dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , temperature changes, avalanches, river flow growth, or volcano eruptions). However, the digitized collections have some implicit issues. They are often riddled with Optical Character Recognition (OCR) errors that hamper the performance of information retrieval systems. Therefore, handling OCR errors is one of the two significant problems for information retrieval from collections of historical documents. On the other hand, these sources' problems are related to historical language changes since digitized texts are written in the language of their origin. This paper proposes an extended description of the Latin American Climate Change Evolution platform called LACLICHEV  [37]. The objective of LACLICHEV is to provide an integrated platform to expose and study meteorological events described in historical newspapers that are possibly related to the history of climate change in Latin America.
Perspectives organize metadata that represent such an event is reported through empirical narratives that can appear in newspaper articles, as in the context of our work. The third contribution is the experimental use of LACLICHEV to build the history of climate change in Latin America from digital newspapers. To track meteorological events, we explored newspapers to search articles that could report such events, the conditions in which they happened, their duration, the places in which they occurred, and their impact in terms of an approximate number of casualties and the kind of damages, etc. As an experimental scenario, we chose the XVIII and XIX centuries, which define a golden age for newspapers in Latin American countries [13], namely, Mexico, Colombia, Ecuador, and Uruguay. Organisation of the paper The remainder of this paper is organized as follows.
Section 2 introduces the general architecture of LACLICHEV and its functions implemented by its main modules. Section 3 describes the knowledge model we propose for modelling meteorological events as described in empirical narratives written in natural language. Section 4 describes the general curation and exploration processes implemented by LACLICHEV to deal with the curation and exploration of historical newspaper articles potentially reporting on climatologic events. It also describes the use cases that we conducted to evaluate it. Section 5 studies approaches that promote datasets exploration for defining the type of analysis possible on top of them. Finally, 6 concludes the paper underlying the contribution and discusses future work.
LACLICHEV for curating and exploring historical newspapers articles Figure  2  shows the general architecture of LACLICHEV organised into three layers: i frontend with an interface providing functions for curating articles and creating events descriptions; and giving access to explore the event history containing curated articles reporting meteorological events; ii backend with the meteorological event history stored in a document management system (see number 1 in Figure  2 ) and modules for curating (pre-processing and tagging the textual content of newspaper articles -number 2 in Figure  2 ) and exploring events (see number 3 in Figure  2 ); iii external layer connecting to document providers that are available through servers accessible on the Web and APIs exported, for example, by libraries.
Curating newspapers content modules The backend of LACLICHEV includes of a set of modules devoted to implement different operations of data curation (see number 2 in Figure  2 ). The objective of curating (historical) newspaper articles is to build a meteorological events history that newspaper articles reporting events with metadata, providing as much information as possible about the reported event. Figure  3  shows the newspapers curation process that is a semi-automatic process devoted to: find articles reporting this type of events within digital collections available in existing digital libraries repositories; geo-tag interactively and store those articles that actually report such events for building a meteorological event database. LACLICHEV relies on a knowledge graph that integrates a thesaurus classifying meteorological event types, Wordnet and a glossary defining meteorologic characteristics of meteorological events.
A term is a string with a parent, a node means a POS tag. According to the model, the ContentTree represents a document's content where the vocabulary used is determined by a Location in a country and a city. These classes represent that the same language, Spanish, varies among countries and cities. Recall that in different locations, people describe meteorological events using different vocabulary. Every article in a newspaper is associated with its content tree. A data analyst or expert domain can explore the articles by navigating their content trees without reading the full content. For example, retrieve articles reporting heavy storms in Uruguay in December 1810. Nodes are related through two relation types: instance, correlation. The relation of type correlation describes two terms that appear in the same sentence with a given distance given by the number of intermediate terms.
It includes newspaper collection from Ecuador and Argentina, namely "La Verdad Desnuda (Guayaquil, Ecuador) and "Vida Intelectual" (Santa Fe, Argentina). The current version of LACLICHEV processed around 19 million images in the newspapers of the fourth countries. The event history has curated 800 different meteorological events. We curated collections and generated the vocabulary used on articles identified as reporting a meteorological event (see Figure  8 ). Digital newspaper collections remain in the initial repositories that belong to the libraries. Then, terms and links to the OCR (Optical Character Recognition) archives containing documents with articles reporting meteorological events were stored in distributed histories managed in each country. As shown in Figure  8 , the process consists of five steps usually used in natural language processing techniques: sentence segmentation, tokenization, speech tagging, entity and relation detection.
