dataesr/scientific-paragraphs-categorization huggingface dataset:
Currently, SQLite  [23] , a relational database management system, is being used to process website data; however, it will be migrated to PostgreSQL soon  [24, 25]  as it offers better concurrency control  [26]. Google OAuth  [27]  (login/logout), Google reCAP-TCHA [28], and Google simple mail transfer protocol (SMTP)  [29]  have been used during development. CoviRx sends emails to its users for various purposes, such as a copy of responses filled by them in contact and "contribute drug" forms; an invitation to the admin panel; and reporting errors that occur during drug upload. A python library called Premailer was used because emails do not support CSS or JS files, and it is important to deliver well-styled HTML emails to our users  [30]. Few properties of database systems and websites  [31] [32] [33]  form the foundation for CoviRx's architecture.
These design principles have been discussed in detail in Table  1. User interacts with the front-end, built using HTML, CSS, JQuery3  [14] , Bootstrap 5  [15] , to request data. Front-end then interacts with the webservers, e. g. , Azure, AWS, and Apache. These fetch data from the application servers powered by Django  [12, 13, 34] , which in turn interact with the database management system, i. e. , SQLite  [23]. Once these Data are extracted, it follows a reverse path back to the user. User interacts with the front-end, built using HTML, CSS, JQuery3  [14] , Bootstrap 5  [15] , to request data. Front-end then interacts with the webservers, e. g. , Azure, AWS, and Apache. These fetch data from the application servers powered by Django  [12, 13, 34] , which in turn interact with the database management system, i.
CoviRx Architecture An iterative software development model was followed  [36]  while designing CoviRx. First, this was achieved by creating a wireframe  [37]  for every front-end page, followed by a basic application implementation. The design components were then modified in each version by adding new features and functionalities. Each component was tested individually  [38]  before merging with the main framework. Upon its completion, the entire application was tested  [39]. Figure  5  depicts various pages/features of CoviRx. Data 2022, 7, x FOR PEER REVIEW 9 of 20.
CoviRx Architecture An iterative software development model was followed  [36]  while designing CoviRx. First, this was achieved by creating a wireframe  [37]  for every front-end page, followed by a basic application implementation. The design components were then modified in each version by adding new features and functionalities. Each component was tested individually  [38]  before merging with the main framework. Upon its completion, the entire application was tested  [39]. Figure  5  depicts various pages/features of CoviRx. Users can interact with our tool using features such as the search engine, drug overviews, contact forms, drug submission forms, etc. Features of the website's home page are depicted in Figure  6. Data 2022, 7, x FOR PEER REVIEW 9 of 20.
CoviRx Architecture An iterative software development model was followed  [36]  while designing CoviRx. First, this was achieved by creating a wireframe  [37]  for every front-end page, followed by a basic application implementation. The design components were then modified in each version by adding new features and functionalities. Each component was tested individually  [38]  before merging with the main framework. Upon its completion, the entire application was tested  [39]. Figure  5  depicts various pages/features of CoviRx. The CoviRx web app's top hierarchy consists of the admin panel [40] and the user interface. It was a design decision to keep the user interface interactive and straightforward. Users can interact with our tool using features such as the search engine, drug overviews, contact forms, drug submission forms, etc.
Drugs are ranked based on activity scores calculated using the assay data as described in MacRaild et al. , 2022  [10]. Another important feature of this page is the dynamic calculation and display of similar drug candidates. Drug similarity is calculated using the Tanimoto coefficient  [9, 45] , which is the ratio of common fingerprints and the number of total fingerprints. The RDKit python library  [46]  was used to generate the fingerprints of the drugs and calculate the Tanimoto similarity between them  [47]. The threshold for similarity is kept at a value of 0. 7, less than the suggested value of 0. 85  [48] , to increase the number of similar drugs. Once the similarity is calculated, the page displays the top 5 (i. e. , highest five Tanimoto coefficients) candidates.
