progress notes for the days of stay 
The data are randomly split into 22,630, 1,000, and 1,000 for train, validation, and test, respectively  Summarization performances are reported in ROUGE-1, ROUGE-2, ROUGE-L and BERTScore in terms of F1  In addition, we also employed BLEURT , which models human judgment 

Architectures and Hyperparameters

Due to our hardware constraints we need a model that is computationally efficient, so we employed the Longformer instead of the conventional transformer  Longformer can    In our model, number of layers, window size, dilation, input sequence length, output sequence length, batch size, learning rate and number of warmup steps are 8, 256, 1, 1024, 256, 4, 3e-5 and 1K, respectively 
Other hyperparameters are the same as in the original Longformer, except for the maximum number of epochs is not fixed and the best epoch  It is selected for each training using the validation data based on ROUGE-1  Also, the original Longformer imports pretrained-BART parameters to initial values, but we do not use pre-trained Japanese BART in this study 
We used three GeForce RTX 2080 TI for our experiments  Our vocabulary for preparing input to Longformer is taken from UTH-BERT , which is pre-trained on the Japanese clinical records  Since the vocabulary of UTH-BERT is trained by WordPiece , we also tokenize our data with WordPiece  However, the vocabulary does not include white space and line breaks, which cannot be handled, so we add those two tokens to the vocabulary, resulting in a total size of 25,002 
The vocabulary has all tokens in full characters, so we normalized full-wdith characters by converting all alphanumeric and symbolic characters to half-width for byte fallback  , we found that all the models with encoded medical meta-information perform better in ROUGE-1, ROUGE-L and BLEURT than the vanilla Longformer 
However, in BERTScore, only hospital and disease models outperform the vanilla  Specifically, disease information is most effective, improving ROUGE-1, ROUGE-2, ROUGE-L, BERTScore and BLEURT by 4 45, 0 73, 3 12, 3 77 and 0 21 points over the vanilla model, respectively  This seems to be because disease information and the ICD-10 ontology efficiently cluster groups with similar representations 
In contrast, in ROUGE-2 and ROUGE-L, the model with physician embedding is inferior to the vanilla model  This seems to be a negative effect of grouping physicians without any consideration of their relevance  It would be better to cluster them by department, physician attributes, similarity of progress notes, etc  Regarding low ROUGE-2 scores in all models, a previous study using the English data set also reported a low ROUGE-2 score of about 5%, which may indicate an inherent difficulty in discharge summary generation 
In BERTScore, the models with the physician and the length of stay did not reach the performance of the vanilla model, suggesting that the system's outputs are semantically inferior  The model with all features performed the lowest of all models in BERTScore  The reason for the low score of the model with all features seems to be that its number of parameters in feature embedding was four times larger than that of the model with the individual feature, and the amount of training data was insufficient 
In BLEURT, all models with meta-information outperform vanilla, which suggests that they are more natural to humans  To analyze the influence of encoded meta-information on the outputs, we evaluate the precisions of the generated text  Specifically, we measure the probability that the generated words are included in the gold summary to investigate if the proper words are generated 
Some previous studies on faithfulness, which also analyze the output of summarization, have employed words or entities -   In this study, we focused on words, not entities, because we wanted to visualize expressions that are not only nouns  The words were segmented by MeCab with the J-MeDic  For each segmented word, the numeral and symbol labels were assigned as parts of speech by MeCab, the morphological analyzer, while the disease and symptom were assigned by the J-Medic dictionary 
The results, shown in Figure , indicate that the encoded disease information leads to generate more proper disease and symptom words  This indicates that the meta-information successfully learns disease-related expressions  The encoded hospital or physician information also improved the precision of symbols generation 
This suggests that different hospitals and physicians have different description habits (e g , bullet points such as "•", "*" and "-", punctuation such as "。" and " ", etc ), which can be grouped by meta-information  In this paper, we conducted a discharge summary generation experiment by adding four types of information to Longformer and verified the impact of the meta-information 
The results showed that all four types of information exceeded the performance of the vanilla Longformer model, with the highest performance achieved by encoding disease information  We found that meta-information is useful for abstractive summarization on discharge summaries  Our limitations are that we used Japanese EHR, the limited number of tested features and not performing human evaluations 
As for the efficacy of the meta-information, we believe that our results are applicable to non-Japanese, but it is left as Fig    The precisions of words in the generated summaries  The vertical axis shows the probability that the words exist in the gold summary  a future work  Other meta-information may be worth verifying such as the patient's gender, age, race, religion and used EHR system, etc 
It is hard to collect a large amount of medical information and process it into meta-information, so we may need to develop a robust and flexible research infrastructure to conduct a more large scale cross-sectional study in the future  In the discharge summary generation task, which demands a high level of expertise, the human evaluation requires a lot of physicians' efforts and it is a very high cost which is unrealistic 
This is a general issue in tasks dealing with medical documents, and this study also could not perform human evaluations  On this research, informed consent and patient privacy are ensured in the following manner  Notices about their policy and the EHR data usage are posted at the hospitals  The patients who disagree with the policies can request opt-out and are excluded from the archive 
In case of minors and their parents, followed the same manner  In the case of minors and their parents are same  To conduct a research on the archive, researchers must submit their research proposals to the institutional review board  After the proposal is approved, the data is anonymized to build a dataset for analysis 
The data is accessible only in a secured room at the NHO headquarters, and only statistics are brought out of the secured room, for protection of patients' privacy  In the present research, the analysis was conducted under the IRB approval (IRB Approval No : Wako3 2019-22) of the Institute of Physical and Chemical Research (RIKEN), Japan, which has a collaboration agreement with the National Hospital Organization 
This data is not publicly available due to privacy restrictions  shows the detailed number of cases handled by physicians  In all hospitals, there is a large difference between the median and the maximum of cases/physician  This indicates that a few physicians handle a large number of cases and many physicians handle fewer cases 
It is impossible to avoid physician IDs first seen at test time without some process that averages the number of cases a physician holds 



ground_truth: Verifying other meta-information such as patient's gender, age, race, etc.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What are some reasons for the lack of data sharing in archaeobotany?