LACLICHEV implements these phases in Python, relying on the NLTK library. The first phase of the pre-processing process of newspapers leads to graphs representing the content of the articles and classic inverse index and frequency matrices used for performing exploration queries. Besides curating the data collections' content, we wanted to discover linguistic variations in different Latin American countries to describe meteorological events. People's Fig. 8. Pre-processing text pipeline language and variations can picture civilians' perception of these events, consequences, and associated explanations. Thus, local vocabularies were created out of the terms used in newspapers' articles (see Figure  9 ). For example, referring to a storm as a stormtrooper  10 Then we updated and enriched through queries, exploration and analytic activities, these vocabularies through human-in-the-loop actions.
Curating data collections LACLICHEV proposes functions that data scientists may exploit through diverse functionalities. Next, we present the type of functions of LACLICHEV's API (application programming interface). The implementation of these functions were adapted to the case of historical newspaper collections: -Curating data collections by exploring and processing their content for building the history of meteorological events possibly related to climate change in the considered Latin American countries. The functions for processing texts in Spanish are the core of LACLICHEV. They were coupled with other functions to extract, derive and associate as much data as possible to articles describing meteorological events. Curation tasks were performed on a collection of textual digital documents with minimum associated metadata, particularly those used by digital libraries that own the collections.
LACLICHEV applies query rewriting techniques to extended user-expressed queries with synonyms, subsuming and general terms. The particular characteristic of this task is that the user (i. e. , data analyst) can interact and guide the process according to her/his knowledge and expectations about what she/he expects to explore and search. The first result of this process, based on a "queries as answers approach", is a set of queries that can potentially provide the largest number of results stemming from the collections of the different libraries. The details of the approach we proposed for LACLICHEV is detailed in the following section. -Analytics operations and analysis results are generally presented within maps (e. g. , how did rainy periods evolved in the region?). In the current version of LACLICHEV analytics queries cannot be expressed in the frontend.
They are implemented manually through notebooks running on top of the event history. The analytics queries concerning aggregative queries on the event's history, for example, the number of events happening in a country within a specific time window. The average wind speed and millimetres of water per hour deduced for events regarding rainfalls and hurricanes in Montevideo. Classifying the terms used for describing specific types of events. The event history is a curated and clean data collection on top of which other analytics models can be applied for discovering knowledge. This characteristics open analytics perspectives for future uses of LACLICHEV. For example, applying supervised learning for analysing newspapers articles and determining whether they describe meteorological events. This can allow to semi-automatise the curation process and enhance it with a recommendation system.
Our approach for rewriting queries is based on a "queries-as-answers" process. This technique rewrites user queries into queries that can produce more precise results according to the explored dataset content. Queries as answers proposed by LACLICHEV consist of a list of frequently used queries. Thus, we focus on the following aspects: Extending query alternatives using hypernyms and synonyms. An initial conjunctive or disjunctive query is rewritten by extending it with general and more specific terms, synonyms, etc. The terms used to express the query are colloquial vocabulary for denoting meteorological events. The rewriting process can be automatic or interactive, in which case the system proposes alternatives, and the user can validate the proposed terms. For example, if the query is "heavy storms", the query can be completed by adding "heavy stormtrooper", "heavy storm dust".
It can also be rewritten with synonyms for the adjective heavy. In that case, it creates a combinatorial set of rewritten queries. Note that the colloquial vocabulary stems from the articles of the curated newspapers. As they are curated, the terms used in the articles feed a vocabulary that is first organised in the frequency matrix produced when texts are processed as part of the curation process. Then we use Wordnet  11  to look for associated terms and synonyms that help address concepts used in different Spanish-speaking countries. We do not translate the query terms to other languages because our digital data collections contain Spanish newspapers. LACLICHEV allows equivalent terms searching to morph a query. For a new term, LACLICHEV generates a node with the operator and then connects the initial term with the equivalent terms in a disjunctive expression subtree.
Then in a second round, the system rewrites the query adding synonyms of the terms, as shown in the upper right side of the figure. Finally, the query is rewritten according to the rules stated in the glossary. LACLICHEV performs a ranking process for the rewritten queries according to the coverage of their potential answer. The queries with the biggest coverage (those that include the largest subset of events in the history database). The algorithms to estimate the coverage of a documents collection are proposed in  [35]. Q'ExT j.
