dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments Analyses performed during this work were supported by research grants to  Melis Uzdurum  from the  American Research Institute in Turkey (ARIT)  and  Koç University Research Center for Anatolian Civilizations (RCAC) ,  National Science Foundation Grants  ( BCS-09121418  and  BCS-1354138 ) to  Mary C. Stiner , a  German Academic Exchange Service (DAAD)  research grant to  Susan M. Mentzer ,  Ludwig Maximilian   University- Istanbul University  Funding to  Joris Peters  (6647), and the  ENVIMED, ARCHEOMED and GOWEST Programmes of the INEE Department of the CNRS in Paris  as well as the  ARTEMIS Programme  of the  LSCE Laboratory in Orsay  to  Catherine Kuzucuoğlu  (LGP). The authors would like to thank  Andrew Cohen  and  Georgia Tsartsidou  for diatom and phytolith observations,  Mustafa N.
The first attempts to identify amorphous organic substances in archaeology date to the end of the 19th century and the beginning of the 20th century. The 1960s saw the development of infrared spectrometry, and then separative and mass spectrometry analyses were implemented in the 1980s. But it is only since the 1990s that extended and systematic research programmes were devoted to these substances. The number of publications has not stopped growing and is becoming exponential. To get an overview of the lipid studies in archaeology, we conceived the ARchaeological Organic residues Literature Database (AROLD) as a first structured and collaborative research tool. This paper describes the challenges of setting up such a database, details its architecture, presents the choices involved in its implementation, and discusses the possibilities of sharing and evolving this tool.
, 2022; Lucquin et al. , 2016; Rageot et al. , 2021; Roffet-Salque et al. , 2015) , but still, they require prompt updating. Furthermore, the preparation and analysis protocols differ according to the research teams, several papers deal with multiple sets of samples from the same site, and contextual information is sometimes partially provided, especially in synthetic review articles. To overcome the current difficulty of dealing with this large and growing literature, we have set up the AROLDatabase.
Why a bibliographic database on organic residues in archaeology? The challenges of the AROLDatabase The project fulfils a triple objective. First, to survey the literature on the characterization of archaeological lipids and record published data in a standardized format. Second, to provide the scientific community with a data inventory and query tool. Third, to assist in the production of synthesis documents including distribution maps of the materials investigated, the chronology of published data, the type of artefacts studied (e. g. , arrowheads, bell-shaped vases, cooking pot, etc. ), protocols of sample treatment or analytical techniques implemented. Holding these thoughts, we have compiled, synthesized and built a comparative bibliography template that can be used as a tool for single and/or multiple search criteria (e.
From published data to a standardized database Synthesizing data on lipid analysis in archaeological artefacts has proved to be a challenge for two main reasons. First, while some articles were published on a one-site, one-period basis, many articles compile data on several archaeological sites and/or several periods or cultural contexts. In addition, some of the papers compile previously published data with new data. Data relating to the same site and chronological period, or cultural phase, are sometimes scattered throughout several papers. Bearing this in mind, we have chosen to use each paper as a cornerstone to design the architecture of AROLD. From one 'publication' to another, the number of samples analysed can vary from one to several hundreds. As it is not always possible to fill in the database on a sample-by-sample basis, we chose the 'sample lot' model.
By structuring AROLD, we defined and ranked the essential criteria to be recorded which were converted into fields to be input as numbers, texts or lists: contex-tual archaeological data (site, period, culture, conservation context, etc. ), types of objects studied, analytical procedures carried out and identified substances. To facilitate the comparison of text fields from one article to another, we produced pre-edited drop-down lists representative of the data available in each article. Semantic choices for the main parameters are detailed in Data S1 in the additional supporting information.
