Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
76,402,788
datasets available to search
ShareScore release 0.7.1
Dataset results
76,402,788 results
Algoritmo de clasificación de expresiones de odio por intensidades en español
<p>Algoritmo de clasificación de expresiones de odio en mensajes en español. Este algoritmo fue desarrollado en el marco del proyecto Hatemedia (PID2020-114584GB-I00), financiado por MCIN/AEI /10.13039/501100011033, con la colaboración de Possible Inc.</p> <p>Por favor lea el documento README IN SPANISH, en el que se expone todos los pasos a seguir para el uso del algoritmo desarrollado en el marco del proyecto Hatemedia (PID2020-114584GB-I00), financiado por MCIN/ AEI /10.13039/501100011033</p> <p>El algoritmo permite la clasificación de expresiones de odio, de acuerdo a 4 tipos de intensidad de odio:</p> <ul> <li>Intensidad 1 – Odio asociado a mensajes incívico</li> <li>Intensidad 2 – Odio asociado a mensajes mal intensionados o con expresiones abusivas</li> <li>Intensidad 3 – Odio asociado a insultos</li> <li>Intensidad 4 – Odio asociado a amenazas veladas o explícitas</li> </ul> <p>La estructura de carpetas con la documentación de Github es la presentada a continuación:</p> <div> <pre><code> 02 Documentación Github └── 01_Intensidades ├── DOCUMENTACIÓN GITHUB(1).docx ├── ejemplo.py ├── Modelo_intensidades.ipynb ├── obtener_caracteristicas.py └── recursos-20240304T124712Z-001.zip </code></pre> <div> </div> </div> <p>Se detalla a continuación el contenido de cada fichero:</p> <ul> <li> <p>DOCUMENTACIÓN GITHUB.docx: Informe en el que se presenta el uso de los scripts ejemplo (1).py y obtener_caracteristicas (1).py para emplear los modelos.</p> </li> <li> <p>ejemplo (1).py: Script Python que muestra el uso de los modelos para realizar predicciones.</p> </li> <li> <p>Modelo_binario_(1) (1).ipnyb: Notebook con el código utilizado para el entrenamiento de los distintos modelos.</p> </li> <li> <p>Obtener_caracteristicas (1).py: Script Python con las funciones de preprocesado utilizadas previamente al uso de los modelos para predecir las entradas de un dataframe.</p> </li> <li> <p>Recursos-20231027T110710Z-001 (1).zip: La carpeta recursos contiene 3 .csv utilizados en la extracción de características.</p> </li> </ul> <p>El dataset que se ha utilizado para el entrenamiento de los modelos es dataset_completo_caracteristicas_ampliadas_todas_combinaciones_v1_textoProcesado.csv (<a href="https://acortar.link/diSV7o" rel="nofollow">https://acortar.link/diSV7o</a>)</p> <p>El Algoritmo fue desarrollado a partir de las pruebas de los modelos aplicados que se muestran en la carpeta MODELOS. En esta carpeta se encuentran todos los resultados de los modelos utilizados durante el proceso de desarrollo de este algoritmo, con los respectivos porcentajes de entrenamiento y prueba.</p> <p>El procedimiento seguido para entrenar los modelos queda reflejado en el Informe técnico Desarrollo del algoritmo de clasificación del odio por intensidades en medios digitales españoles en X (Twitter), Facebook y portales web (<a href="https://doi.org/10.6084/m9.figshare.25884262.v2" rel="nofollow">https://doi.org/10.6084/m9.figshare.25884262.v2</a>).</p> <p>Autores: </p> <ul> <li>Xiomara Blanco</li> <li>Almudena Ruiz</li> <li>Daniel Pérez Palau</li> <li>Oscar De Gregorio</li> <li>Juan José Cubillas</li> <li>Elias Said-Hung</li> <li>Julio Montero-Díaz</li> </ul> <p>Financiado por: Agencia Estatal de Investigación – Ministerio de Ciencia e Innovación</p> <p>Con el apoyo de:</p> <ul> <li>POSSIBLE S.L.</li> </ul> <p>Más información:</p> <ul> <li><a href="https://www.hatemedia.es/" rel="nofollow">https://www.hatemedia.es/</a> o contactar con: <a href="mailto:elias.said@unir.net">elias.said@unir.net</a></li> <li>Este algoritmo está relacionado con el algoritmo de clasificación de odio/no odio, desarrollado también por los autores: <a href="https://github.com/esaidh266/Algorithm-for-classifying-hate-expressions-in-Spanish">https://github.com/esaidh266/Algorithm-for-classifying-hate-expressions-in-Spanish</a></li> </ul>