Scope and limitations LACLICHEV is running its first version; we expect to enrich the number of digital newspapers digitalised in the libraries. These new items will imply a new curation process that will improve the event history in two directions. First, more articles will describe the already curated events; this will complete the information stored in the history. Second, with more events, we will further test and enhance the analytics queries that require to have a specific volume of data to generate representative maps and analyses about the meteorological events that happened in the past. In future versions, and with more curated events, LACLICHEV is willing to answer prediction queries like Could it have been possible to predict the evolution of climate behaviour from the data in XVIII and XIX centuries?.
This query requires collecting, curating, and preparing more newspaper articles and other complementary data. However, it concerns future work. Another limitation of the current LACLICHEV is that it does not provide the adapted mechanisms for exploring the linguistic aspects of the vocabulary. It gathers the terms and organises them in a mesh data structure. Still, it does not provide tools for curating the languages and allowing an analytics exploration of their use of meteorological across countries and time. processes are essential to extract knowledge that can be digitally analyzed and correlated.
Data exploration The emergence of the notion of data exploration provides different perspectives of the data and tools for helping data scientists choose and compound datasets adapted for target experiments  [23, 5]. The tools  [17]  include functions like "data grooming"  [27] , which denotes transforming raw data into analyzable data with various data structures. Other approaches  [24]  focus on transforming human-readable data into machine-readable data considering inconsistencies in data formatting given that they are produced under different conditions. The idea is to exhibit processes, digital spaces, and systems that host datasets and provide them with access to understand the conditions in which data are processed. Data grooming denotes transforming raw data into analysable data with various data structures.
Multi-scale queries propose to split a query into multiple queries executed on different database fragments and then perform a union of those queries. This allows scaling the query size as the user gets more confident in her query. Result set post-processing and query morphing go on the premise that the user probably does not need the exact answer to a query. Result set post-processing assumes an array of simple statistical information such as min, max, and mean to be more helpful, especially on massive data sets. Query morphing assumes queries can be wrongly formulated. Query morphing still focuses on answering the query given by the user but will also use a small portion of resources in searching data around the original query. Query morphing. Another trend regarding data exploration is to tackle the lack of knowledge a user may have on the dataset.
Query morphing and queries as an answer are rewriting techniques that compute alternative queries (e. g. adding terms) that can potentially better explore a dataset than an initial query. Approaches such as interactive query expansion (IQE)  [30, 8, 19]  have shown the importance of data consumers in the data exploration process. Users' intention helps navigate the unknown data, formulate queries and find the desired information. In most occurrences, user feedback acts as vital relevance criteria for the following query search iteration. The key challenge is identifying bad queries using statistical information or massive scientific databases and identifying interesting queries to return. Identifying bad queries can be done using a list of frequently used queries and returning them based on user feedback.
SVD/PCA  [15]  is probably the most known algorithm for exploring data sets. It is used to reduce high-dimensional data represented as a matrix. From a practical perspective, it searches for the combination of weighted attributes that expresses the most information, allowing data analysts to work with the more useful 2 or 3-dimensional graphs. From a geometric perspective, these techniques search for the vectors with the highest variance and then express the original matrix according to this new system of dimensions. Using Eigenvalues makes it possible to estimate the amount of information in each dimension  [12]. Visualization and summarization are essential to understand the data and maintain it. The field of visual analytics seeks to provide people with better and more effective ways to understand and analyze these large datasets while also enabling them to act upon their findings immediately  [22].
Visual analytics provides technology  [26, 28]  that combines human and electronic data processing strengths. Structured query languages and the graphical interface developed over the top are the standard procedure for accessing data in a database. Many tools exist to perform data visualization with web visualization tools such as D3. js or other tools such as Matlab  [20]  or R programming language  [16]. One of the most critical steps of these tools is to let the data analyst move from confirmatory data analysis (using charts and other visual representations to present results) to exploratory data analysis (interacting with the data/results). This has led to visual data exploration and visual data mining  [7].
The frequency of word tokens in documents impacts the document's weight calculated using a numerical statistic of term frequency-inverse document frequency (TF-IDF). The proposed approach uses the title, abstract, and keywords of the paper and the categories of topics to perform a classification process. The documents are classified and clustered into the primary categories based on the highest cosine similarity measure between category weight and documents' weights. The work proposed in  [3]  discusses the challenge of processing and analysing historical manuscripts. Authors investigate how deep learning models detect and recognise handwritten words in Spanish American notary records. For dealing with natural language (ancient Spanish), professional historians prepared a labelled dataset of 26,482 Spanish words employed in the experiments.