Contribute Drug CoviRx supports and encourages the community to help us enlarge this open-source database. Authenticated individuals can contribute to the web app using the "contribute drug" feature, an overview of which is depicted in Figure  11. Only registered users can make these contributions to ensure data integrity and uniformity. All contributed drugs would be verified manually by a team of admin users to ensure data authenticity and reliability. Another important feature of this page is the dynamic calculation and display of similar drug candidates. Drug similarity is calculated using the Tanimoto coefficient  [9, 45] , which is the ratio of common fingerprints and the number of total fingerprints. The RDKit python library  [46]  was used to generate the fingerprints of the drugs and calculate the Tanimoto similarity between them  [47].
Admin Panel Bulk Upload Support To facilitate the process of bulk drug upload, CoviRx has a feature which enables the admin users to upload large amounts of drug data in one go by directly submitting a CSV file. A single drug upload/delete/modify functionality is also available. CoviRx evaluates every drug against multiple constraints before adding it to the primary database. These constraints ensure the validity of the drugs, so drugs that fail them are considered "invalidated" and shared with the admin users to reverify the data. Figure  12  depicts the various sub-features of the bulk upload feature.
Custom Fields Every data field of a drug displayed on the CoviRx platform maps to an attribute in the database. This implies that the code needs to be altered if a field is added, deleted, or modified for a particular drug. Custom fields have been added to overcome this problem of repeatedly changing the code and are critical components in the admin panel. It is a JSON field in the database that stores key-value pairs, with the key holding the name of the drug field and the value holding the value for that field  [52]. This feature would make CoviRx extensible and flexible in the long run.
Articles Scraping To ensure CoviRx does not end up being a static database after its creation, we have introduced a new feature to keep the database up to date with new articles published by peer-reviewed journals and reputed preprint servers such as bioRxiv and medRxiv. CoviRx looks up for new articles periodically (at present every month) to find new assay data for drugs present in the CoviRx database. The papers found are then assigned to admin users for verification. We selected Google Scholar  [53]  as a platform to find new articles as it covers a very wide range of publications including preprints. A paper can be found at multiple places (preprint server, peer reviewed journal, etc. ), in different formats (html, pdf, etc. ), therefore CoviRx ensures that only the most recent version gets scraped to prevent duplicate entries of the same article.
The scraped articles are then equitably assigned to admin users, who, as domain experts, verify the article and update the database. When an admin user becomes inactive or leaves, the articles assigned to them are distributed amongst the remaining admin users automatically. Articles are grouped drug-wise to ease verification as shown in Figure  13. The admin users can then view a list of the articles scraped, along with the drug, target model associated, and other keywords used for scraping as shown in Figure  14. If the articles found are relevant, the drug's COVIDvid-19 assay data can be updated with the help of the "Update form" as shown in Figure  15. Since CoviRx crowd-sources the article verification job to pharmacy students, it becomes imperative to monitor the articles being verified, as shown in Figure  16.
Conclusions CoviRx provides users with repurposed drugs that could have anti-viral activity against SARS-CoV-2, based on pharmacological information, assay activity, and drug status in COVID-19 clinical trials. Its user-friendly interface and multi-language support improve data accessibility. Various drug-likeness properties such as molecular weight, logP, hydrogen bond donors (HBD), and hydrogen bond acceptors (HBA), amongst others, are included for each compound to assist with early drug discovery programs. The users who wish to design their own filters for drug down-selection can benefit from the website's pharmacokinetics (PK) section, which provides information regarding a compound's route of administration, the volume of distribution, clearance, protein binding, etc. This platform also encourages its users to help expand this open-source dataset by submitting their drug repurposing data, and to register as volunteer administrators to keep the database error free and up to date.
Supplementary Materials: The following supporting information can be downloaded at: https: //www. mdpi. com/article/10. 3390/data7110164/s1. Table  S1   Website general information, Link S7: Code Repository, Link S8: Code Usage, S9: API calls available, S10. Additional information regarding reusability of CoviRx, S11: Supplementary references are included in "CoviRx-supplementary-information" file; "Total list of 214 drugs-table" is shared as a separate file which lists the drugs with the highest activity scores in CoviRx.
