Find research datasets worth reusing

opencc-by-4.0Jan 2023View details →

Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse

Datasets and R source code of the article Depeux C, Branger A, Moulignier T, Moreau J, Lemaître J-F, Dechaume-Moncharmont F-X, Laverre T, Paulhac H, Gaillard J-M, Beltran-Bech S (2023) Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse. Peer Community Journal 3:e7 http://dx.doi.org/<a href="https://doi.org/10.24072/pcjournal.228">10.24072/pcjournal.228</a> This article previously appeared as preprint Depeux C, Branger A, Moulignier T, Moreau J, Lemaître J-F, Dechaume-Moncharmont F-X, Laverre T, Pauhlac H, Gaillard J-M, Beltran-Bech S (2022) Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse. bioRxiv, 2022.09.26.509512 https://doi.org/10.1101/2022.09.26.509512 Peer-reviewed and recommended by Peer Community in Ecology:  Belsare A (2022) An experimental approach for understanding how terrestrial isopods respond to environmental stressors. Peer Community in Ecology, 100506. <a href="https://doi.org/10.24072/pci.ecology.100506">https://doi.org/10.24072/pci.ecology.100506</a>

opencc-by-4.0Apr 2023View details →

Dataset of a 5G RTSP video streaming use case

About the project: monitoring 5G RTSP video streaming This dataset collects data from a 5G video streaming use case. A video is streamed by a cvlc server (realized as a Kubernetes pod) through RTSP to a variable number of 5G UE clients that activate according to a daily traffic pattern. The values of the 4 dataset features (number of active UEs, gNB's downlink bit rate, pod's outbound traffic, and pod's CPU usage) are collected by a custom monitoring system deployed in the context of the MONB5G project. Setup/Equipment The Kubernetes cluster, including the server pod, runs in a COTS server. The 5G core and gNB is realized through Amarisoft Callbox Ultimate. The UEs are emulated through Amarisoft Simbox. In order to display the video in ffplay clients, we use the Remote UE from Amarisoft, so traffic from Simbox is forwarded to an external VM with GUI. Video The streamed video is Big Buck Bunny at 30 FPS from <a href="https://peach.blender.org/">https://peach.blender.org/</a>. <table> <tbody> <tr> <td> Video codec  </td> <td> Advanced Video Codec (AVC)  </td> </tr> <tr> <td> Width  </td> <td> 1920 pixels  </td> </tr> <tr> <td> Height  </td> <td> 1080 pixels  </td> </tr> <tr> <td> Display aspect radio  </td> <td> 16:9  </td> </tr> <tr> <td> Duration  </td> <td> 10 min 34 s  </td> </tr> <tr> <td> Max Bitrate  </td> <td> 16.7 Mb/s  </td> </tr> <tr> <td> Frame rate  </td> <td> 30 FPS  </td> </tr> </tbody> </table> What does this Zenodo project contain? <ol> <li>The csv file of the dataset (dataset.csv)</li> <li>A picture displaying an overview of the setup (overview.png)</li> <li>A picture displaying Grafana charts for each featuer (grafana.png)</li> <li>A picture displaying a screenshot of the Remote UE VM with multiple UEs playing the video (ues.png)</li> </ol> Dataset The dataset has 5 columns (time + 4 features). Features: <ol> <li>Time: timestamp in epoch format.</li> <li>Number of active UEs (N): number of UEs that are currently downloading more than 100 kbps. No unit.</li> <li>gNB's downlink bit rate (R): aggregate downlinkg bitrate from the gNB to all the UEs. In Mbps.</li> <li>Outbound traffic (O): outboun traffic at the pod's interface, transmitting the video(s) packets. In Mbps.</li> <li>CPU (C): CPU usage at the server pod. In millicores (mc). Each iteration represents a whole day, composed of 24 "demand periods". Each demand period takes 2 minutes and is given by the number of active UEs consuming the video stream (N). N is included for informative reasons. Sampling rate is 10 seconds, but some parameters are refreshed at a lower frequency given monitoring limitations. This means that some parameters repeat the same value in consecutive measurements.</li> </ol>

opencc-by-4.0Apr 2023View details →

Documentary sources of case studies on the issues a data protection officer faces on a daily basis

