dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016) , considered among the most common type of running water system in the world, the data encapsulated by this work remain largely fragmented and hence "dark. " As such, our ability to answer the key questions beyond a case-by-case basis remains limited. The Intermittent River Biodiversity Analysis and Synthesis (IRBAS,  Datry, Larned, & Tockner, 2014)  database is the first of its kind specifically dedicated to the digital storage and provision of biodiversity, and associated hydrological and environmental, data on intermittent rivers worldwide. The IRBAS database (http://irbas. cesab. org) draws on primary datasets, collates data into standardized formats, and is accessible via an online platform where users can contribute, query, and request data. The IRBAS database thus has the capacity to grow in content as the study of intermittent rivers continues and allows data retrieval for meta-analyses to address questions and test hypotheses across multiple systems, regions, and taxa (Table  1 ).
It also facilitates networking within and among scientists, managers, policymakers, and the public interested or involved in the study and management of river biodiversity (e. g. , as represented by networks and aligned projects such as BioFresh, http://project. freshwaterbiodiversity. eu/; Life TRivers, www. lifetrivers. eu; 1000 Intermittent Rivers Project, http://1000_ intermittent_rivers_project. irstea. fr; H2020 European COST Action Science and Management of Intermittent Rivers and Ephemeral Streams, SMIRES, http://www. cost. eu/COST_Actions/ca/CA15113). In this paper, we describe the IRBAS database, including how users can contribute to it and access data. We outline the key benefits of using and contributing to the database and consider its future applications for fundamental and applied research.
| DATABASE STRUCTURE, MODULES, AND LANGUAGE The IRBAS database is a relational database that houses three main types of data: mandatory (1) site and sampling location information, (2) biotic sample data and methods information, and optional (3) environmental sample data and methods information. Together with general metadata (e. g. , data contributor and associated publication information; Table  1 ), these types of data are each uniquely identified and linked to an individual and uniquely identified dataset, which is entered into the database by a contributor through the use of template files (Figure  1 ). The IRBAS database is designed so that it can flexibly handle and represent datasets from different sources, and which may vary, for example, in levels of taxonomic resolution and sampling methods.
It is based on the structure of a similarly flexible database, BETSI (http:// betsi. cesab. org) for soil invertebrates, which in turn possesses key aspects of other, flexible relational database schema (e. g. , as used in genomics research, such as CHADO; http://gmod. org/wiki/Chado) specifically designed to handle complex representations of biological knowledge via the use of controlled vocabulary and modules that logically separate database components. These aspects reduce database complexity while facilitating modification and/or extension in the future. Several modules handle general metadata on the contributed datasets (Table  1 ). A dedicated module links source information (e. g. , publication details) to each contributed dataset. A second module stores information such as dataset contributor and availability status (i.
e. , whether the contributor has allowed users full or restricted access to the dataset). A third module stores all other types of general metadata (e. g. , the date of entry into the database). A module is dedicated to storing what we refer to as sample data, allowing us to separate data into biotic (i. e. , taxonomic observations of biota) or environmental (e. g. , water temperature measures) data. Taxonomic observations are also linked to a taxonomy module which stores information on taxonomic classification. This identifies the rank of each taxon observed (e. g. , as family, genus, or species) and allows datasets to include taxa identified at different levels of taxonomic resolution (see Taxonomy Module for more information). Each sample within a dataset, whether it is a biotic or an environmental sample, must include information on sampling methods (metadata; Table  1 ) as well as the actual values of the measures taken (sample data) and must also be linked to a spatial component (i.
e. , a particular location identified by latitude and longitude in the WGS84 datum) which is managed in a dedicated module and allows samples to be visually located on a map of the world (see  Contributing and Accessing Data). This spatial information is also considered metadata (Table  1 ). The IRBAS database is written in Structured Query Language (SQL) and runs under PostgreSQL, which will allow the option of adding geographical information system (GIS) functionality to the database in the future if desired (via the PostGIS module). Queries are performed as functions written in pl/SQL, via scripting executed through T A B L E 1 Types of metadata stored in the IRBAS database. Asterisks indicate metadata that are available through the IRBAS interface via a map-based search (Figure  2 ) an interface under the content management system (CMS) DRUPAL, written in PHP.