of external APIs used by CoviRx, Figure S3. 1: Drug upload form, Figure S3. 2 : Contact form, Figure S3. 3: Identifiers available for normal search mode, Figure S3. 4: Graphical visualization giving database overview, Figure S4. 1: Website Analytics, Figure S4. 2: Drug bulk upload feature, Figure S4. 3: Email sent by CoviRx to correct invalidated drugs, Figure S4. 4: Invitation email, Figure S4. 5: Tech stack used for development, S5: Tanimoto Coefficient formula, S6:Website general information, Link S7: Code Repository, Link S8: Code Usage, S9: API calls available, S10. Additional information regarding reusability of CoviRx, S11: Supplementary references are included in "CoviRx-supplementary-information" file; "Total list of 214 drugs-table" is shared as a separate file which lists the drugs with the highest activity scores in CoviRx.
of external APIs used by CoviRx, Figure S3. 1: Drug upload form, Figure S3. 2: Contact form, Figure S3. 3: Identifiers available for normal search mode, Figure S3. 4: Graphical visualization giving database overview, Figure S4. 1: Website Analytics, Figure S4. 2: Drug bulk upload feature, Figure S4. 3: Email sent by CoviRx to correct invalidated drugs, Figure S4. 4: Invitation email, Figure S4. 5: Tech stack used for development, S5: Tanimoto Coefficient formula, S6:.
Acknowledgments: The authors would like to acknowledge the contributions made to the project by the broader sySTEMs Initiative:  Aditya V Vashi ,  Alexander J McAuley ,  Carmel M O'Brien ,  Carol Lee ,  Chandana Venkatadri ,  Darcie Cooper ,  David Beale ,  Eugene Athan ,  Elizabeth Vincan ,  James Hudson ,  Jenny O'Connell ,  Jian-Wei Liu ,  John Noel Viana ,  Kanta Subbarao ,  Kathie Burkett ,  Kim R Blasdell ,  Laurence Wilson ,  Lee Trinidad ,  Mary Tachedjian ,  Matthew P Bruce ,  Michael Kuiper ,  Nagendrakumar Balasubramanian Singanallur ,  Nathan J Gödde ,  Petrus Jansen van Vuren ,  Sarah Goldie ,  Shane Riddell ,  Simone Clayton ,  Simran Chahal ,  Stephanie Keating ,  Trevor Drew  and  Vinod Sundaramoorthy.
The relevance of the different processes and their spatial variation were neither fully understood nor verified by observations, thus the need for further observation and analysis. The overall goal of the Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa (DACCIWA) project (European Union Framework Programme (FP7/2007-2013) under grant agreement no. 603502) was to significantly improve monitoring capacities and the scientific understanding of key interactions between surface-based emissions, atmospheric dynamics and chemistry, clouds, aerosols, and the climate of West Africa and to implement these findings into the next generation of weather and climate models  3. Therefore, from mid-June to end of July 2016, as part of DACCIWA, an extensive measurement/observational campaign was conducted in the region: one of the objectives being to obtain high-quality observations that would allow the identification of the relevant processes impacting the formation of LLSC and the ABL diurnal cycle.
Near-surface measurements. In Ile-Ife, an energy balance station was set up over a closely-cropped grass surface at the measurement site. The configuration of the used eddy-covariance (EC) system 17 comprised of a sonic anemometer and a Lambda Instruments Corporation (LI-COR) infrared gas analyser both installed on a 2. 1 m mast. Co-located with the EC system was a four-component net radiometer placed on a 1. 7 m radiation stand and a soil-heat flux plate buried at a depth of -0. 02 m and a soil moisture probe at -0. 05 m. A 15 m tower positioned nearby provided every 10 s multi-level measurements of wind speed at 5 selected heights (1. 14, 1. 88, 3. 3, 6. 3 and 12. 1 m AGL), arranged in a log-linear fashion. With the exception of the 1. 88 m AGL level, measurements of air temperature and relative humidity were provided with all the tower data averaged every minute.
The EC system sampled every 0. 1 s to produce the turbulent temperature, moisture and wind components. The integrated datasets were further reduced to 30-min averages to determine sensible and latent heat fluxes, among other meteorological parameters. In Kumasi, the energy balance parameters were measured with a LI-COR gas analyser co-located at 3. 5 m AGL with a sonic anemometer sampling respectively CO 2 concentrations, wind speed and direction and sonic temperature every 0. 05 s and stored as 60 s average in the dataset. Before averaging, the CO 2 concentration, wind speed and direction have been used to provide an estimation of the turbulent fluxes every 30 min. A radiation sensor at 1. 68 m AGL was sampling shortwave and longwave radiation every 5 s. Additionally, 6 soil moisture, soil temperature and soil heat flux probes, buried at a depth of -0.