Zenodo-communities for EU projects
<div> <div>Dataset of Zenodo communities associated with EU-funded projects. Only communities linked to a single EU project under either Horizon Europe, Horizon 2020, or Framework Programme 7 are included. Earlier Framework Programmes are not included, as Framework Programme 6 ended in 2006, and Zenodo was launched on May 8, 2013. The dataset was extracted from Zenodo on May 22, 2024, and contains data as of that date. It includes 2,724 communities linked to an EU-funded project.</div> </div>
Daten der Data Literacy Bedarfserhebung für die historisch arbeitenden Disziplinen (Erhebungszeitraum: August-Oktober 2023)
<p>Bei dieser Publikation handelt es sich um Daten aus der NFDI4Memory Data Literacy Bedarfserhebung, die von August bis Oktober 2023 durchgeführt wurde. Der Datensatz beinhaltet die Rohdaten, wie sie aus dem Fragebogentool SoSciSurvey heruntergeladen wurden und die aufbereiteten Daten, die die Grundlage für die Auswertung waren.</p> <p><strong>Rohdaten aus SoSciSurvey:</strong></p> <p>.xlsx-Format: </p> <ul> <li><a href="../api/records/12166939/draft/files/codebook_4memory-data-literacy_2024-06-19_11-28.xlsx/content" target="_blank" rel="noopener noreferrer">codebook_4memory-data-literacy_2024-06-19_11-28.xlsx</a></li> <li><a href="../api/records/12166939/draft/files/data_4memory-data-literacy_2024-06-19_11-28.xlsx/content" target="_blank" rel="noopener noreferrer">data_4memory-data-literacy_2024-06-19_11-28.xlsx</a></li> </ul> <p>.csv-Format:</p> <ul> <li><a href="../api/records/12166939/draft/files/data_4memory-data-literacy_2024-06-19_11-30.csv/content" target="_blank" rel="noopener noreferrer">data_4memory-data-literacy_2024-06-19_11-30.csv</a></li> <li><a href="../api/records/12166939/draft/files/variables_4memory-data-literacy_2024-06-19_11-30.csv/content" target="_blank" rel="noopener noreferrer">variables_4memory-data-literacy_2024-06-19_11-30.csv</a></li> <li><a href="../api/records/12166939/draft/files/rdata_4memory-data-literacy_2024-06-19_11-33.csv/content" target="_blank" rel="noopener">rdata_4memory-data-literacy_2024-06-19_11-33.csv</a></li> <li><a href="../api/records/12166939/draft/files/sdata_4memory-data-literacy_2024-06-19_11-32.csv/content" target="_blank" rel="noopener">sdata_4memory-data-literacy_2024-06-19_11-32.csv</a></li> <li><a href="../api/records/12166939/draft/files/values_4memory-data-literacy_2024-06-19_11-30.csv/content" target="_blank" rel="noopener noreferrer">values_4memory-data-literacy_2024-06-19_11-30.csv</a></li> <li><a href="../api/records/12166939/draft/files/Codebuch.csv/content" target="_blank" rel="noopener noreferrer">Codebuch.csv</a></li> <li><a href="../api/records/12166939/draft/files/Ausgangsdatensatz.csv/content" target="_blank" rel="noopener noreferrer">Ausgangsdatensatz.csv</a></li> </ul> <p>.sql-Format:</p> <ul> <li><a href="../api/records/12166939/draft/files/data_4memory-data-literacy_2024-06-19_11-33.sql/content" target="_blank" rel="noopener noreferrer">data_4memory-data-literacy_2024-06-19_11-33.sql</a></li> </ul> <p>.sps-Fromat:</p> <ul> <li><span><a href="../api/records/12200702/draft/files/spss_4memory-data-literacy_2024-06-19_11-31.sps/content" target="_blank" rel="noopener noreferrer">spss_4memory-data-literacy_2024-06-19_11-31.sps</a></span></li> </ul> <p><span>.do-Format:</span></p> <div> <ul> <li><a href="../api/records/12200702/draft/files/import_4memory-data-literacy_2024-06-19_11-32.do/content" target="_blank" rel="noopener noreferrer">import_4memory-data-literacy_2024-06-19_11-32.do</a></li> </ul> <p>.r-Format:</p> <div> <ul> <li><a href="../api/records/12200702/draft/files/import_4memory-data-literacy_2024-06-19_11-33.r/content" target="_blank" rel="noopener