Read the following text and answer briefly.

Context 2: Sowing the Seeds of Future Research: Data Sharing, Citation and Reuse in Archaeobotany
Reading: Sowing the Seeds of Future Research: Data Sharing, Citation and Reuse in Archaeobotany
University of Oxford, GB
Lisa is a post-doctoral research fellow at All Souls College, University of Oxford  Her publications include the co-authored volume The Rural Economy of Roman Britain (Britannia Monographs, 2017)  Her research interests are focussed on agricultural practices in the later prehistoric and Roman period and the utilisation of archaeobotanical data to investigate human-plant relationships 
The practices of data sharing, data citation and data reuse are all crucial aspects of the reproducibility of archaeological research  This article builds on the small number of studies reviewing data sharing and citation practices in archaeology, focussing on the data-rich sub-discipline of archaeobotany  Archaeobotany is a sub-discipline built on the time-intensive collection of data on archaeological plant remains, in order to investigate crop choice, crop husbandry, diet, vegetation and a wide range of other past human-plant relationships  Within archaeobotany, the level and form of data sharing is currently unknown  This article first reviews the form of data shared and the method of data sharing in 239 articles across 16 journals which present primary plant macrofossil studies  Second, it assesses data-citation in meta-analysis studies in 107 articles across 20 journals  Third, it assesses data reuse practices in archaeobotany, before exploring how these research practices can be improved to benefit the rigour and reuse of archaeobotanical research 
Keywords: Archaeobotany, Data reuse, Data sharing, Open science
How to Cite: Lodwick, L , 2019  Sowing the Seeds of Future Research: Data Sharing, Citation and Reuse in Archaeobotany  Open Quaternary, 5(1), p 7  DOI: http://doi org/10 5334/oq 62
Accepted on 29 May 2019 Submitted on 25 Mar 2019
Archaeology is a discipline built on the production and analysis of quantitative data pertaining to past human behaviour  As each archaeological deposit is a unique occurrence, ensuring that the data resulting from excavation and analysis are preserved and accessible is crucially important  Currently, there is a general perception of a low level of data sharing and reuse  Such a low level of data availability would prevent the assessment of research findings and the reuse of data in meta-analysis (Kansa & Kansa 2013; Moore & Richards 2015)  As observed across scientific disciplines, there is a major problem in the reproduction of scientific findings, commonly known as the ‘replication crisis’ (Costello et al  2013)  A range of intersecting debates contribute to this, including access to academic findings (open access), open data, access to software and access to methodologies, which can be broadly grouped as open science practices  Without these, the way that scientific findings can be verified and built upon is impaired  Questions of reproducibility have been raised in recent years in archaeology, with considerations of a range of practices which can improve the reproducibility of findings, and a recent call for the application of open science principles to archaeology (Marwick et al  2017)  Discussion has so far focussed on access to grey literature (Evans 2015), data sharing (Atici et al  2013), data citation practices (Marwick & Pilaar Birch 2018) and computational reproducibility (Marwick 2017), with a focus on lithics, zooarchaeological evidence, and archaeological site reports 
Quantitative assessments of current levels of data sharing, data citation and reuse remain limited in archaeology  The focus of evaluation has been on the uptake of large-scale digital archives for the preservation and dissemination of digital data, such as the Archaeology Data Service (ADS), utilised by developer-led and research projects, and recommended for use by many research funders in the UK (Richards 2002; Wright and Richards 2018)  Much less focus has been paid to the data-sharing practices of individuals or small-groups of university-based researchers who may be disseminating their research largely through journal articles  Recent work on the availability of data on lithics assemblages found a low level of data sharing (Marwick & Pilaar Birch 2018) and there are perceptions of low levels of data reuse (Huggett 2018; Kintigh et al  2018)  Within zooarchaeology numerous studies have explored issues of data sharing and reuse (Kansa & Kansa 2013, 2014), and the sub-discipline is seen as one of the most advanced areas of archaeology in regards to open science (Cooper & Green 2016: 273)  Beyond zooarchaeology, however, explicit discussion has remained limited 
This paper assesses data sharing and reuse practices in archaeology through the case study of archaeobotany – a long established sub-discipline within archaeology which has well-established principles of data recording  Archaeobotany is an interesting case study for data sharing in archaeology as it straddles the division of archaeology between scientific and more traditional techniques  Quantitative data on archaeological plant remains are also of interest to a range of other fields, including ecology, environmental studies, biology and earth sciences  The key issues of data sharing and data reuse (Atici et al  2013) have been touched upon in archaeobotany over the past decade within broader discussions on data quality (Van der Veen, Livarda & Hill 2007; Van der Veen, Hill & Livarda 2013)  These earlier studies focussed on the quality and availability of archaeobotanical data from developer-funded excavations in Britain and Cultural Resource Management in North America (Vanderwarker et al  2016: 156)  However, no discussion of data-sharing and reuse in academic archaeobotany occurred  A recent review of digital methods in archaeobotany is the notable exception, with discussions of the challenges and methods of data sharing (Warinner & d’Alpoim Guedes 2014) 
Currently, we have no evidence for the levels of data sharing and reuse within archaeobotany  This article provides the first quantitative assessment of 1) data publication in recent archaeobotanical journal articles 2) data citation in recent archaeobotanical meta-analysis 3) the reuse of archaeobotanical datasets, in order to assess whether practices need to change and how such changes can take place 
2  Data Publication and Re-use Practices in Archaeobotany
2 1  History of data production and publication
Archaeobotanical data falls within the category of observational data in archaeology (Marwick & Pilaar Birch 2018)  Archaeobotanical data is considered as the quantitative assessment of plant macrofossils present within a sample from a discrete archaeological context, which can include species identification, plant part, levels of identification (cf  – confer or “compares to”), and a range of quantification methods including count, minimum number of individuals, levels of abundance and weight (Popper 1988)  Archaeobotanical data is usually entered into a two-way data table organised by sample number  Alongside the counts of individual taxa, other information is also necessary to interpret archaeobotanical data, including sample volume, flot volume, charcoal volume, flot weight, level of preservation, sample number, context number, feature number, feature type and period  Beyond taxonomic identifications, a range of other types of data are increasingly gathered on individual plant macrofossils (morphometric measurements, isotopic values, aDNA) 