Discussion Any query and analysis must be based on a good understanding of the available data collections because the way they are combined and analyzed impacts the quality and accuracy of the results. Existing solutions are not delivered in integrated environments that data analysts can comfortably use to explore data collections. The technical effort is still necessary to combine several tools to explore and process datasets and go from raw independent data sets to knowledge, for example, on climate change. Therefore, our research aims to tailor a data exploration environment to help explore digital data collections using a human-inthe-loop approach. In existing solutions, data analysts cannot comfortably explore data collections and design analytics settings, particularly in cases where documents and questions combine scientific observations with empirical observations, like in the case of meteorological events described empirically in the past.
The current version of LACLICHEV did not explore the linguistic aspect, with original or more advanced methods studying texts and combining present and past observations to try to derive conclusions, for example, about climate change. A technical effort is still necessary to combine several tools to explore and process datasets and go from raw independent data sets to knowledge, understanding and prediction, for example, on climate change. Therefore, LACLICHEV aimed to tailor a data exploration environment that could help explore digital datasets using a human-in-the-loop approach. Regarding the qualitative assessment of LACLICHEV, we have not run user experience testing to collect feedback and user experience, and we might perform such testing in the future. For the time being, we focus on the analytics such as correlating different descriptions of the "same" event from articles in various newspapers, the location of meteorological events in old maps and their correlation with modern maps.
BP  [18]. At the time the present work was initiated, type I collagen sequences had not been generated for the majority of African wild bovids, with the only protein sequences in international databases being those of cattle (Bos taurus), zebu (Bos indicus), wild yak (Bos mutus), a hybrid of B. taurus x B. indicus, chiru (Pantholops hodgsonii until recently), sheep (O. aries) and goat (C. hircus). We thus report the first large-scale study of archaeological remains of putative caprines from eastern to southernmost Africa using biomolecular methods. First, we present an exploitable COL1 reference set for zooarchaeological research that is especially relevant to the diffusion of domestic caprines in Africa. We establish by high-throughput tandem mass spectrometry the COL1 sequences from nine species of wild bovids from Africa that might be confused with domesticated caprines in archaeological contexts.
Modern material Previously, very few species of Bovidae type I collagen were available in public repositories (such as the National Center for Biotechnology Information (NCBI) or UniProt, commonly used in proteomics). In 2021, Janzen et al. [39]  published COL1 references for a large number of African wild bovids. The royalsocietypublishing. org/journal/rsos R. Soc. Open Sci. 10: 231002 proposed sequences are either translated from genomic data or obtained from one individual analysed through liquid chromatography-tandem mass spectrometry (LC-MS/MS)  [39]. To avoid redundancy, species published in the Janzen et al. paper are not reported in the main text of this manuscript, although some overlap of species exists due to concurrent projects (for more details, see electronic supplementary material, table  S1 ).
Type 1 collagen sequences de novo reconstruction The raw data were converted to. mgf using the mass spectrometer manufacturer software, DataAnalysis (v. 4. 4, Bruker Daltonics). The sequences were reconstructed using a database-assisted tool using the Byonic workflow C57 (Byologic, Protein Metrics). The restricted database used for the search ). The referenced sequences can be found on either NCBI or UniProt public repositories and correspond to entire translated protein sequences, including signal peptide and tropocollagen. Then, in order to reconstruct only the secreted protein sequences, we excluded these parts of the sequence from further analyses. When referring to amino acid positions, we use the sheep reference protein sequences. The alpha 1 chain starts (first position in electronic supplementary material, information 2) at position 170 (Q) and ends at position 1229 (K) in the UniProt reference (W5P481).
Archaeological samples identification using type I collagen sequences Since Janzen et al. [39]  published COL1 sequences of some of the species we had initially included in our dataset, the 'overlapping' species sequences were excluded from this paper. We hereby only present data for nine species (compared with the 19 initially sampled for reference purposes): Ammodorcas clarkei, Eudorcas rufifrons, Gazella dorcas, Nanger dama, N. soemmeringi, Ammotragus lervia, Capra nubiana, Addax nasomaculatus and Pelea capreolus. All other species COL1 sequences used for reference were taken from Janzen et al. All archaeological samples were searched using the MaxQuant software (v. 2. 1. 3. 0,  [76] ) against the updated database of bovid species (electronic supplementary material), and using the following parameters: trypsin allowed missed cleavages was set on 3; carbamidomethylation of cysteines was set as fixed modification, and deamidation (N,Q), Gln to pyro-Glu (N term Q), phosphorylation (S, T) and oxidation (M, P) as variable modifications; mass tolerances were set to 10 ppm for precursor and 0.