noreferrer">import_4memory-data-literacy_2024-06-19_11-33.r</a></li> </ul> </div> </div> <p><strong>Aufbereitete Daten:</strong></p> <p>.xlsx-Format:</p> <ul> <li><span><a href="../api/records/12200702/draft/files/2024-06-06-aufbereitete_Daten.xlsx/content" target="_blank" rel="noopener noreferrer">2024-06-06-aufbereitete_Daten.xlsx</a></span> (enthalten sind die Tabellenblätter Ausgangsdatensatz (unbearbeitet), Codebuch (unbearbeitet), überarbeiteter Datensatz und Zusatztabelle Fachbereich</li> </ul> <p>.csv-Format:</p> <ul> <li> <div><a href="../api/records/12166939/draft/files/Dokumentation.csv/content" target="_blank" rel="noopener noreferrer">Dokumentation.csv</a></div> </li> <li><a href="../api/records/12166939/draft/files/Zusatztabelle_Fachbereich.csv/content" target="_blank" rel="noopener noreferrer">Zusatztabelle_Fachbereich.csv</a></li> <li><span><a href="../api/records/12200702/draft/files/%C3%BCberarbeiteter%20Datensatz.csv/content" target="_blank" rel="noopener noreferrer">überarbeiteter Datensatz.csv</a></span></li> </ul>
BIOSCAN-5M
<h2>Overview</h2> <p>As part of an ongoing worldwide effort to comprehend and monitor insect biodiversity, we present the <a href="https://biodiversitygenomics.net/5M-insects/">BIOSCAN-5M</a> Insect dataset to the machine learning community. BIOSCAN-5M is a comprehensive dataset containing multi-modal information for over 5 million insect specimens, and it significantly expands existing image-based biological datasets by including taxonomic labels, raw nucleotide barcode sequences, assigned barcode index numbers, geographical information, and specimen size.</p> <p>Every record has <strong>both image and DNA</strong> data. Each record of the BIOSCAN-5M dataset contains six primary attributes:</p> <ul> <li>RGB image</li> <li>DNA barcode sequence</li> <li>Barcode Index Number (BIN)</li> <li>Biological taxonomic classification</li> <li>Geographical information</li> <li>Specimen size</li> </ul>
Bases de datos de encuestas docentes no universitarios en España 2021-2022, sobre diversidad, interculturalidad, estereotipos y aculturación
<p>Bases de datos de encuestas docentes no universitarios en España 2021-2022, recabado en el marco del proyecto Diverprof (https://diverprof.es/). Se incluye informe de variables iniciales y ad-hoc elaboradas para analizar las actitudes hacia la aculturación y la diversidad cultural en el profesorado a nivel de educación obligatoria en España.</p> <p>Proyecto (PP-2022-06), financiado por la Universidad Internacional de La Rioja.</p>
PsPM-REW2: SCR, ECG, respiration, and eyetracking measurements in a Pavlovian appetitive conditioning task with juice delivery with acquisition and recall test after one week
<p>This dataset contains skin conductance responses (SCR), electrocardyogram (ECG), respiration, pupil size (PSR), and gaze coordinates measurements for 34 healthy unmedicated participants (20 females and 14 males aged 24.9 +/- 4.1) participating in a Pavlovian differential cued rewarding conditioning experiment with 2 sessions. <br>CSs were isoluminant colored triangles presented on the screen center with grey background.<br>US was a sip of the favourite juice selected individually. SOA between the CS onset and US was 5.5 s. Participants underwent the conditioning task with CSs (CS+ 50% reinforced) and US delivery in session 1 with 2 blocks, and were tested in a retention/extinction task one week later in session 2 with 2 blocks. No US was delivered during session 2. The ITI was jittered on each trial uniformly at random between 9 to 16 s. The blocks in each session were recorded on the same day with self-paced breaks.</p>
PsPM-REW1: SCR, ECG, respiration, and eyetracking measurements in a Pavlovian appetitive conditioning task with juice delivery with acquisition and recall test after one week