Archaeobotanical training places a strong emphasis on recording data on a sample-by-sample basis (Jacomet & Kreuz 1999: 138–139; Jones & Charles 2009; Pearsall 2016: 97–107)  Time-consuming methodologies utilised in the pursuit of accurate sample-level data recording include sub-sampling and splitting samples into size fractions and counting a statistically useful number of items per sample (Van der Veen & Fieller 1982)  The creation of sample-level data means analysis is often undertaken on the basis of individual samples, for instance the assessment of crop-processing stages and weed ecological evidence for crop husbandry practices  The analysis of sample level data also enables archaeobotanical finds to be integrated alongside contextual evidence from archaeological sites  Requirements for the publication of this data are in place in some archaeological guidelines, for instance current Historic England guidelines for archaeological practice in England (Campbell, Moffett & Straker 2011: 8) 
From the earliest archaeobotanical reports, such as Reid’s work at Roman Silchester, the sample from which plant remains were recovered was noted (Lodwick 2017a), but often results were reported as a list of taxa, or long catalogues of detailed botanical descriptions with seed counts, such as Knörzer’s work at Neuss (Knörzer 1970)  Early systematic archaeobotanical reports displayed data within in-text tables, for example Jones’s work at Ashville (Jones 1978) and the two-way data table has been the standard form of reporting archaeobotanical data ever since  Often data tables are presented within book chapters or appendices, but the financial, space and time constraints of book publishing are limiting  Furthermore, there is the perception that specialist data was not necessary for publication (Barker 2001)  Hence, alternative methods of the dissemination of specialist archaeological data were pursued in the later twentieth century 
From the 1980s, archaeobotanical data tables were often consigned to microfiche following a Council for British Archaeology and Department of Environment report (Moore & Richards 2015: 31), with the example of the excavation of Roman Colchester where the contents of all archaeobotanical samples were available on microfiche (Murphy 1992)  An alternative in the 2000s was providing data tables on CD Rom as seen, for instance, in the CD accompanying the study of a Roman farmstead in the Upper Thames Valley (Robinson 2007) or the One Poultry excavations in London (Hill and Rowsome 2011)  Meanwhile, the inception of the Archaeology Data Service, a digital repository for heritage data, in 1996 meant archaeological datasets were increasingly digitally archived, for instance the data from the Channel Tunnel Rail Link Project (Foreman 2018) or a recent large-scale research excavation at Silchester (University of Reading 2018)  In these cases, archaeobotanical data is available to download as a  csv file 
Whilst the data publication strategy of large excavations was shifting, the availability of data from post-excavation assessment reports has remained challenging  So-called ‘grey literature’ results from the initial evaluation stage of developer-funded investigations and accompanying post-excavation assessment often contain a semi-quantitative evaluation of archaeobotanical samples on a scale of abundance  Whilst paper reports were initially deposited with county Historic Environment Records, a process of digitisation focussing on the Roman period has meant many pdfs are now available through the ADS (Allen et al  2018), whilst born-digital reports are now deposited through OASIS (Online AccesS to the Index of archaeological investigationS), as part of the reporting process (Evans 2015), althought the extent to which specialist appendices are included is variable 
These varying ‘publication’ strategies means archaeobotanical data is often available somewhere for recent developer-funded excavations and large-scale developer-funded excavations, even if much of this data is as a printed table or  pdf file (Evans 2015; Evans and Moore 2014)  However, academic journals are typically perceived as the most high-status publication venue for archaeobotanical data, and a crucial publication venue for academics in order to comply with institutional requirements and the norms of career progression  Aside from the problem of access to pay-walled journals by those without institutional subscriptions to all journals, the publication of primary data alongside research articles faces various problems, from the outright lack of inclusion of data, to problematic curation of supplementary data and a lack of peer review of data (Costello et al  2013; Warinner and d’Alpoim Guedes 2014: 155; Whitlock, 2011)  The extent of these problems for archaeobotany is currently unknown  Given the growth in archaeobotanical data production as methodologies are introduced into many new regions and periods over the last decade, it is vital that we know whether the mass of new data being produced is made available and is being reused 
Recent important advances within archaeobotanical data sharing have focussed on the construction of the ARBODAT database, developed by Angela Kreuz at the Kommission für Archäologische Landesforschung in Hessen  The database is used by a range of researchers in Germany, the Czech Republic, France and England (Kreuz & Schäfer 2002)  Data sharing enabled by the use of this database has facilitated research on Neolithic agriculture in Austria, Bulgaria and Germany (Kreuz et al  2005), and Bronze Age agriculture in Europe (Stika and Heiss 2012)  The use of this database makes data integration between specialists easier due to the shared data structure and metadata description, but often the primary archaeobotanical data is not made publicly available 
2 2  Meta-analysis in archaeobotany
Beyond the need to preserve information, a key reason for the formal sharing of archaeobotanical data is in its reuse to facilitate subsequent research  There has been a long-standing concern within archaeobotany with the need to aggregate datasets and identify temporal and spatial patterns  The palaeobotanist Clement Reid maintained his own database of Quaternary plant records in the late nineteenth century (Reid 1899), which formed the foundation of Godwin’s Quaternary database (Godwin 1975)  Mid-twentieth century studies of prehistoric plant use compiled lists of archaeobotanical materials incorporating full references and the location of the archive (Jessen & Helbaek 1944)  The International Work Group for Palaeoethnobotany was itself founded in 1968 in part with the aim to compile archaeobotanical data, first realised through the publication of Progress in Old World Palaeoethnobotany (Van Zeist, Wasylikowa & Behre 1991), and subsequently through the publication of annual lists of new records of cultivated plants (Kroll 1997) 
To take England as an example, regional reviews produced by state heritage authorities have provided catalogues of archaeobotanical datasets in particular time periods and regions (e g  Murphy 1998)  When one archaeobotanist has undertaken the majority of study within a region, pieces of synthesis within books have provided a relatively comprehensive review, for instance in the Thames Valley, UK (Lambrick & Robinson 2009)  Over the last decade regional synthesis has occurred within several funded reviews which produced catalogues of sites with archaeobotanical data (Lodwick 2014; McKerracher 2018; Parks 2012) and a series of funded projects in France have enabled regional synthesis (Lepetz & Zech-Matterne 2017)  However, many of these reviews are not accompanied by an available underlying database, and draw upon reports which are themselves hard to access 