The dataset contains the text of the documents that are sources of evidence used in [1] and [2] to distill our reference scenarios according to the methodology suggested by Yin in [3]. The dataset is composed of 95 unique document texts spanning the period 2005-2022. This dataset makes available a corpus of documentary sources useful for outlining case studies related to scenarios in which the DPO finds himself operating in the performance of his daily activities. The language used in the corpus is mainly Italian, but some documents are in English and French. For the reader's benefit, we provide an English translation of the title of each document. The documentary sources are of many types (for example, court decisions, supervisory authorities' decisions, job advertisements, and newspaper articles), provided by different bodies (such as supervisor authorities,  data controllers, European Union institutions, private companies, courts, public authorities, research organizations, newspapers, and public administrations),  and redacted from distinct professional roles (for example, data protection officers, general managers, university rectors, collegiate bodies, judges, and journalists). The documentary sources were collected from 31 different bodies. Most of the documents in the corpus (a total of 83 documents) have been transformed into Rich Text Format (RTF), while the other documents (a total of 12) are in PDF format. All the documents have been manually read and verified. The dataset is helpful as a starting point for a case studies analysis on the daily issues a data protection officer face. Details on the methodology can be found in the accompanying papers. The available files are as follows: <ul> <li>documents-texts.zip --> contain a directory of .rtf files (in some cases .pdf files) with the text of documents used as sources for the case studies. Each file has been renamed with its SHA1 hash so that it can be easily recognized.</li> <li>documents-metadata.csv --> Contains a CSV file with the metadata for each document used as a source for the case studies.</li> </ul> This dataset is the original one used in the publication [1] and the preprint containing the additional material [2]. [1] F. Ciclosi and F. Massacci, "The Data Protection Officer: A Ubiquitous Role That No One Really Knows" in IEEE Security & Privacy, vol. 21, no. 01, pp. 66-77, 2023, doi: 10.1109/MSEC.2022.3222115, url: https://doi.ieeecomputersociety.org/10.1109/MSEC.2022.3222115. [2] F. Ciclosi and F. Massacci, "The Data Protection Officer, an ubiquitous role nobody really knows." arXiv preprint arXiv:2212.07712, 2022. [3] R. K. Yin, Case study research and applications. Sage, 2018.

opencc-by-4.0May 2023View details →

Experimenting with Formal Verification and Model-based Development in Railways: the case of UMC and Sparx Enterprise Architect - Complementary Data

This repository contains the UMC and SPARX EA data used in the paper: Experimenting with Formal Verification and Model-based Development in Railways: the case of UMC and Sparx Enterprise Architect by Davide Basile, Franco Mazzanti and Alessio Ferrari.

opencc-by-4.0Aug 2023View details →

Data for "Saturation of destratifying and restratifying instabilities during down front wind events: a case study in the Irminger Sea"

This archive contains processed data used in the study "Saturation of destratifying and restratifying instabilities during down front wind events: a case study in the Irminger Sea". We are grateful for the financial support of the Natural Environment Research Council (grants NE/L002612/1 and NE/T013494/1). This work used the ARCHER2 UK National Supercomputing Service (https://www.archer2.ac.uk). We would also like to thank Andrew Coward for providing computational support. The results contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. The results contain modified GEBCO data produced by the GEBCO Compilation Group (2023) GEBCO 2023 Grid (doi:10.5285/f98b053b-0cbc-6c23-e053-6c86abc0af7b)

openother-openSep 2023View details →

Harmonised branded foods dataset for FNS Cloud case studies (2017-2020)

The dataset was compiled by merging and harmonisation of different datasets, which were compiled in four countries (Netherlands, Germany, Slovenia, Switzerland) in period 2017-2020 with the use of different methodologies. Dataset was prepared to support conduction of case studies within the HORIZON2020 FNS Cloud project. The dataset includes selected mandatory food labelling information about nutrition composition of foods/drinks within three categories, that are important for reformulation strategies: soft drinks, breakfast cereals and yoghurts. For one of the included datasets, data aslo include category of fruit and vegetable juices (including nectars).