| CONTRIBUTING AND ACCESSING DATA The IRBAS database is accessible via its online Web interface, hosted by CESAB (Centre de Synthèse et d'Analyse sur la Biodiversité; http:// irbas. cesab. org), and includes several pages dedicated to data contribution and access. These pages explain how to: Step 1. contribute data a. get started as a registered user b. fill in data entry templates c. save the completed templates Step 2. insert completed templates into the database Step 3. explore the database and request data a. via a map interface (identifying locations of samples and extracting metadata) b. via queries (performing more detailed data requests and extraction) (Figure  2 ).
| Step 1: Contributing data Contributing data first requires setting up a username and password to become a registered user of the IRBAS database (Step 1a; Figure  2 ). This allows users to log-in and gain full access to the database as both a contributor and requester. Registered users wishing to contribute data can download empty template spreadsheets (in the form of tab-delimited. txt files) that are used to standardize the format, units, and terminology of datasets, samples, and measures (Step 1b; Figure  2 ). For each dataset submitted, a completed Site and Biota template must be provided (i. e. , completion of these two templates is mandatory); completion of the Environment template is optional (Figure  1 ). The database is primarily a biodiversity database, but environmental data are welcome and increase the value and usefulness of the data contributed and the versatility of the database.
Each template also contains fields for general metadata which detail the associated dataset name, data contributor, associated publication citation details, data availability status, etc. (Table  1 ). The general metadata must be uniform across each Site, Biota, and Environment templates that encapsulate a unique dataset (Figure  1 ). The data F I G U R E 1 Schematic of the processing and flow of data in the IRBAS database. For each dataset submitted, completion of a Site and Biota template is mandatory (solid arrows) and completion of an Environment template is optional (broken arrows). A dataset must comprise general metadata (e. g. , dataset name, data contributor; Table  1 ) and data collected on sites, biota, and optionally on habitat (environmental data). The Site template is used to insert the site data, including spatial (geo-locational) data and other metadata about the sampling locations, for example, river names and flow regimes (Table  1 ).
The Biota template is used to insert the biotic sample data (taxonomical inventories of biota) along with metadata on the associated sampling methods and flow status of sites at the time of sampling (Table  1 ). The Environment template is used to insert the environment sample data (physical and chemical measures) along with metadata on the associated sampling methods and flow status of sites at the time of sampling (   2 ) before insertion into the database. This process continues until no further errors are detected at which point the contributor is informed that the files have been inserted successfully.
| Step 3: Exploring the database and requesting data All registered users can explore the database and access data via a map (Step 3a; Figures  2  and 3 ) or via queries (Step 3b). The map shows the locations in the world of all samples across all datasets in the database. Users can zoom in and out to identify specific locations where samples are present and to access summary metadata on these samples and locations (Table  1 ). Queries follow a simple check-box procedure and allow users to make generic or specific data requests via the interface. For example, users may be interested in data on fish only, or from certain time spans, rivers or regions, or in certain datasets only. Results are extracted from the database and provided to users as downloadable (tab-delimited). csv files.
| TAXONOMY MODULE To ensure taxa stored in the IRBAS database are named according to the latest international conventions, the IRBAS database uses Encyclopedia F I G U R E 2 Steps involved in contributing to and accessing data from the IRBAS database. Steps 1 and 2 result in the contribution of a dataset to the IRBAS database (see Figure  1 ), which requires users to register, fill in templates with data (solid arrows indicate mandatory templates and broken arrows optional templates), save these as. csv files, and then insert them into the IRBAS database via the IRBAS interface. Step 2 involves a quality control and assurance parsing process to ensure data are inserted correctly, completely, and in accordance with the standardization requirements of the database, and the checking of taxonomic nomenclature in the Biota file against the IRBAS taxonomic library (Figure  4 ).