<p>This dataset contains skin conductance responses (SCR), electrocardyogram (ECG), respiration, pupil size (PSR), and gaze coordinates measurements for 37 healthy unmedicated participants (26 females and 11 males aged 24.2 +/- 4.2) participating in a Pavlovian differential cued rewarding conditioning experiment with 2 sessions. <br>CSs were isoluminant colored triangles presented on the screen center with grey background.<br>US was a sip of the favourite juice selected individually. SOA between the CS onset and US was 5.5 s. Participants underwent the conditioning task with CSs (CS+ 50% reinforced) and US delivery in session 1 with 2 blocks, and were tested in a retention/extinction task one week later in session 2 with 2 blocks. No US was delivered during session 2. The ITI was jittered on each trial uniformly at random between 9 to 16 s. The blocks in each session were recorded on the same day with self-paced breaks.</p>
Librerías de odio según intensidad y tipos en medios informativos digitales en España
<p>Librerías de odio según intensidad y tipos en los medios informativos digitales en España, resultado del proyecto "Hatemedia" (proyecto PID2020-114584GB-I00), financiado por MCIN/ AEI /10.13039/501100011033.</p> <p>A partir de la <a href="https://doi.org/10.6084/m9.figshare.26085700.v1" target="_blank" rel="noopener"><strong>BD usada para el entrenamiento</strong></a> de los diferentes modelos de algoritmos de clasificación desarrollados en el proyecto Hatemedia, se extranjeron 6.273 lemas simples y compuestos, asociados a los mensajes con expresiones de odio identificados por cada una de las intensidades y tipos de odio estudiados en este proyecto. Las librerías de odio por intensidad y tipo de odio, están conformadas por un total de: 2.706 y 3.567 lemas simples y compuestos, respectivamente.</p> <p><strong>Por intensidad de odio:</strong></p> <ul> <li>Intensidad 1 – Odio asociado a mensajes incívidos: 1.000 lemas simples y compuestos.</li> <li>Intensidad 2 – Odio asociado a mensajes mal intensionados o con expresiones abusivas: 364 lemas simples y compuestos.</li> <li>Intensidad 3 – odio asociado a insultos: 1.110 lemas simples y compuestos.</li> <li>Intensidad 4 – Odio asociado a amenazas veladas o explícitas: 232 lemas simples y compuestos.</li> </ul> <p><strong>Por tipo de odio:</strong></p> <ul> <li>Odio general: 1.001 lemas simples y compuestos.</li> <li>Odio misogino: 505 lemas simples y compuestos.</li> <li>Odio politico: 1.235 lemas simples y compuestos.</li> <li>Odio sexual: 160 lemas simples y compuestos.</li> <li>Odio xenófobo: 666 lemas simples y compuestos.</li> </ul> <p>Una vez recabado estos lemas, se llevó a cabo el siguiente proceso:</p> <ul> <li>ETIQUETADO DE EXPRESIONES Y EXTRACCIÓN DE LEMAS. Del total de mensajes identificados se eliminaron stop-words, se identificaron datos anómalos (que no pertenecían a un idioma conocido o eran diminutivos de éste), separándolos posteriormente en función de su intensidad y su tipo de odio. A partir de esta separación, se identificaron tanto los lemas simples como compuestos de forma independiente para cada intensidad y tipo de odio.</li> <li>IDENTIFICACIÓN DE DUPLICADOS: En la primera fase se realizaron dos listados, el primero de lemas simples y el segundo de lemas compuestos. El primer paso fue filtrar estas dos listas para identificar lemas repetidos, obteniendo estas dos bibliotecas donde cada lema aparece una sola vez.</li> <li>INTEGRACIÓN BBDD: A continuación, en la tercera fase, se procedió a unir ambas bibliotecas para construir una biblioteca final que integrara todos los lemas, tanto simples como compuestos. Finalmente, se realizó un filtrado final para asegurar que no se repitan los lemas.