Through the 1990s and 2000s, a series of databases were constructed in order to collate data from sites in a particular region and facilitate synthetic research  However, these databases have all placed the role of data archiving onto later projects specifically funded to collate data, rather than sourcing datasets at the time of publication  Such a model is unsustainable, and is unlikely to result in all available datasets being compiled  The Archaeobotanical Computer Database (ABCD), published in 1996 in the first issue of Internet Archaeology, contained much of the archaeobotanical data from Britain available at the time of publication, largely at the level of individual samples  The database was compiled between 1989 and 1994 and is still accessible through the accompanying online journal publication (Tomlinson & Hall 1996)  The ABCD made major contributions to recent reviews of the Roman and Medieval periods (Van der Veen, Livarda & Hill 2008; Van der Veen, Hill & Livarda 2013)  However, the database could only be centrally updated, with the online resource remaining a static version, lacking much of the new data produced subsequent to the implementation of PPG16 in 1990  The ADEMNES database, created through a research project undertaken at the Universities of Freiburg and Tübingen, contains data from 533 eastern Mediterranean and Near Eastern sites (Riehl & Kümmel 2005)  Kroll has maintained the Archaeobotanical Literature Database to accompany the Vegetation History and Archaeobotany articles (Kroll 2005) now accessible as a database (Kirleis & Schmültz 2018)  Numerous other databases have collated archaeobotanical studies, including the COMPAG project (Fuller et al  2015), the Cultural Evolution of Neolithic Europe project (Colledge 2016), RADAR in the Netherlands (van Haaster and Brinkkemper 1995), BRAIN Botanical Records of Archaeobotany Italian Network (Mercuri et al  2015) and CZAD – Archaeobotanical database of Czech Republic (CZAD 2019) 
The majority of databases have a restricted regional coverage, whilst research-project driven period-specific databases provide overlapping content  Whilst there are a wide range of archaeobotanical databases available, few contain primary datasets (other than the ABCD) which can be downloaded as  csv files  Data which is most commonly available are bibliographic references per site, with some indications of mode of preservation, quantity of archaeobotanical data, and sometimes taxa present  The databases do not inter-relate to each other, and function primarily as bibliographic sources enabling researchers to find comparative sites or to identify published datasets which need to be re-tabulated prior to meta-analysis  The IWGP website curates a list of resources, but otherwise the resources are often disseminated through the archaeobotany jiscmail list 
Beyond the aim of cataloguing archaeobotanical data within a region and period, meta-analysis is often used in archaeobotany to identify spatial and chronological trends in a range of past human activities, for instance crop choice, crop husbandry practices, plant food consumption, the trade in luxury foods or the use of plants in ritual  Meta-analysis can be undertaken on the basis of simple presence/absence data per site, but in order for such analysis to be rigorous and comparable, sample-level data must be utilised  For instance, sample-level data is required for meta-studies, in order to identify high-quality samples of unmixed crops for weed ecology analysis (Bogaard 2004), to assess the importance of context in the evaluation of wild plant foods (Wallace et al  2019), or to use volumetric measurements as a proxy for scale (Lodwick 2017b)  The reuse of archaeobotanical data also extends to include datasets used as “controls” in commonly used forms of statistical analysis, for instance Jones’s weed data from Amorgos, Greece, which is utilised as a control group in discriminant analysis of crop-processing stage (Jones 1984), and ethnographic observations of crop items in different crop-processing stages (Jones 1990) 
2 3  Open data principles and solutions
Debates over issues of data publication and meta-analysis have been on-going across scientific disciplines over the last decade (Editors 2009), and have been summarised within principles of open science, as recently set out in relation to archaeology (Marwick et al  2017)  Open Data is one of the three core principles for promoting transparency in social science (Miguel et al  2014)  The FAIR principles, developed by representatives from academia, industry, funding agencies, industry and publishers, provide four principles which data sharing should meet for use by both humans and machines – Findability, Accessibility, Interoperability, and Reusability (Wilkinson et al  2016)  A recent report assessing the adoption and impact of FAIR principles across academia in the UK included archaeology as a case study (Allen and Hartland 2018: 46)  It reported how the ADS was often used to archive data, but that “The journal itself provides the “story” about the data, the layer that describes what the data is, how it was collected and what the author thinks it means ” The report also raises the problem that smaller projects may not have the funding to utilise the ADS, meaning that other repositories are utilised  Increasingly, archaeological data is made available through a wide range of data repositories (OSF, Mendeley Data, Zenodo, Open Context), university data repositories (e g  ORA-Data), or social networking sites for academics (Academia edu, ResearchGate)  More widely in archaeology, some have observed that archaeological data is rarely published (Kintigh et al  2014), and recent reviews have reported low levels of data sharing (Huggett 2018; Marwick & Pilaar Birch 2018)  A closely related issue is that of data reuse  Responsible reuse of primary data encourages the sharing of primary data (Atici et al  2013), but levels of data reuse in archaeology are thought to remain low (Huggett 2018)  Principles for responsible data citation in archaeology have recently been developed summarising how datasets should be cited (Marwick & Pilaar Birch 2018) 
In order to assess the current status of data sharing, citation and data re-use in archaeobotany, a review was undertaken of the publication of primary data and the publication of meta-analysis in major archaeological journals over the last ten years, building on recent pilot studies within archaeology (Marwick & Pilaar Birch 2018)  The review of academic journals provided a contrast to recent assessments of archaeobotanical data deriving from developer-funded archaeology (Lodwick 2017c; Van der Veen, Hill & Livarda 2013)  Journal articles have been selected as the focus of this study as the provision of online supplementary materials in the majority of journals and the ability to insert hyperlinks to persistent identifiers (eg a DOI) to link to datasets available elsewhere should not limit the publication of data and references  Much archaeobotanical data is also published elsewhere, especially from projects not based in the university sector, that is commercial or community archaeology in the UK  Archaeobotanical datasets emanating from this research are more commonly published through monographs, county journal articles, and unpublished (or grey literature) reports, but these are beyond the scope of the current review 
All journal articles were included which represent the principle reporting of a new archaeobotanical assemblage  The selected journals fall within three groups  First, what is considered the specialist archaeobotanical journal (Vegetation History and Archaeobotany (VHA))  Second, archaeological science journals (Archaeological and Anthropological Sciences, Environmental Archaeology, The Holocene, Journal of Archaeological Science (JAS), Journal of Archaeological Science: Reports (JASR), Journal of Ethnobiology, Quaternary International, Journal of Wetland Archaeology), which can be considered as specialist sub-disciplinary journals which should be maintaining data-quality  Third, general archaeology journals (Antiquity, Journal of Field Archaeology, Oxford Journal of Archaeology, Journal of Anthropological Archaeology, Journal of World Prehistory)  Finally, the broader cross-disciplinary journals PLoS One and Proceedings of the National Academy of Sciences (PNAS) were included  Published articles from the past ten years (2009–2018) have been analysed in order to assess the availability of plant macrofossil data  This ten-year period brackets the period where most archaeological journals have moved online and adopted supplementary materials 