08 m, sampled soil heat flux, soil temperature and soil water potential every 5 min. In addition to the energy balance tower, an automatic weather station has been set up at 2 m AGL measuring air temperature, relative humidity, pressure, wind speed, wind direction, and precipitation with one-minute resolution. In Savè, two towers were deployed on two different vegetation covers. The first one was deployed over a mix of grass and bushes  10  , with the instrumentation from the observation system KITcube  18. This tower was equipped with sensors sampling every 1 s air temperature, relative humidity (2 m AGL), air pressure (1. 5 m AGL), surface temperature and soil heat flux (-0. 05 m). Longwave and shortwave radiation was measured with the same sampling rate at 3 m AGL. Data from this tower have been aggregated every minute and every ten minutes.
The data from this device have been then aggregated over 60 s. Every 0. 1 s water content (average between -0. 01 and -0. 31 m), soil temperature (average between -0. 065 and -0. 0165 m) and soil heat flux (-0. 05 m) were recorded and then aggregated over 60 s. Precipitation (1. 3 m AGL), incoming and outgoing long and shortwave radiations (7. 16 m AGL) were sampled every 60 s. In addition, a sonic anemometer and a LI-COR gas analyser sampled the three wind components, the sonic temperature, the air humidity and the CO 2 concentration every 0. 1 s in order to estimate 30-min turbulent fluxes. Furthermore, biogenic soil fluxes of NO and NH 3 were measured at the Savè site with the use of a chemiluminescence analyser, and the closed dynamic chamber technique  20. Fluxes are given as daily averaged fluxes, calculated from 8 to 25 fluxes measured each day.
Data Records The present dataset with associated metadata is available on the Base Afrique de l'Ouest Beyond AMMA Base (BAOBAB) database http://baobab. sedoo. fr/DACCIWA/ where it is organised by country (Benin, Ghana, Nigeria), instrument and measured parameter. In total, 70 data records were generated and 7 DOIs have been created. All the data recorded in Ile-Ife can be found in Oluwagbeniga et al. 29  and in Kumasi in Brooks et al. 30. The data recorded at Savè site are structured as follows: all the data recorded by UPS are stored in Derrien et al. 20. The data sets from KIT include near-surface measurements  31  , cloud and precipitation measurements  32  and thermodynamic data from surface-based remote sensing  33. The meteorological and black carbon data measured with the unmanned research aircraft ALADINA of the TU Braunschweig are available in Bärfuss et al.
34. The data formatting strategy has been designed with the aim to make direct comparisons between sites. All data recorded except the cloud cameras at Kumasi and Savè are available in NetCDF format, Savè X-band radar data are additionally packed as tar. gz. The cloud camera in Kumasi provides images in AVI format, the images in Savè are stored in JPG format and are archived as tar. gz. Similar aggregation times have been used for fluxes estimates. The supplementary tables are splitting the dataset into 3 subjects i. e. , measurements of dynamics and thermodynamics in the boundary layer and above (Supplementary Table  1 ), measurements of cloud characteristics, aerosol and precipitation (Supplementary Table  2 ), near surface measurements (Supplementary Table  3 ), and energy balance measurements (Supplementary Table  4 ).
technical Validation Before and during the campaign, it was necessary to calibrate the individual sensors and measurement systems and to perform other quality controls to ensure the data quality of the DACCIWA data sets. Consequently, the standard meteorological sensors (e. g. temperature, humidity, air pressure, wind speed) were calibrated prior to the campaign. For example, for temperature and humidity this was done either in certified facilities (climate chambers) or at the manufacturer's premises. Anemometers were calibrated in certified wind tunnels or have been calibrated by the manufacturer. The equipment from the energy balance stations, like radiation instrumentation, was subject to regular calibrations by the manufacturers. The trace gas analysers operated at Savè supersite were calibrated regularly with reference gases.