</li> </ul> <p>Una vez hecho el proceso descrito, se revisó manualmente cada uno de los lemas identificados, con el fin de eliminar aquellos que no aludían a expresiones de odio, por motivo de contexto o significado del término, quedando finalmente la siguiente relación de lemas, según intensidad y tipo de odio. Las librerías de odio por intensidad y tipo de odio, están conformadas por un total de: 1.140 y 1.673 lemas simples y compuestos, respectivamente.</p> <p><strong>Por intensidad de odio:</strong></p> <ul> <li>Intensidad 1 – Odio asociado a mensajes incívidos: 401 lemas simples y compuestos.</li> <li>Intensidad 2 – Odio asociado a mensajes mal intensionados o con expresiones abusivas: 99 lemas simples y compuestos.</li> <li>Intensidad 3 – odio asociado a insultos: 542 lemas simples y compuestos.</li> <li>Intensidad 4 – Odio asociado a amenazas veladas o explícitas: 98 lemas simples y compuestos.</li> </ul> <p><strong>Por tipo de odio:</strong></p> <ul> <li>Odio general: 463 lemas simples y compuestos.</li> <li>Odio misogino: 239 lemas simples y compuestos.</li> <li>Odio politico: 579 lemas simples y compuestos.</li> <li>Odio sexual: 74 lemas simples y compuestos.</li> <li>Odio xenófobo: 319 lemas simples y compuestos.</li> </ul>
Populist attitudes and other socio-political views and psychological traits of the UK population
<p>This dataset is the result of an original survey designed by an interdisciplinary team of researchers from National Universtity of Distance Education (UNED), University of Zaragoza, University of Córdoba and University of Valencia.</p> <p>The goal of the survey was to better understand the relationship between populist attitudes and relevant socio-political and psychology items and indexes. The UK was selected as case study given the lack of similar studies in this country and the relevance of the data to better understand the political context that had been heavily impacted by the Brexit referendum and proces of separation from the European Union.</p> <p>The survey was theoretically informed and included among others:</p> <ul> <li>Populism, Elitism and Pluralism items by Akkerman et al. (2014) (14 items)</li> <li>New items design for a new Multidimensional Scale of Populist Attitudes (37 items) (Olivas Osuna 2021; Olivas Osuna et al. 2024; Olivas Osuna et al. forthcoming)</li> <li>Conspiracy Beliefs items (8 items) (Bruder et al. 2013; Brotherton et al. 2013)</li> <li>Social alienation index (6 items) (Bélanger et al. 2019)</li> <li>Justification of violence index (6 items) (Bélanger et al. 2019)</li> <li>Radicalised network (3 items)(Moyano 2011)</li> <li>Meaning in life (presence and search)(4 items)(Steger et al. 2006)</li> <li>Bordering attitudes (6 items)(Olivas Osuna et al. forthcoming)</li> <li>Endorsement for political parties</li> <li>Items reflecting level of agreement with the main slogans and arguments used by British Eurosceptics (11 items)</li> <li>Items on satisfaction with democracy and importance of democracy and with illiberal views (ESS)</li> <li>Left-right ideological self-placement</li> <li>Socio-demographic variables (age, religion, education, etc.)</li> </ul> <p>Fieldwork was conducted between 17 November and 4 December 2020. Participants were recruited following socio-demographic representativity criteria via the online platform Prolific. Survey were collected via Google Forms (Survey title: <em>Political and social views in the UK</em>).</p> <p>Files uploaded include:</p> <ul> <li>Total responses received (N=849) (.xlsx file) </li> <li>Responses analysed once participants failing attention checks were eliminated from the sample (N=748) (.csv file)</li> <li>Survery questionnair (.pdf file)</li> </ul>
Multiscale Land Surface Parameters for Europe