Data citation in synthetic studies has been assessed in the same range of publications  The extent of data reuse ranges from the analysis of whole sample data to the presence/absence of individual crops  The location of a data citation has been assessed in the same range of publications, with the addition of journals where occasional research incorporating archaeobotanical data is featured (Britannia, Journal of Archaeological Research, Ethnobiology Letters, Medieval Archaeology, Proceedings of the Prehistoric Society, World Archaeology)  The underlying dataset for the analysis is available in Lodwick 2019 
4 1  Primary data sharing
Here, the location of primary archaeobotanical data, that is sample level counts of macroscopic plant remains, was assessed for 239 journal articles across 16 journals (Lodwick 2019 Table 1)  Figure 1 shows the results grouped by journal  Overall, only 56% of articles shared their primary data  In, Antiquity, JAS, JASR, PLOS One, Quaternary International and VHA, the highest proportion of publications did not include their primary data, that is to say that the sample-by-sample counts of plant macrofossils was not available  This level of data is comparable to the findings of other pilot studies in archaeology  Marwick and Pilaar Birch found a data sharing rate of 53% from 48 articles published in Journal of Archaeological Science in Feb – May 2017 (Marwick & Pilaar Birch 2018: 7), and confirm previous assertions that data is often withheld in archaeology (Kansa 2012: 499)  This is better than some disciplines, with a 9% data sharing rate on publication found across high impact journal science publications (n = 500) (Alsheikh-Ali et al  2011) and 13% in biology, chemistry, mathematics and physics (n = 4370) (Womack 2015), yet still indicates that nearly half of articles did not include primary data  Primary archaeobotanical data is more likely to be shared in archaeobotanical and archaeological science journals than general archaeology journals  However, within the primary archaeobotanical journal, VHA, 51% of articles do not include their primary data (Figure 1) 
Chart showing the location of primary archaeobotanical data by journal in primary archaeobotanical data publications 
Where primary data was not shared, the data which was available ranged from summary statistics, typically counts or frequencies, reported either by site, site phase, or feature group  Figure 2 summarises these results by year, showing that there is a gradient within articles not sharing their full ‘raw’ data, from those only provided sample counts on one aspect of the archaeobotanical assemblage, to those only presenting data graphically or within discussion  Beyond full data, the most common form of data shared is either summary counts per site or summary counts per feature or phase  Whilst this data does enable some level of reuse, the results of any sample-level data analysis presented within an article cannot be verified, and the data cannot be reused for crop-processing or weed ecology analysis which requires sample level data  Furthermore, such data would have been collected on a sample-by-sample basis, but this information is lost from the resulting publication 
Chart showing the form of archaeobotanical data shared by year in primary archaeobotanical data publications 
The forms in which data are made available vary across journals  The sharing of primary data within an article remains the most common data sharing form in archaeobotany (Figure 1)  Data tables in text require manual handling to extract data, in journals such as VHA, whilst in other journals in-text tables can be downloaded as  csv files  These however would not be citable as a separate dataset  Supplementary datasets are the third most common form of data sharing  Indeed, the use of electronic supplementary material has been advocated recently for by some journals, such as the Journal of Archaeological Science (Torrence, Martinón-Torres & Rehren 2015)  Microsoft Excel spreadsheets are the most common form of supplementary data, followed by  pdfs and then word documents (Figure 1)  Both  xlsx and  docx are proprietary file formats, and not recommended for long term archiving or open science principles  There is no indication of improvement over the last decade in the form of data sharing  In 2018, 50% of articles did not share their primary data, and where the data was shared, it was in proprietary forms ( docx,  xlsx) or those that do not easily facilitate data reuse ( pdf) (Figure 3) 
Chart showing the location of archaeobotanical data from 2009–2018 in primary archaeobotanical data publications 
Just one of the articles included in this review incorporated a dataset archived in a repository (Farahani 2018), in contrast to the substantial growth in data repositories across academic disciplines (Marcial & Hemminger 2010)  Other examples provide the underlying data for monograph publications, such as that of the archaeobotanical data from Gordion, Turkey (Marston 2017a, 2017b), Silchester, UK (Lodwick 2018; University of Reading 2018) and Vaihingen, Germany (Bogaard 2011a; Bogaard, 2011b) 
Several of the journals that have been assessed have research data policies  In the case of Vegetation History and Archaeobotany, sufficient papers have been surveyed to assess the impact of the research data policy on the availability of data  Figure 4 show the proportion of data sharing formats through time just for VHA (note the small sample size)  The introduction of a research data policy in 2016 encouraging data sharing in repositories has not resulted in any datasets being shared in that format  Of the 10 articles published in PLOS One after the introduction of a clear research data policy in 2014, 4 did not contain primary data  However, elsewhere, journals with no research data policy, such as Antiquity, has one of the lower levels of data sharing (Figure 1) 
Chart showing the location of primary archaeobotanical data in Vegetation History and Archaeobotany 
There are various reasons for why a primary dataset may be lacking  The option of providing supplementary datasets has been available in many of the journals here since before the start of the surveyed period (e g  Vegetation History and Archaeobotany in 2004), and so cannot be a reason for the absence of data publication in this journal while it may be a reason in other journals  Reasons suggested for a lack of data sharing within archaeology include technological limitations, and resistance amongst some archaeologists to making their data available due to cautions of exposing data to scrutiny, lost opportunities of analysis before others use it and loss of ‘capital’ of data (Moore & Richards 2015: 34–35)  Furthermore, control over how data tables is presented (taxa ordering, summary data presented) may also contribute to the preferential publishing of data within journal articles  Another factor to consider is the emphasis on the creation of new data through archaeological research (Huvila 2016)  The creation of a new archaeobotanical dataset through primary analysis is a key form of training in archaeobotany, and the perception of the value of the reuse of other previously published archaeobotanical journals may be low, hence not encouraging the sharing of well-documented datasets  Excellent exams of data reuse have resulted in influential studies (Bogaard 2004; Riehl 2008; Wallace et al  2019), and would hopefully encourage further data sharing in the future 
Given that there are numerous examples of meta-analysis which do take place in archaeobotany, it seems likely that the prevalent form of data sharing is through informal data sharing between individual specialists  However, this does not improve access to data in the long term, and is inefficient and time consuming, with large potential for data errors (Kansa & Kansa 2013), and relies on personal networks, which are likely to exclude some researchers  The absence of primary data in many archaeobotanical publications thus inhibits the verification of patterns observed within a dataset, and strongly limits the re-use potential of a dataset 
4 2  Data citation