Current and ongoing content of the AROLDatabase The database is primarily fed from peer-reviewed articles and academic output, including supplementary materials and related papers, to get more detailed information on the archaeological sites under consideration (especially concerning dating, location, environment or culture). Among the ORA literature, we chose to focus on lipids. To gain an overview of food commodities and beverages, we also included data on fermented beverages, particularly short-chain acids involved in wine and beer and alkaloids present in cocoa on the American continent. At this stage of the database input, we have not included proteins, sugars and anything related to colouring matters. However, the database is designed to be expanded to include these types of substances at a later stage.
Articles relating to ancient DNA are not referenced either. Another issue to be addressed was the quality of the data published, the robustness of the results obtained and the validity of the interpretations. Even though the database is mainly based on peer-reviewed articles, the expansion of ORA research is occasionally accompanied by a lack of expertise in the methods used or the interpretation of the data as recently discussed by  Whelton et al. (2021) , although guidelines are now accessible  (Dunne et al. , 2017; Roberts et al. , 2018). As the aim of this database is to compile and share published scientific data, we have chosen to start by using all published data in the first instance, arguing that it is crucial for any user of the database to critically read the articles they intend to cite afterwards.
In a forthcoming step, the database could be updated with fields opened for scientific debate between the contributors to the database and the authors of the articles. In this initial implementation phase, only a few scholars contributed to the database, via an interface hosted by Filemaker Server (Figure  2 ). This interface dedicated to contributors is accessible through a standard web browser protected by individualized access codes in such a way that a personalized monitoring of modifications can be carried out (Figure  2 ). This workspace is composed of five tabs: 'Home' presents the database, the user information and the latest modifications recorded; 'Literature', 'Site ID' and 'Sample lots', are the pillars of the database; and 'Data display' allows the visualization and representation of data by means of graphs, time scales and distribution maps.
These features are based on the open-source JavaScript libraries leaflet (v1. 7. 1 2 ) for mobile-friendly interactive maps and high charts to create responsive and interactive charts. The AROLDatabase makes possible the import of bibliographic references from Zotero or Mendeley. Each contributor to the database can thus enter the access key to at least one personal library on Zotero 3 and one on Mendeley 4 in the 'Home' tab. The current dataset used in this database is available in a French national data repository (Nakala) and will be regularly updated with new data. 5  As some publications do not provide enough detail to be properly comparable with publications supplying supplementary information, we wish to make AROLD a participatory and common tool, where scholars can consult data from their own work and contribute information that has not been detailed in the published papers.
Data consultation Two ways of browsing the database have been set up: • One open-access version including published and academic data is available. 6 Querying the dataset is performed through a search engine and drop-down lists. This webpage also generates a json+ld tag whose json structure is established according to the dataset model, established by schema. org 7 (Figure  3 ). All documents created using the public interface must be copyrighted as detailed on the web page and according to the data set. 8 • Another working interface is available (with a personal password restricted access) for colleagues who wish to consult, input, complete their data or further develop the database (Figure  2 ). Public sharing is made possible because it involves reviewing data already published elsewhere and referring to the original publication.
Case study in the AROLDatabase To highlight the opportunities opened by this new tool, a query for aquatic products identified in Neolithic ceramics was chosen as case study, as detailed below. At the time of writing, the database contains 1214 archaeological sites and 1570 sample lots (Figures  4 5 6 ). Among them, 59 literature references and 118 sample lots are related to Neolithic aquatic products. From this query a distribution map and list of sites with relative chronological information are produced by the database (Figure  4 ) (see Data S2 in the additional supporting information). Selected maps detail the results according to the type of aquatic substances (marine, marine mammals, freshwater and undetermined aquatic products; Figure  4 , 2) and products encountered into the same corpus (other animal fats, Figure  4 , 3; dairy products, Figure  4 , 4; beeswax or birch bark tar, Figure  4 , 5).