opencc-by-4.0Oct 2022View details →

Deliverable 2.3 Design Load Case Database for Code-to-Code Comparison

In work package 2 of FLOATECH a detailed validation and verification of the capabilities of QBlade-Ocean was performed. Thereby, three wind turbine models mounted on floating substructures with differing characteristics serve as the means for the validation.This  dataset contains Floating Offshore Wind Turbine (FOWT) calculations in various design situations, computed with three different codes. In more detail, three floating platform archetypes are used in the code-to-code comparison ongoing in work package 2: a semi-submersible-type floater and a spar-type floater as well as the Hexafloat® concept recently proposed by Saipem®. The three test-cases are the NREL 5MW RWT mounted on the DeepCwind semi-submersible platform, the DTU 10MW RWT mounted on the SOFTWIND spar-type platform and the DTU 10MW RWT mounted on the Hexafloat® platform.  More details regarding the dataset structure and the testcases can be found in the accompanying document.    Changelog:  Version 5.0.0 <ul> <li>Changed structural damping ratio (increased) in OpenFAST results (SOFTWIND and Hexafloat models) to match structural damping ratios in QBlade</li> <li>Added wave time series at platform undisplaced position</li> <li>Added TurbSim input files for wind field generation. TurbSim v2.0.0 (https://www.nrel.gov/wind/nwtc/turbsim.html - National Renewable Energy Laboratory)</li> <li>Added detailed DLC and simulation information in "DLC&SimulationList.xlsx"</li> </ul> Version 4.0.0 <ul> <li>Correction of bugs in QB 5MWOC4 and 10MWSOFT models: wind shear exponent in wind fields (0.11 -> 0.14)</li> <li>Update of hydrodynamic database used in OpenFAST and QBlade results of 10MWSOFT model </li> </ul>   <pre><code>Data in version 5.0.0 is used in WES publication: Papi, F., Troise, G., Behrens de Luna, R., Saverin, J., Perez-Becker, S., Marten, D., Ducasse, M.-L., and Bianchini, A.: A Code-to-Code Comparison for Floating Offshore Wind Turbine Simulation in Realistic Environmental Conditions: Quantifying the Impact of Modeling Fidelity on Different Substructure Concepts, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2023-107, in review, 2023.</code></pre>

opencc-by-nc-4.0Sep 2023View details →

Qualitative Data on 60 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain)