One of the common arguments for increasing data sharing is an associated increase in the citation of the articles which have data available  Here, the data citation practices of meta-analyses of plant macrofossil data undertaken over the last decade have been reviewed  20 journals were consulted, including a wider range of period-specific journals, and 107 articles were assessed (Lodwick 2019 Table 2)  Data citation was assessed as ‘in text’ or ‘in table’ to refer to when the citation and the bibliographic reference were within the article, as ‘in supplementary data’ when the citation and reference were within the supplementary materials, and as ‘no citation’ when no citation and reference was provided 
21% of articles (n = 22) did not contain any citations to the underlying studies  16% (n = 17) contained citations within supplementary data files  50% of articles (n = 53) contained a citation within a table within the main article, and 14% (n = 15) contained citations within the main text  For the 21% of articles without data citations, the results of these studies could not be reproduced without consulting individual authors  The papers supplying the underlying data also received no credit for producing these datasets  Where articles contain citations within the main article (in text or table), full credit is provided to the underlying studies, a citation link is created through systems such as google scholar, and the study can be easily built upon in the future  Where the citation is provided within supplementary data, the original studies do receive attribution, but are not linked to so easily 
Through time, there is a steady decrease in the proportion of studies without citations to the underlying data, whereby of the 17 meta-analysis articles published in 2018, only one had no data citations  In comparison, in 2009, 3 out of 8 meta-analysis articles contained no data citation (Figure 6)  Overall this is a more positive outlook on the reuse of published data, but the consistent presence of articles lacking data citation indicates that improvements are needed  Reasons for a lack of data citation may include restrictions on word counts imposed by journals, a lack of technical knowledge in making large databases available, or the wish to hold on to a dataset to optimise usage  Considering the type of journal (Figure 5), levels of data citation are worse in general archaeology journals, with sub-disciplinary journals showing slightly better levels of data citation  In particular VHA has a lack of consistency in where data citations are located 
Chart showing the location of data citations in meta-analysis journal articles by journal type 
Chart showing the location of data citations in meta-analysis journal articles from 2009–2018 
4 3  Reuse of archived archaeobotanical datasets
The majority of data citations assessed in the previous section are to articles or book chapters rather than data-sets  The ADS currently hosts 66 data archives which have been tagged as containing plant macro data, deriving mainly from developer-funded excavations but also some research excavations  However, in some of these the plant macro data is contained within a pdf  As, the archiving of archaeobotanical datasets in data repositories is still at an early stage, the reuse of these datasets is assessed here on a case-by-case basis  The archaeobotanical dataset from the Neolithic site of Vaihingen, Germany (Bogaard 2011b) has not been cited on google scholar  Metrics are provided through the ADS, showing this dataset has been downloaded 56 times with 477 individual visits (as of 25/2/19)  The archaeobotanical dataset from Gordion by Marston has no citations on Google Scholar (Marston 2017b), neither does the Giza botanical database (Malleson & Miracle 2018), but these are both very recently archived datasets  In contrast, the Roman Rural Settlement Project dataset, which includes site-level archaeobotanical data, has received greater levels of use, with 12 citations in Google Scholar, over 40,000 file downloads, and over 35,000 visits (Allen et al  2018) and the archaeobotanical computer database (Tomlinson & Hall 1996) has been cited 44 times, and is the major dataset underpinning other highly-cited studies (Van der Veen, Livarda & Hill 2008; Van der Veen, Hill & Livarda 2013)  Whilst there is clearly precedence for the reuse of archaeobotanical databases, current data citation practices within archaeobotany do not yet appear to be formally citing individual datasets, meaning an assessment of the reuse of archived archaeobotanical datasets is challenging 
5  Steps Forward
This review of data sharing, citation, and reuse practices in archaeobotany has found medium levels of data sharing, good levels of data citation, but so far limited levels of reuse of archived data sets  This picture is similar across archaeology, in part attributed to the status of archaeology as a small-science, where data-sharing takes place ad-hoc (Marwick & Pilaar Birch 2018)  Here, recommendations are discussed for improving these data practices within archaeobotany, of applicability more widely in archaeology 
Clearly an important step is improving the sharing of plant macrofossil data  Given the reasonable small size of most archaeobotanical datasets (a  csv file < 1mb), and a lack of ethical conflicts, there seems to be few reasons why the majority of archaeobotanical data couldn’t be shared  In the case of developer-funded derived data, issues of commercial confidentiality could limit the sharing of data  A key stage is establishing why levels of data sharing are not higher  Issues within archaeobotany may include the conflict between having to publish results within excavation monographs, which may take some time to be published, and have limited visibility due to high purchase costs and no digital access, and the need to publish journal articles for career progression within academia  The production of an archaeobotanical dataset is very time-consuming, and interim publication on notable aspects of an assemblage may be considered as a necessary publication strategy  More broadly, one important aspect is issues of equity in access to digital archiving resources (Wright & Richards 2018), such as differential access to funds, training and knowledge  A recent study in Sweden found that we need to know concerns, needs, and wishes of archaeologists in order to improve preservation of archaeological data (Huvila 2016), especially when control of ones data may be linked to perceptions of job security  In order to make improvements in data sharing and reuse across archaeology, we need improved training in data sharing and the reuse of data in higher education (Touchon & McCoy 2016; Cook et al  2018), improved training in data management (Faniel et al  2018), and crucially, the necessary software skills to make the reuse of archived datasets attainable (Kansa & Kansa 2014: 91)  Examples of good practice in archaeobotany are the Vaihingen and Gordion datasets which demonstrate how datasets can be archived in data repositories to accompany a monograph (Bogaard 2011b; Marston 2017b), whilst Farahani (2018) provides an excellent example of a journal article, where the primary data is supplied as a  csv in a cited data repository along with the R script for the analysis 
In tandem with the need to encourage authors to share their data, is the need for journals to create and implement research data policies  Given the existence of research data policies in many of the journals included here, this reflects other findings of the poor enforcement of data policies by journals (Marwick & Pilaar Birch 2018), supporting arguments that journals should not be relied upon to make data accessible, and data should instead by deposited in digital repositries  In order to implement change in data sharing, there is a role to play for learned societies and academic organisation in lobbying funding bodies, prioritising data sharing in research projects  A key step is through journal editorial boards, and the enforcement of any pre-existing research data policies (Nosek et al  2015)  Revi