<p><strong>General Description</strong></p> <p>The <em>Multiscale Land Surface Parameters for Europe</em> dataset is derived from <a href="../records/7676373">Global Ensemble DTM</a>. Data is computed using GRASS GIS and SAGA GIS. Original DTM data is in projection EPSG:4326, and reprojects to Equi7 (EPSG:27704), computes the parameters, and eventually reprojects to EPSG:3035. High resolution layers (120m downward in geo-hydrological parameters and 60m downward in others) are computed in tiles. In order to eliminate boundary effects and reprojection resampling, Regional land surface parameters have 3400 pixels overlap and local land surface 100 pixels overlap. Below is the list of land-surface parameters.</p> <ul> <li><strong>Local land-surface parameter</strong></li> </ul> <p><strong>slope in degree (slope): </strong>steepness at each cell</p> <p><strong>hillshade:</strong> visualizing of terrain determined by a light source and the slope and aspect of the elevation surface</p> <p><strong>easterness: </strong>cosine of aspect</p> <p><strong>northerness:</strong> sine of aspect</p> <p><strong>minimum curvature (minic): </strong>valleys in negative value and local convex landform in positive value</p> <p><strong>maximum curvature (maxic):</strong> ridges in positive values and local concave landform in negative value</p> <p><strong>positive openness (pos.openness): </strong>the "dominance" of an elevated location over its surroundings</p> <p><strong>negative openness (neg.openness):</strong> the "enclosure" of a lower location by elevated surroundings</p> <ul> <li><strong>Regional land-surface parameter</strong></li> </ul> <p><strong>sink removal DTM (nosink)</strong></p> <p><strong>flow accumulation (flow.accum): </strong>depiction of the flow convergence upslope pixels to downslope pixels</p> <p><strong>geomorphon classes (geomorphon):</strong> 9 terrain forms based on the line-of-sight neighbor pixels</p> <p><strong>specific catchment area (spec.catch.area.factor):</strong> the total catchment area divided by flow width</p> <p><strong>topographic wetness index (twi):</strong> a parameter describing the tendency of a cell to accumulate water</p> <p><strong>slope length and steepness factor (ls.factor):</strong> the S-factor measures the effect of slope steepness, and the L-factor defines the impact of slope length.</p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> January 2000 – December 2022</li> <li><strong>Type of data:</strong> Land surface parameters of geomorphometry</li> <li><strong>How the data was collected or derived:</strong> Derived from <a href="../records/7676373">Global Ensemble DTM</a> in 30m using GRASS GIS and SAGA GIS running in a local HPC.</li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900000 899000 7401000 5501000)</li> <li><strong>Spatial resolution:</strong> 60m, 120m, 240m, 480m, 960m</li> <li><strong>Image size: </strong>108,350 x 76,700; 54,175 x 38,350; 54,175 x 38,350; 13,544 x 9,588; 6,772 x 4,794<strong> </strong></li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">https://github.com/AI4SoilHealth/SoilHealthDataCube/issues</a></p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> slope = slope in degree</li> <li><strong>variable procedure combination:</strong> edtm = Ensemble digital terrain model</li> <li><strong>Position in the probability distribution / variable type:</strong> m = measurement</li> <li><strong>Spatial support:</strong> 60m, 120m, 240m, 480m, 960m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = Europe</li> <li><strong>EPSG code:</strong> epsg.3035 = EPSG:3035</li> <li><strong>Version code:</strong> v20240528 = 2024-05-28 (creation date)</li> </ol>
EU-TRHeaDS Conjoint Dataset