<h2>Dataset name</h2> Small_Scale_Fishery_Data_2023_v2                   <h2>Title</h2> Qualitative Data on 60 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain).                           <h2>Description         </h2>                This dataset was created for the Fish2Sustainability research project, which aims to evaluate how small-scale fisheries (SSF) contribute to Sustainable Development Goals (SDGs). The dataset includes 60 case studies across eight countries and was developed using a rapid appraisal framework. The framework includes a four-step process:                1.     Identifying specific SDG targets influenced by SSF;                2.     Extracting relevant variables from UN indicators;                3.     Gathering expert input via a questionnaire to score these variables;                4.     Creating composite indicators to measure SSF performance against SDGs.                The dataset contains raw data from step 3, case study details, variable scores, and comments from data collectors (contributing authors). The dataset is valuable for researchers interested in small-scale fisheries and socio-ecological systems. By incorporating expert judgments from individuals with expertise in SSF, particularly in data-poor contexts, the dataset offers a wealth of knowledge for conducting comparative analyses across different contexts.               <h2>Authors               </h2> Léopold, M.1, Bitoun, R.E.2, Beckensteiner, J.3, Chuenpagdee, R.4, Fondo, E.N.5, Akintola, S.L.6, Bach, P.7, Frangoudes, K.8, Gaibor, N.9, Gutierrez-Cala, L.10, Massey, Y.7, Randrianandrasana, R.11, Razanakoto, T.11, Saavedra-Díaz, L.M.10, Schreiber Arias, M.12,13, Salas, S.14, Devillers, R.2,4          <h3>Affiliations          </h3> 1             ENTROPIE (IRD, University of La Reunion, CNRS, University of New Caledonia, Ifremer), c/o IUEM, Plouzané, France 2             Espace-Dev (IRD, Univ. Montpellier, Univ. Guyane, Univ. La Réunion, Univ. Antilles, Univ. Nouvelle Calédonie), Montpellier, France 3             AMURE (Ifremer, UBO, CNRS), Plouzané, France 4             Department of Geography, Memorial University of Newfoundland, St. John’s, NL, Canada 5             Kenya Marine and Fisheries Research Institute, Mombasa, Kenya 6             Department of Fisheries, Faculty of Science, Lagos State University, Nigeria 7             MARBEC, University of Montpellier, CNRS, Ifremer, IRD, Sète, France 8             Université de Bretagne Occidentale: Brest, France 9             Instituto Público de Investigación de Acuicultura y Pesca (IPIAP), Universidad del Pacifico (UPAC), Guayaquil, Ecuador 10          Grupo de Investigación en Sistemas Socioecológicos para el Bienestar Humano (GISSBH), Programa de Biología, Universidad del Magdalena, Colombia 11          Centre d’Etudes et de Recherches Economiques pour le Développement (CERED), Université d’Antananarivo, Madagascar 12          EqualSea Lab, Universidad Santiago de Compostela, A Coruña, Spain 13          School of Global Studies, University of Gothenburg, Gothenburg, Sweden 14          Centro de Investigación y de Estudios Avanzados (CINVESTAV), IPN, Unidad Mérida, Mexico               <h2>Method               </h2> Case studies were selected in eight countries by national SSF experts, based on specific criteria and research priorities. Case studies were not selected to represent the full diversity of SSF globally or even nationally. Instead, they were chosen to capture a range of fisheries that could showcase different contributions to SDGs. SSF were defined based on various characteristics, such as resources harvested, gear used, and location of the fishery.                <h3>Geographical Coverage </h3> 60 small-scale fisheries located in seven countries are documented in the data: <ul> <li>Colombia (4 case studies) – Pacifico: La Guajira, San Andrés y Providencia; Caribe: Chocó, Cauca, Valle del Cauca, Nariño.</li> <li>Ecuador (3) – Region: Esmeraldas, Manabi, Guayas, El Oro.</li> <li>France (2) – Region: Bretagne, Occitanie.</li> <li>Kenya (22) – County: Kilifi, Kwale, Lamu, Mombasa, Tana River.</li> <li>Madagascar (20) – Region: Analanjirofo, Anosy, Atsimo Andrefana, Boeny, Diana, Menabe, Vatovavy Fitovinany.</li> <li>Mexico (2) – State: Baja California Sur, Campeche, Yucatan.</li> <li>Nigeria (6) – State: Bayelsa, Cross River, Lagos, Ondo, Ogun. </li> <li>Spain (1) – State: Galicia.              </li> </ul> <h3>Data Collection </h3> Data collection took place from November 30, 2022, to July 3, 2023, spanning approximately seven months. The data presented serve as a snapshot of the conditions within a specific small-scale fishery during the assessment period. To consider the evolution of trends such as exports, economic growth, and income, we considered any relevant variables over the past decade. Data collection approaches varied depending on the context, and data collectors received training to ensure survey consistency. We used primary data sources such as interviews, observations, and measurements whenever possible. In cases where resources were limited, we preferred secondary sources such as existing datasets and literature. Our methods were standardized, but data collectors could adjust them based on their resources. We primarily used direct observation, focus groups, and interviews to collect data. Scoring in interviews and focus groups was done directly or through group analysis by interviewers. Disagreements were resolved through additional interviews or group discussions, with secondary data used if needed. Please refer to the methods in : Bitoun et al., (2024). A methodological framework for capturing marine small-scale fisheries’ contributions to the sustainable development goals. Sustainability Science, 19(4), 1119–1137. https://doi.org/10.1007/s11625-024-01470-0.             <h3>Ethics    </h3> Participants had the option to join of their own accord, were fully briefed on the research goals, and were given the opportunity to review interview guidelines before proceeding. Depending on the circumstances, interviews could last 45 minutes to 4.5 hours. Participants were guaranteed confidentiality and anonymity in the handling and reporting of their data.               <h3>Suggested citation</h3> Léopold, M., Bitoun, R., & Devillers, R. (2023). Qualitative Data on 61 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain) (Version 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.16077739 <h2>Data Files</h2> The dataset includes the following: <ul> <li>The raw dataset (.xls format).</li> <li>A data dictionary describing and defining each dataset column (.xls format).</li> </ul>