ground_truth: Technological limitations, resistance to exposing data to scrutiny, and desire to hold onto data for personal use.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What happens to Ngotho after he attacks Jacobo at a workers' strike?

Read the following text and answer briefly.

Context 3: Weep Not, Child is a 1964 novel by Kenyan author Ngũgĩ wa Thiong'o  It was his first novel, published in 1964 under the name James Ngugi  It was among the African Writers Series  It was the first English language|English novel to be published by an East African  Thiong'o's works deal with the relationship between Africans and white settlers in colonial Kenya, and are heavily critical of colonial rule  Specifically, Weep Not, Child deals with the Mau Mau Uprising, and "the bewildering dispossession of an entire people from their ancestral land " Ngũgĩ wrote the novel while he was a student at Makerere University 

The book is divided into two parts and eighteen chapters  Part one deals mostly with the education of Njoroge, while part two deals with the rising Mau Mau movement 

Plot summary

Njoroge, a little boy, is urged to attend school by his mother  He is the first one of his family able to go to school  His family lives on the land of Jacobo, an African made rich by his dealings with white settlers, namely Mr  Howlands, the most powerful land owner in the area  Njoroge's brother Kamau works as an apprentice to a carpenter, while Boro, the eldest living son, is troubled by his experiences while in forced service during World War II, including witnessing the death of his elder brother  Ngotho, Njoroge's father and a respected man in the surrounding area, tends Mr  Howlands' crops, but is motivated by his passion to preserve his ancestral land, rather than for any compensation or loyalty 

One day, black workers call for a strike to obtain higher wages  Ngotho is ambivalent about participating in the strike because he fears he will lose his job  However, he decides to go to the gathering, even though his two wives do not agree  At the demonstration, there are calls for higher wages  Suddenly, the white police inspector brings Jacobo to the gathering to pacify the native people  Jacobo tries to put an end to the strike  Ngotho attacks Jacobo, and the result is a riot where two people are killed  Jacobo survives and swears revenge  Ngotho loses his job and Njoroge’s family is forced to move  Njoroge’s brothers fund his education and seem to lose respect for their father 