Data collection and quick looks were performed to check the quality and the integrity of the data through the campaign (http://dacciwa. sedoo. fr). (3) After the experiment: each institution applied their own post-processing method to the instrumentation that belongs to them and agreements between institutions were made on the file format and some derived parameter products. Quality-controlled data including meta data were published on the BAOBAB repository. (4) Use of data: user feedback was considered and the technical and scientific quality of the dataset was proved in numerous scientific publications which successfully investigated the processes relevant for the diurnal cycle of the ABL and the nocturnal LLSC evolution. Kalthoff et al. 10  show that extended LLSCs form frequently during the night-time and persist into the following day in the investigation area in June and July, while their characteristics (formation and dissolution time, cloud base height) vary considerably from day to day.
The soil fluxes of nitric oxide (NO) and ammonia (NH 3 ) observed over four different land cover types at Savè were successfully used for a model comparison with the Goddard Earth Observing System Chemistry Model (GEOS-Chem)  35. Reinares Martines et al. 48 analysed a warm-rain episode over southern West Africa using X-band radar measurements from Savè in combination with LES. The radiosoundings at Kumasi supersite were also applied to assess the quality of a newly developed reusable radiosonde systems  16. The numerous studies highlight that the datasets collected at the three supersites allowed to successfully solve several of DACCIWA questions and is a valuable addition to already available datasets like those from the African Monsoon Multidisciplinary Analysis (AMMA)  49, 50  project and the West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL)  51, 52  project.
author contributions Ile-Ife site measurements: Oluwagbemiga Jegede contributed to the writing of the paper. Planned and coordinated the DACCIWA measurement campaign in Ile-Ife, provided all the logistics including instrument and manpower deployment, programming of data acquisition systems, field measurements, data retrieval and post-processing. Uploaded campaign data to the BAOBAB database. Muritala Ayoola participated in instrument deployment, field measurements, data retrieval and post-processing. Kumasi site measurements: Barbara Brooks contributed in the writing of the paper, planned the experiment, organised and coordinated the Kumasi site, prepared the experiment including the development of measurement strategy, participated in the measurement campaign and was responsible for the organisation of post-processing.
Jeffrey Nii Armah Aryee, Fred Cayle-Aethelhard and Winifred Ayinpogbilla Atiah participated in the instrumentation set-up the measurement campaign and the instrumentation pack-up. Kwabena Fosu-Amankwah participated in the instrumentation set-up, the measurement campaign and the instrumentation pack-up, and largely contributed to the frequent radiosounding activities. Leonard Kofitse Amekudzi contributed in the writing of the paper, was responsible for the logistics at KNUST and coordinated communication inter sites. Sylvester Danuor was responsible for the invitation for the NCAS team. Savè site measurements: Martin Kohler was responsible for the writing of the paper, participated in the measurement campaign, performed data quality control, data postprocessing, data documentation, generation of metadata and submission of data to the database.
Norbert Kalthoff contributed in the writing of the paper, planned the experiment, organised and coordinated the campaign, prepared the experiment including the development of measurement strategy, participated in the measurement campaign and was responsible for the organisation of post-processing. Bianca Adler contributed in the writing of the paper, contributed to the preparation of campaign including the development of measurement strategy, participated in the measurement campaign, programmed quick looks, performed data quality control, data post-processing, data documentation, generation of metadata and submission of data to the database. Karmen Babić contributed in the writing of the paper, performed data quality control, data post-processing, generation of metadata, and submission of data to the database.
Jan Handwerker programmed quick looks, participated in the measurement campaign, generated metadata and submitted data to the database. Norbert Kunka established the infrastructure for data acquisition, data backup and network, and participated in the measurement campaign. Andreas Wieser was involved in organisation and coordination of the campaign, setup of instrumentation, participated in the measurement campaign and performed data quality control. Astrid Lampert has coordinated TU Braunschweig team and data analysis. Barbara Altstädter was involved in the data analysis of ABL properties and black carbon. Falk Pätzold was responsible for the instrumentation and data acquisition set-up, as well for data post-processing. Konrad Bärfuss was involved in the instrumentation and data acquisition set-up, data post-processing and campaign participation.