Registered users can explore the database via the interface by zooming in on locations with data presented on a map of the world (Step 3a) and make requests to download data (Step 3b) using a simple check-box selection procedure that sends specific queries via the interface to the database. IRBAS logo © IRBAS Step 1: ContribuƟng data of Life (hereafter EOL; http://www. eol. org/) as its authoritative resource for taxonomic nomenclature and classification hierarchies. The taxonomic and geographical coverage of EOL is not limited to particular countries, continents, or hemispheres, or to particular groups of organisms (e. g. , invertebrates, fish, or plants), thus matching the global scope of the IRBAS database. All names of taxa supplied by contributors are parsed via a list of names stored on the database (the IRBAS taxonomic library), recognized by EOL, before datasets are inserted (Figure  4 ).
We compiled the library by first identifying families of taxa from around the world commonly found in rivers, wetlands, and riparian zones. All taxa within these families and their associated classification ranks within the taxonomic hierarchies listed in EOL were then entered into the database using a PHP script and the EOL application programming interface (API) (http://eol. org/info/api_overview), which essentially embeds the functionality of EOL into the taxonomy module of the IRBAS database. The resulting, relational list of taxa (>600,000) and their ranks is likely to cover the majority of taxa encountered in contributed datasets. During dataset insertion, the taxonomic nomenclature used in the completed Biota files of contributors is checked against this EOL-generated library, and if errors are encountered, the contributor is alerted following the procedure outlined in Step 2 above.
| DATABASE CONTENT As of August 2016, the IRBAS database houses data from 11 contributed datasets, associated with over 2,000 individual samples of biological, hydrological, and environmental data and over 400 taxa. These data were collected from intermittent rivers (and in some cases also from nearby perennial rivers) in North America (USA, 3 datasets), Europe (France, Spain, and UK; 3, 1 and 1 datasets, respectively), and Oceania (Australia and New Zealand; 1 and 2 datasets, respectively; Figure  3 ). The database currently lacks data from the continents of Africa, Asia, and South America and from rivers in tropical (D) and polar-alpine (E) climate zones (according to the Köppen-Geiger climate classification). The majority of sampling sites are in temperate climate zones (60%) and on intermittent rivers (67%).
The database also includes data collected from perennial rivers and perennial reaches of intermittent rivers. This is because many ecological studies of intermittent rivers seek to make comparisons with perennial rivers (asking questions such as "Are biological communities of intermittent river reaches subsets of those of nearby perennial river reaches?") to better understand how flow intermittence acts as a hydrological determinant of river biodiversity (e. g. ,  Datry, Larned, Fritz, et al. , 2014; Leigh & Datry, 2016). Hence, the IRBAS database accepts datasets comprising data collected from both intermittent and perennial rivers. Biotic samples currently housed in the database primarily describe aquatic invertebrate communities (80% of biotic samples). Fish and terrestrial invertebrate communities are represented to a lesser extent (7% and 8% of biotic samples, respectively), and to date, there are no plant community samples represented.
| BENEFITS OF CONTRIBUTING AND ACCESSING DATA The IRBAS database was compiled with the view, espoused by col-   (Datry, Larned, Fritz, et al. , 2014; Leigh & Datry, 2016; Leigh, Stubbington, Sheldon, & Boulton, 2013; Leigh, Bonada, et al. , 2016)  demonstrate the value of collating, standardizing, and analyzing data from disparate sources; in fact, many of the individual datasets compiled and synthesized for these studies are now available via the IRBAS database. These studies have deepened understanding of the resistance and resilience of aquatic biota to flow intermittence and identified general relationships between hydrological and environmental drivers and community responses, a major goal of ecological and biodiversity research. Further insights will no doubt come from new analyses and continued synthesis of data from across the globe facilitated by the IRBAS database to address the myriad of questions and challenges that intermittent river systems, respectively, inspire and pose  (Datry, Fritz, et al.