PERSPECTIVES: RECOMMENDED DATA AND METADATA • Retrieving data to fill the database generally involved the tedious unravelling of articles, including the supplementary information. Standardizing the data for comparison with the existing literature occasionally requires interpreting the limited information provided. Some publications do not provide enough details to be properly compared with the most detailed papers: most of the time, the coordinates of the investigated sites are missing (sometimes no map is even provided), and the geological contexts and the post-excavation course of the objects are poorly commented (in particular for museum collections). comparison with the published literature meaningless, as different methods may have significantly differing extraction efficiency. • Some articles mention maximum, minimum, mean and/or median extraction yields, without specifying whether these data refer to all samples or only those considered interpretable.
• While the commodities identified in each sample are available in a table in some articles, this information is often only partially reported and/or left to the interpretation of the reader, for example, in the form of a graph representing the values of stable carbon isotopes in palmitic and stearic acids. • The study of the exploitation of natural resources over time is hampered by the absence of calibrated dates and/or chronological period in some papers. Depending on whether the query is made by chronological period or over a range of dates, the results obtained will be different, which is a limit to the use the database for a diachronic study (e. g. , to investigate the origins of the exploitation of a specific commodity in pottery). This scanning and analysis of the publications led us to identify and formalize the data and metadata necessary for an optimal comparison of the results published and for a good knowledge of the contexts of discovery (type of site, stratigraphic and chronological control, context of preservation, etc.
CONCLUSIONS: DATA SHARING AND ACCESSIBILITY The AROLDatabase provides an overview of the published literature in lipid archaeology concerning a period, a region, a commodity or combination of these criteria, through several options for interacting and searching: • Use the open access for scholarly query. 9 • Participate as contributors by adding and modifying data via a restricted access work interface. • Be involved as future database structure developers. Presently, AROLD, can be used as a bibliographic synthesis template to review the state of the art when starting a project or an article. Despite the great heterogeneity of the data provided in the literature, the analytical information could be standardized and data on the archaeological context could be systematically collected and documented when available in the articles (location of the site, type of site, chronology, context of preservation).
A C K N O W L E D G E M E N T S We thank the pioneers who studied lipids in archaeology and all their collaborators and successors who built this research framework in which preliminary synthesis and large-scale reflection are now possible through all this publication work. We are grateful to the University Nice Côte d'Azur, the CNRS (Centre National de la Recherche Scientifique) and the SNF (Swiss National Science Foundation-with the SINERGIA project 'Foodways in Africa') for their support. Indeed, in the framework of this latter project, it was possible to feed the database particularly with published studies on Africa. We also sincerely thank the reviewers for their thorough proofreading of the article and exploration of the database and for their sound advice.
D ATA AVA I L A B I L I T Y S TAT E M E N T The data that support the findings of this study are openly available in nakala. fr. 2 Cf. https://leafletjs. com/. 3 For web api v3 documentation, see https://www. zotero. org/support/dev/web_api/v3/start/. 4 For api documentation, see https://dev. mendeley. com/. 5 For DublinCore metadata of this database (including this public and private access url), see https://nakala. fr/10. 34847/ nkl. 271a7pts/. 6 See https://www. cepam. cnrs. fr/datas/arold. php/. 7 See https://schema. org/Dataset/. 8 For the AROLDatabase, see https://doi. org/10. 34847/nkl. 271a7pts/. 9 See https://www. cepam. cnrs. fr/datas/arold. php/.
• Papers do not always specify the extraction method (e. g. , conventional solvent extraction or acid methanolysis) leading to the extraction yields mentioned in the text or tables, which makes theF I G U R E 3 Overview of the open access web page of the AROLDatabase. F I G U R E 3 (Continued) F I G U R E 4 Series of documents from the AROLDatabase on aquatic products: (1) bar chart indicating the date for each archaeological site reported in AROLD where aquatic commodities have been identified; (2) satellite map with the location of sites containing information on aquatic molecules from Neolithic sites reported into AROLD; (3) map with detailed indications of the type of aquatic products discussed in the papers; and (4-6) maps showing other products identified at sites where aquatic products have also been identified.