opencc-by-4.0Dec 2019View details →

Information Based Behavioural Intervention Study (Bologna case)

iSCAPE Dataset Reference No. = DS_PD_011, DS_MA_003, DS_PD_016 Following datasets are acquired and generated during the implementation of behavioural intervention study in Bologna: <ol> <li>Participant's detailed activity-travel diary </li> <li>Pollutant concentrations of a study area</li> <li>Introductory and Followup questionnaire responses </li> </ol>

opencc-by-4.0Dec 2019View details →

Information Based Behavioural Intervention Study (Hasselt case)

iSCAPE Dataset Ref. No. = DS_PD_010, DS_MA_002, DS_PD_015 Following datasets are acquired and generated during the implementation of behavioural intervention study in Hasselt: <ol> <li>Participant's detailed activity-travel diary </li> <li>Pollutant concentration maps</li> <li>Introductory and Followup questionnaire responses </li> </ol>

VICINITY IoT Use Case Data Sets

This repository contains Internet-of-Things (IoT) use case data sets from the VICINITY project. The use cases are described under https://vicinity2020.eu, where also the contributing companies are described in more detail.  The use cases include:  <ul> <li>eHealth use case data by GNOMON/CERTH (anonymized)</li> <li>Smart parking, building control use case data by HITS, TINYM</li> <li>Energy management data set by ENERC</li> <li>Several other, smaller use cases with less data from open call winners. </li> </ul> The data sets have also been the basis for the publications available for download under the same URL; the folder AAU gives additionally the data from AAU's publications.

COVID-19 case fatality derived age-adjusted risk of death

This is an old version of the plot. For the newer version, please visit <a href="https://zenodo.org/record/3829175">https://zenodo.org/record/3829175</a> doi:<a href="https://doi.org/10.5281/zenodo.3787930">10.5281/zenodo.3787930</a> This is the plot of the COVID-19 risk of death adjusted by age based on the case fatality data from the Kaggle project Data Science for COVID-19 in South Korea (DS4C) hosted at https://www.kaggle.com/kimjihoo/coronavirusdataset The plot was created with the Python & C library published on April 3, 2020, (release 2.0 of April 21, 2020) on GitHub at https://github.com/yuryatin/covid19_age_adjusted_mortality

COVID-19 case-fatality-derived age-adjusted risk of death

This is an old version of the plot. For the newer version, please visit <a href="https://zenodo.org/record/3787931">https://zenodo.org/record/3787931</a> doi:10.5281/zenodo.3787931 This is the plot of the COVID-19 risk of death adjusted by age derived from the case fatality data from the Kaggle project Data Science for COVID-19 in South Korea (DS4C) hosted at https://www.kaggle.com/kimjihoo/coronavirusdataset The plot was created with the Python & C library initially published on April 3, 2020, (with the release 2.0 of April 21, 2020) on GitHub at https://github.com/yuryatin/covid19_age_adjusted_mortality

Techno-economic sustainability analysis methodology for conversion routes of renewable feedstock resources to bio-based products – case studies

The dataset provides a set of sustainability principles, criteria and indicators for the evaluation of the conversion routes stage of a bio-based product.  The selected case studies on the employment of alternative feedstocks and production of the bio-based products are implemented in order to evaluate the proposed methodology. Mass and energy balances for all case studies, estimated techno-economic metrics, cost of externalities and risk assessment results are provided

COVID-19 age-adjusted risk of death derived from case fatality

This is an old version of the plot. For the newer version, please visit https://zenodo.org/record/3787931 doi:10.5281/zenodo.3787931 This is a plot of the risk of death in COVID-19 adjusted by the age and derived from the case fatality data kindly collected by the team of data scientists and data engineers in the Kaggle project Data Science for COVID-19 in South Korea (DS4C), which is hosted at https://www.kaggle.com/kimjihoo/coronavirusdataset. The model for the plot was created with the release 2.0 of the Python & C library initially published on April 3, 2020 on GitHub at https://github.com/yuryatin/covid19_age_adjusted_mortality. Please, visit https://github.com/yuryatin/covid19_age_adjusted_mortality for more detailed description of the model and the source code.