2016; Datry, Larned, & Tockner, 2014 ; Table  2 ). Through its visibility, the database portal will also raise awareness of the global prevalence and ecological significance of intermittent rivers for scientists and water managers. Biological and ecological information on intermittent rivers is being sought increasingly by managers and policymakers faced with increasing flow intermittence, resulting from climate change and growing demands for freshwater, and the monitoring and management challenges this presents  (Datry, Fritz, & Leigh, 2016). Observed and projected shifts from perennial to intermittent flow regimes and increased duration and frequency of drying in intermittent rivers have thus heightened the need for a centralized and open-access database such as the IRBAS database. T A B L E 2 Ten research questions the IRBAS database is helping to address or may help to address in the future Top research questions.
9. Are there predictable differences in community diversity and composition between natural and human-induced intermittent rivers? 10. How are intermittent rivers and their biota responding to global change, and what do we predict for the future? By nature of its open access, the IRBAS database will promote synergies among research groups worldwide. It will also build on previous freshwater biodiversity data synthesis efforts at the European scale and beyond, for example, via links to BioFresh (http://data. freshwaterbiodiversity. eu/) and GBIF (http://www. gbif. org/), and will continue to grow both in terms of its user and dataset numbers. For example, the database will be expanded and used by a consortium of 150 researchers involved in the SMIRES Cost Action.
| IMPORTANT CONSIDERATIONS The IRBAS consortium has applied the Creative Commons CC-By 4. 0 (https://creativecommons. org/licenses/by/4. 0/) approach to protect the data and ensure that data providers are properly acknowledged by users. Access to the database is password protected and to ensure users that the data they contribute are held securely: (1) Data from the IRBAS database are not to be used for any commercial purpose without permission from the data contributor(s) and (  2 ) users accept all risks and responsibility for losses, damages, costs, and other consequences (direct or indirect) resulting directly or indirectly from using the interface and any information or material available from it. Any use of data from the IRBAS database must attribute credit to the IRBAS consortium (by citing this IRBAS database publication along with the IRBAS project publication  Datry, Larned, & Tockner, 2014)  and the original data contributor(s).
Use of the data must also include citation of the original source(s) [e. g. , journal publication(s)] describing the collection and presentation of the relevant datasets. Citation details are available when accessing data via the interface to the IRBAS database (http://irbas. cesab. org). Users need also to be aware that the data housed within the database are limited to those encapsulated by the fields and standardized formats of the Site, Biota, and Environment templates. There is capacity, however, to expand, adapt, or improve the existing database owing to the underlying, flexible nature of the database structure and its use of modules and a controlled vocabulary. Essentially, new functionalities and data types could be added in the future without the need for major redevelopment. This, for example, could include the addition of new modules linked to the taxonomy module that house molecular systematics and species traits information.
| CONCLUDING REMARKS The IRBAS database acts as both a data portal and discovery tool, facilitating the synthesis and analysis of data to elucidate patterns in and controls on intermittent river biodiversity. Contributing data, and providing them in the standardized formats generated by the database, not only increases data visibility but also useability. As the IRBAS database grows, we expect to see new insights arising from future, previously impossible and unimagined, meta-analyses and for collaborative research initiatives to continuously evolve. This will allow generalizations to be tested across multiple systems, regions, and taxa and better equip scientists and managers with the data and knowledge they need to study, conserve, or restore river ecosystems and biodiversity. We heartily encourage contributions of data from around the world.
Saved, completed templates are inserted into the database via the interface's file-selection dialog box (Step 2; Figure2). Step 2 involves a quality control and assurance parsing process to ensure data are entered correctly, completely, and in accordance with the standardization requirements [e. g. , mandatory fields are filled, taxonomy is acceptable (see Taxonomy Module), data values are not impossible, and sample identifiers are unique]. Where errors are detected, the contributor is given information, via the interface, which will allow them to detect and correct the errors, and resave and reinsert their files.