Camielsa Prévost https://orcid. org/0000-0002-8905-6072 Léa Drieu https://orcid. org/0000-0002-7324-4925 E N D N O T E S 1 Copyright terms for datasets and SQL structure are defined by Creative Commons at https://creativecommons. org/licenses/ by-sa/4. 0/deed. en (CC-By-SA 4. 0; C. Prévost, L. Drieu, A. Pasqualini and M. Regert).
Side-scan sonar A total of 268 linear kilometers of side scan was acquired to characterize the seafloor, bed rugosity, and geometrics patterns. The sonar used is a Cadden Klein 4900. It transmits and receives acoustic signals on two frequencies: 455 kHz and 900 kHz. The maximum range is 75 m per side at 900 kHz and 200 m per side at 455 kHz. The fish was towed 150 m behind the vessel at a speed of 3. 5-4 knots. The sidescan imagery obtained were processed using the DELPH Interpretation software of Ixblue. Only the lower frequency of the 35 profiles was processed by applying bottom track, an AGC (Automatic Gain Control) gain with an average level of 50 m and the filtering windows to 10 m and slant corrections. The navigation and wave corrections did not apply. The backscatter mosaic, presented here, come from ".
Dive and sampling Dive and sampling were selected based on the interpretation of the backscatter, which delineated the GARS in the study area. Sampling and diving were carried out between 80-and 120-m water depth. These data were enabled the observation of epiphytic megafauna from 12 reef exposure sites as well as the collection of biological and geological (reef rocks and peripheral sands samples). It is on the basis of the first images provided by underwater robot that a scientific mission was designed to first map and measure the extension of the Guyanese reefs with the help of sonar. Then for the first time, a group of divers equipped with GPS was called upon to directly access the reef at a water depth of 80-100 m. They film and sampled a largely unsuspected marine fauna from 12 reef exposures.
Acknowledgements The data discussed in the present study were collected during the Amazon Reef expedition organized in 2019 under the dual supervision of the  CRIOBE-CNRS Laboratory at the University of Perpignan and Greepeace  who chartered its oceanographic ship, the Esperanza. We deeply thank  Greenpeace , particularly the Esperanza crew and captain  François Chartier  and  Edina Ifticene , as well as the divers team and  Serge Plane (CRIOBE-CNRS)  for involving the  French Guiana University  in the expedition and for the perfect conditions in which the Amazone Reef acquisitions were carried out. Gary Fowler post-edited the English style and grammar. Lastly, we would like to thank  Isabelle Thinon  and one anonymous reviewer who provided many useful suggestions to help improve our manuscript.
Introduction Micro-LIBS (Laser-Induced Breakdown Spectroscopy) imaging is currently experiencing a strong and fast development. 2] [3]  In addition to its table-top instrumentation, LIBS imaging also exhibits strong analytical performances combining multi-element capabilities, no restrictions in the detection of light elements, detection limits in the range of ppm for most of the elements, microscopic-scale resolution and the capability to image a large sample surface (>10 cm 2 ) in few hours. 4, 5 7] [8] [9]  However, the large number of spectra (>million in some cases) contained in an imaging dataset and the spectral complexity make this task difficult, in particular when certain elements (i. e. , iron or titanium) are present. It becomes even a crucial issue when high throughput analysis at high speed is foreseen.
Indeed, such data processing generally requires time and a strong expertise in emission spectroscopy to ensure that spectral interferences or any other unexpected issues do not bias the results. This aspect represents an important limiting factor to disseminate the method outside the LIBS community. 1] [12] [13] [14]  The relevance of such methods was demonstrated in various applications, which may involve LIBS data containing more than one million spectra. We can mention, as examples, the use of Principal Component Analysis (PCA),  10, 11  classication and clustering methodologies,  12  as well as spectral unmixing methods. 13 More recently, the group of L. Duponchel proposed a new method called Interesting Features Finder (IFF), able to retrieve minor and trace elements present on a small number of pixels, independent of the variance they express in the spectral dataset.