A case study on the origin of near-Earth plasma

Summary of data files. This is the dataset for a paper "A case study on the origin of near-Earth Plasma" submitted to JGR-space by Glocer et al The paper conducts multifluid MHD simulations of the magnetosphere by the BATSRUS code, with solar wind input, ionospheric outflow by the PWOM code, and ionospheric potential solver, and inner magnetospehre by the CIMI code. Solutions with and without plasmasphere are considered. All plots in the paper are made with spacepy (specific fork: https://github.com/aglocer/spacepy) or with tecplot The data is organized as follows Top directory: imf.dat: Has the solar wind input as a simple time series. Can be read and plotted with the "ImfInput" tool in spacepy tared Directories  with "PS" and "noPS" tags have simulation output with and without plasmasphere. Each has three subdirectories described as follows: GM: Has the "Global Magnetosphere" output from BATSRUS for the images shown. y=0*out are cuts in the y=0 GSM plane and the *log file is the log output containing Dst. Both can be plotted with spacepy pybats. The 3d files are used in a few images and are tecplot binary files and read and plotted with tecplot. PW: Has the "Polar Wind" output from the PWOM code for images shown. The *out files are time dependent binary output for each field line. They can be read and plotted with the pybats.pwom  tool in spacepy. North and South indicte northern and southern hemisphere respectively IE: Has the "Ionosphere Electrodynamics" output from the potential solver for plots shown. the *log files have the CPCP data as a function of time. They can be read and plotted with pybats in spacepy IM: Has the "Inner Magnetosphere" output from the CIMI code. The CIMIeq.out file has time dependent snapshots of the solution on the min B surface for plots shown. The *log files have the total energy as a function of time for each species (among other variables). Both files are read and plotted with pybats and pybats.cimi code in spacepy.

opencc-by-4.0May 2020View details →

COVID-19 case fatality-derived risk of death adjusted by age and gender

This figure plots the risk of death in COVID-19 adjusted by the age and gender, which was modeled with the case fatality data kindly collected by the team of data scientists and data engineers in the Kaggle project Data Science for COVID-19 in South Korea (DS4C), which is hosted at https://www.kaggle.com/kimjihoo/coronavirusdataset. The model for the plot was built with the release 2.2 of the open-sourced Python & C library for macOS and Linux initially published under GPLv3 license on April 3, 2020, on GitHub at https://github.com/yuryatin/covid19_age_adjusted_mortality. Please, visit <a href="https://github.com/yuryatin/covid19_age_adjusted_mortality">https://github.com/yuryatin/covid19_age_adjusted_mortality</a> for more detailed description of the model and for the source code.

opencc-by-4.0May 2020View details →

CodiEsp corpus: gold standard Spanish clinical cases coded in ICD10 (CIE10) - eHealth CLEF2020