Mwihaki, Jacobo's daughter and Njoroge's best friend, enters a girls' only boarding school, leaving Njoroge relatively alone  He reflects upon her leaving, and realizes that he was embarrassed by his father's actions towards Jacobo  For this reason, Njoroge is not upset by her exit and their separation  Njoroge switches to another school 

For a time, everyone's attention is focused on the upcoming trial of Jomo Kenyatta – a revered leader of the movement  Many blacks think that he is going to bring forth Kenya’s independence  But Jomo loses the trial and is imprisoned  This results in further protests and greater suppression of the black population 

Jacobo and a white landowner, Mr  Howlands, fight against the rising activities of the Mau Mau, an organization striving for Kenyan economic, political, and cultural independence  Jacobo accuses Ngotho of being the leader of the Mau Mau and tries to imprison the whole family  Meanwhile, the situation in the country is deteriorating  Six black men are taken out of their houses and executed in the woods 

One day Njoroge meets Mwihaki again, who has returned from boarding school  Although Njoroge had planned to avoid her due to the conflict between their fathers, their friendship is unaffected  Njoroge passes an important exam that allows him to advance to High School  His village is proud of him, and collects money to pay Njoroge's High School tuition 

Several months later, Jacobo is murdered in his office by a member of the Mau Mau  Mr  Howlands has Njoroge removed from school for questioning  Both father and son are brutally beaten before release and Ngotho is left barely alive  Although there doesn't seem to be a connection between Njoroge's family and the murder, it is eventually revealed that Njoroge's brothers are behind the assassination, and that Boro is the real leader of the Mau Mau  Ngotho soon dies from his injuries and Njoroge finds out that his father was protecting his brothers  Kamau has been imprisoned for life  Only Njoroge and his two mothers remain free, and Njoroge is left as the sole provider of his two mothers  Njoroge fears that he cannot make ends meet; he gives up hope of continuing in school and loses faith in God 

Njoroge asks Mwihaki's for support, but she is angry because of her father’s death  When he finally pledges his love to her, she refuses to leave with him, realizing her obligation to Kenya and her mother  Njoroge decides to leave town and makes an attempt at suicide; however, he fails when his mothers find him before he is able to hang himself  The novel closes with Njoroge feeling hopeless, and ashamed of cowardice 

Characters in Weep Not, Child
 Njoroge: the main character of the book whose main goal throughout the book is to become as educated as possible 
 Ngotho: Njoroge's father  He works for Mr Howlands and is respected by him until he attacks Jacobo at a workers strike  He is fired and the family is forced to move to another section of the country  Over the course of the book his position as the central power of the family weakened, to the point where his self-realization that he has spent his whole life waiting for the prophecy (that proclaims the blacks will be returned their land) to come true rather than fighting for Kenyan independence, leads to his depression 
 Nyokabi and Njeri: the two wives of Ngotho  Njeri is Ngotho's first wife, and mother of Boro, Kamau, and Kori  Nyokabi is his second wife, and the mother of Njoroge and Mwangi 
 Njoroge has four brothers: Boro, Kamau, Kori and Mwangi (who is Njoroge's only full brother, who died in World War II) 
 Boro: Son of Njeri who fights for the Allies in World War II  Upon returning his anger against the colonial government is compounded by their confiscation of the his land  Boro's anger and position as eldest son leads him to question and ridicule Ngotho, which eventually defeats their father's will (upon realizing his life was wasted waiting and not acting)  It is eventually revealed that Boro is the leader of the Mau Mau (earlier alluded to as "entering politics") and murders Mr Howlands  He is caught by police immediately after and is scheduled to be executed by the book's end  It is highly likely that it is also Boro who kills Jacobo 
 Mwihaki: Njoroge's best friend (and later develops into his love interest)  Daughter of Jacobo  When it is revealed that his family killed Jacobo (most likely Boro), Mwihaki distances herself from Njoroge, asking for time to mourn her father and care for her mother 
 Jacobo: Mwihaki's father and an important landowner  Chief of the village 
 Mr  Howlands: A white settler who emigrated to colonial Kenya and now owns a farm made up of land that originally belonged to Ngotho's ancestors  Has three children: Peter who died in World War II before the book's beginning, a daughter who becomes a missionary, and Stephen who met Njoroge while the two were in high school 

Themes and motifs
Weep Not, Child integrates Gikuyu mythology and the ideology of nationalism that serves as catalyst for much of the novel's action  The novel explores the negative aspects of colonial rule over Kenya  Njoroge's aspiration to attend university is frustrated by both the violence of the Mau Mau rebels and the violent response of the colonial government  This disappointment leads to his alienation from his family and ultimately his suicide attempt 

The novel also ponders the role of saviours and salvation  The author notes in his The River Between: "Salvation shall come from the hills  From the blood that flows in me, I say from the same tree, a son shall rise  And his duty shall be to lead and save the people " Jomo Kenyatta, the first prime minister of Kenya, is immortalised in Weep Not, Child  The author says, "Jomo had been his (Ngotho's) hope  Ngotho had come to think that it was Jomo who would drive away the white man  To him, Jomo stood for custom and traditions purified by grace of learning and much travel " Njoroge comes to view Jomo as a messiah who will win the struggle against the colonial government 

See also

Things Fall Apart
Death and the King's Horseman

References

External links
Official homepage of Ngũgĩ wa Thiong'o
BBC profile of Ngũgĩ wa Thiong'o
Weep Not, Child at Google Books

British Empire in fiction
Novels set in colonial Africa
Historical novels
Kenyan English-language novels
Novels by Ngũgĩ wa Thiong'o
Novels set in Kenya
1964 novels
Heinemann (publisher) books
Postcolonial novels
African Writers Series
1964 debut novels



ground_truth: After attacking Jacobo at a workers' strike, Ngotho loses his job and Njoroge's family is forced to move.