14 lthough all these methodologies are highly powerful and allow an exhaustive exploration of the LIBS imaging dataset, they still require an important expertise in data manipulation and in tool implementation, especially when the dataset contains a large number of spectra. We propose here the evaluation of the use of an articial neural network (ANN) for the processing of LIBS imaging data. The main idea is to address the identication of various mineral phases present in a sample without any complex manipulation or prior observation of the data. 6] [17] [18]  Advantages of ANN are well known and include fast response time (possible implementation in real time), low sensitivity to noise, high robustness, and accurate prediction capabilities. The use of an ANN to automate micro-LIBS imaging processing can address most of the current issues faced in the processing, which include: a large dataset (>1 million spectra, single-shot spectrum which oen experiences noise, "mixed" spectra (spectral interferences between 2 or more elements)), and spectral variability due to laser shot-toshot uctuations and long period of analysis.
However, to the best of our knowledge, there is no work on the use of an ANN for processing LIBS imaging data in the literature. To evaluate the processing capabilities of the ANN we have chosen to study the case of archaeological mortars. Lime mortar is a complex mixture resulting from the hardening of lime, water, and aggregates. From an archaeological point of view, the study mortar is of strong importance since it has been widely used from the time of the Roman Empire until the Industrial Revolution and the recipes used differ according to the geographical area of preparation, crasman, or function. 19 esides, there is a strong need for accurate dating of such materials, generally done by carbon-14 ( 14 C) dating, but precise and reliable identication of the binder, possible secondary calcite, and aggregates, all present in a carbonate form, are essential prior to any dating.
In the perspective of robust and accurate identication of the Fig. 2  Overview of the samples used to build the reference spectra database (see ESI Table  1  † for more details). This journal is © The Royal Society of Chemistry 2023 J. Anal. At. Spectrom. binder for radiocarbon dating, it was important to provide a large and wide set of calcium carbonates (geogenic and some biogenic), but also other carbonaceous materials such as charcoal. We selected a total of 1353 reference spectra. For each of the raw samples presented in Fig. 2 , a LIBS imaging sequence was recorded, whose size varied from 300 × 20 pixels to 300 × 100 pixels depending on the sample sizes. Images obtained from mortar samples were larger and specic to each sample due to their size variability. The pixel (i. e. , spectrum) selection was done randomly on the phases identied by an archaeometrist (specialist in ancient materials), each one corresponding to a single-shot spectrum and having been precisely attributed to one of the 8 categories.
The general methodology of data manipulation is presented in Fig. 4 , which schematically describes the processing of 6 spectra associated with 6 different categories among the 8 (named from 1 to 6). A pre-treatment, consisting in a normalization, was rst applied to each spectrum. This step appeared necessary since all the data presented hereaer were obtained on a rather long time scale. The reference spectra were measured over a few days of experimentation but the imaging data of the three shown samples (M1, M2 and M3) were obtained over a little more than a year. We have evaluated two normalization approaches applied to each of the 3 spectrometers: a normalization by the total intensity of each spectrum and a standard normal variate (SNV) normalization. The results of such normalization will be discussed below.
ANN optimization As mentioned above, an important step of this work consisted in optimizing the different parameters of the network with the idea to get the best ANN performance. For that we have assessed the MSE (mean square error), giving the average squared difference between the estimated predictions and the reference values. The evaluation of the MSE was done both for the training and the validation datasets. The training MSE can only decrease with the multiplication of iterations, in contrast to the validation MSE that may even show an increase in the presence of overtraining (i. e. , the ANN gets too specialized on the exact training set details). These trends are illustrated in Fig. 5a  for an ANN trained with 1500 iterations. Here, we have considered as parameters to be optimized: the iteration number of the training algorithm, the type of normalization applied to the spectral data, and the neuron number in the hidden layer.