Introduction These are the train, development and test sets of the CodiEsp corpus. Train, development and test have gold standard annotations. In addition, the unannotated background set is also distributed. All documents are released in the context of the CodiEsp track for CLEF ehealth 2020 (<a href="http://temu.bsc.es/codiesp/">http://temu.bsc.es/codiesp/</a>). The CodiEsp corpus contains manually coded clinical cases. All documents are in Spanish language and CIE10 is the coding terminology (it is the Spanish version of ICD10-CM and ICD10-PCS). The CodiEsp corpus has been randomly sampled into three subsets: the train, the development, and the test set. The train set contains 500 clinical cases, and the development and test set 250 clinical cases each. CodiEsp participants must submit predictions for the test and background set, but they will only be evaluated on the test set.   Please cite if you use this dataset: Antonio Miranda-Escalada, Aitor Gonzalez-Agirre, Jordi Armengol-Estapé and Martin Krallinger. Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of CLEF eHealth 2020. In CLEF (Working Notes). 2020 <pre><code>@inproceedings{miranda2020overview, title={Overview of automatic clinical coding: annotations, guidelines, and solutions for non-english clinical cases at codiesp track of CLEF eHealth 2020}, author={Miranda-Escalada, Antonio and Gonzalez-Agirre, Aitor and Armengol-Estap{\'e}, Jordi and Krallinger, Martin}, booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, year={2020} }</code></pre>   Annotation quality Inter-annotator agreement: 88.6% for diagnosis coding, 88.9% for procedure coding and 80.5% for the textual reference annotation. For more information, see the <a href="http://ceur-ws.org/Vol-2696/paper_263.pdf">paper</a>. Zip structure Four folders: train, dev, test and background. Each one of them contains the files for the train, development, test and background corpora, respectively. <ul> <li>train, dev and test folders have: <ul> <li>3 tab-separated files with the annotation information relevant for each of the 3 sub-tracks of CodiEsp. </li> <li>A subfolder named text_files with the plain text files of the clinical cases.</li> <li>A subfolder named text_files_en with the plain text files machine-translated to English. Due to the translation process, the text files are sentence-splitted.</li> </ul> </li> <li>The background folder has only text_files and text_files_en subfolders with the plain text files.</li> </ul> Format The CodiEsp corpus is distributed in plain text in UTF8 encoding, where each clinical case is stored as a single file whose name is the clinical case identifier. Annotations are released in a tab-separated file. Since the CodiEsp track has 3 sub-tracks, every set of documents (train and test) has 3 tab-separated files associated with it.  For the sub-tracks CodiEsp-D and CodiEsp-P, the file has the following fields: <pre>articleID ICD10-code </pre> Tab-separated files for the sub-track CodiEsp-X contain extra fields that provide the text-reference and its position: <pre>articleID label ICD10-code text-reference reference-position</pre> Corpus summary statistics The final collection of 1000 clinical cases that make up the corpus had a total of 16504 sentences, with an average of 16.5 sentences per clinical case. It contains a total of 396,988 words, with an average of 396.2 words per clinical case.   Resources: <ul> <li><a href="https://temu.bsc.es/codiesp/">Web</a></li> <li><a href="http://ceur-ws.org/Vol-2696/paper_263.pdf">Citation</a>: Antonio Miranda-Escalada, Aitor Gonzalez-Agirre, Jordi Armengol-Estapé and Martin Krallinger. Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of CLEF eHealth 2020. In CLEF (Working Notes). 2020</li> <li><a href="https://doi.org/10.5281/zenodo.3859869">Silver Standard corpus</a></li> <li><a href="https://doi.org/10.5281/zenodo.3730566">Annotation guidelines</a></li> <li><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhA0crlSVCYMPqMUWd4mXc4x">YouTube presentations</a></li> <li><a href="https://temu.bsc.es/codiesp/index.php/participants-systems/">Participant codes</a></li> </ul>   For more information, visit the track webpage: http://temu.bsc.es/codiesp/ or email us at encargo-pln-life@bsc.es   Copyright (c) 2019 Secretaría de Estado para el Avance Digital

Number of cases of coronavirus disease (COVID-19) in Ireland

Datasets in this publication report the number of diagnoses with coronavirus disease (COVID-19) as reported by the Department of Health in Ireland. This includes new cases diagnosed per day and cumulative cases, hospitalisations, ICU admissions, deaths, number of healthcare workers, number of clusters, gender of cases, age groups of cases, mode of transmission, age groups of those hospitalised, and cases per county. To aid standardisation of age groups and cases per county, the population estimates by age group for 2019 and the actual county population in the 2016 Census from Ireland's Central Statistics Office are also included as separate datasets, to allow expression of cases per million population. These are  <ol> <li>doh_covid_ie_cases_analysis.csv, where data from Ireland's Health Protection Surveillance Centre is included up to midnight on each included date (currently up to 16-Jun-2020). </li> <li>age_population_cso_2019.csv</li> <li>counties_population_cso_2016.csv</li> </ol> age_population_cso_2019.csv has been updated to include separate population estimates for those aged 65-74 years, 75-84 years, and 85 years and over. This is in response to the HSPC releasing case and hospitalisation data for these groups rather than a combined 65 years and over group. counties_population_cso_2016.csv has been updated to remove trailing spaces in the 'county' column. doh_covid_ie_cases_analysis.csv is regularly updated at <a href="https://github.com/frankmoriarty/covid_ie/blob/master/doh_covid_ie_cases_analysis.csv">https://github.com/frankmoriarty/covid_ie/blob/master/doh_covid_ie_cases_analysis.csv</a>