I G U R E 3 Geographical coverage and numbers of samples with biodiversity or environmental data collected from intermittent and nearby perennial rivers, stored in the IRBAS database as of August 2016. The IRBAS database allows users to contribute and access data and will thus continue to grow in content and value as a research and networking tool. Map data: ©2016 Google F I G U R E 4 The IRBAS database uses Encyclopedia of Life (EOL; http://www. eol. org/) as its authoritative resource for taxonomic nomenclature and classification hierarchies. All names of taxa supplied by contributors are parsed via a list of names stored on the database (the IRBAS taxonomic library), recognized by EOL, before datasets are inserted into the IRBAS database.
ACKNOWLEDGMENTS CL was funded as a postdoctoral researcher by IRBAS (Intermittent River Biodiversity Analysis and Synthesis working group; www. irbas. fr), which is supported by the  Centre for Synthesis and Analysis of Biodiversity (CESAB) , and funded jointly by the  French Foundation for Research & Biodiversity (FRB)  and the  French National Agency for Water and Aquatic Environments (ONEMA). CESAB hosted several workshops where ideas central to this manuscript, and the development of the IRBAS database, were discussed among its authors. We thank  CESAB  staff  Magali Grana  and directors  Eric Garnier  and  Alison Specht  for their support throughout,  Andrew Boulton ,  Clifford Dahm , and  Bernard Hugueny  for their contributions to and support of the  IRBAS  project and participants, and  Pierre Mancini  for assistance with Web interface development.
F I G U R E 3 Geographical coverage and numbers of samples with biodiversity or environmental data collected from intermittent and nearby perennial rivers, stored in the IRBAS database as of August 2016. The IRBAS database allows users to contribute and access data and will thus continue to grow in content and value as a research and networking tool. Map data: ©2016 Google F I G U R E 4 The IRBAS database uses Encyclopedia of Life (EOL; http://www. eol. org/) as its authoritative resource for taxonomic nomenclature and classification hierarchies. All names of taxa supplied by contributors are parsed via a list of names stored on the database (the IRBAS taxonomic library), recognized by EOL, before datasets are inserted into the IRBAS database.
By combining the MUSE spectroscopy with the current HFF data, we can therefore improve on existing lens models, probing the mass distribution out to larger scales and with much higher precision. In this way, we can improve our view of this massive cluster in conjunction with the final HFF data release. Overall, this work is organized as follows: In Section 2, we describe the MUSE and HFF data and the data reduction processes we used. In Section 3, we describe how we extract MUSE redshifts and present the A370 redshift catalog, paying particular attention to redshifts of the multiplyimaged galaxies. Using these redshifts, we construct a new mass model, which we present in Section 4. We discuss the results of the mass modeling and make predictions for future work in Section 5. Finally, we briefly conclude in Section 6.
HST Imaging HST imaging data of A370 were taken as part of the Hubble Frontier Fields (HFF) Program, and are publicly available on the HFF website 2. While the imaging campaign for A370 is now complete, the Epoch 1 (v1. 0) mosaics that we use in this work consist of deep Advanced Camera for Surveys (ACS;  Ford et al. 2003)  data in three optical bands, F435W, F606W, and F814W (ID: 13504, PI: J. Lotz). These are supplemented by shallower F814W imaging from archival programs: (ID: 11507, PI: K. Noll) and (ID: 11591, PI J. -P. Kneib). Additionally, shallow, archival, Wide-Field Camera 3 (WFC3;  Kimble et al. 2008 ) data is available in three bands: F105W (ID: 13459, PI: T. Treu), F140W (ID: 11108, PI: E. Hu; ID: 13459, PI: T. Treu), and F160W (ID: 11591, PI: J. -P. Kneib; ID: 14216, PI: R. Kirshner), and a shallow, pilot HFF exposure, also in the F140W band.