<p>The EU-TRHeaDS Conjoint Dataset is a set of 20,920 observations that was gathered, organised and edited in the framework of the research project ‘EU Citizens’ Transnational Rights and Health-related Deservingness at the Street-level - EU-TRHeaDS’ (PI: Roberta Perna), which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101022244.</p> <p>One of EU-TRHeaDS' aims is to investigate which criteria ‘activate’ the category of healthcare (un)deservingness in the context of intra-EU migration among the general public in two EU Member States (Belgium and Spain), and the extent to which these preferences turn into patterns of systematic penalisation towards specific EU nationality groups. It does so by carrying out a conjoint experimental study nested in an online survey run in parallel in Belgium and Spain with a representative sample of the population on the dimensions of gender, age (18 years old), level of education achieved and geographical region of residence. During the four tasks of the experiment, respondents were asked who they would prioritise to access publicly-funded healthcare out of two fictitious patients who differed in four attributes, all randomly assigned: 1) nationality; 2) migration trajectory; 3) responsibility over ill health, and 4) employment status.</p> <p>As a subset of a larger survey on intra-EU mobility and access to healthcare rights, the EU-TRHeaDS Conjoint Dataset specifically includes the socio-demographic variables of the probabilistic sample in each country, the variables of the conjoint experiment and information about the time spent by respondents in completing each of the four experimental tasks.</p> <p>For detailed information and the codebook, see the document 'EU-TRHeaDS_conjoint_Description&Codebook'</p>
Conserved regulation of RNA processing in somatic cell reprogramming
<p><strong>Data set 1. Transcript expression across human RNA-Seq samples: estimated read counts. </strong>The file contains estimated read counts, generated by kallisto (<a href="https://pachterlab.github.io/kallisto/">https://pachterlab.github.io/kallisto/</a>), for human transcripts and RNA-Seq samples used in this study (see Additional file 2 of the accompanying publication). The format is a compressed (GZIP) tab-separated transcript-by-sample matrix. Ensembl transcript identifiers and a combined Sequence Read Archive study/sample name identifier serve as row and column names, respectively.</p> <p><strong>Data set 2. Transcript expression across murine RNA-Seq samples: estimated read counts. </strong>As in Data set 1, but for mouse transcripts.</p> <p><strong>Data set 3. Transcript expression across simian RNA-Seq samples: estimated read counts. </strong>As in Data set 1, but for chimpanzee transcripts.</p> <p><strong>Data set 4. Transcript expression across across human RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 1, but instead of read counts, transcript abundances in transcripts per million (TPM), as estimated by kallisto (<a href="https://pachterlab.github.io/kallisto/">https://pachterlab.github.io/kallisto/</a>), are listed. Format, column and row names as in Data set 1.</p> <p><strong>Data set 5. Transcript expression across murine RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 4, but for mouse transcripts.</p> <p><strong>Data set 6. Transcript expression across simian RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 4, but for chimpanzee transcripts.</p> <p><strong>Data set 7. Differential expression analyses across human RNA-Seq sample groups: log fold changes. </strong>The file contains log fold changes, inferred by edgeR (<a href="http://bioconductor.org/packages/release/bioc/html/edgeR.html">http://bioconductor.org/packages/release/bioc/html/edgeR.html</a>), for human genes and the RNA-Seq sample group contrasts listed in Additional file 3 of the accompanying publication in a compressed (GZIP) TSV gene-by-comparison matrix. Ensembl gene identifiers and a descriptive contrast identifier serve as row and column names, respectively.</p> <p><strong>Data set 8. Differential expression analyses across murine RNA-Seq sample groups: log fold changes. </strong>As in Data set 7, but for mouse genes.</p> <p><strong>Data set 9. Differential expression analyses across simian RNA-Seq sample groups: log fold changes. </strong>As in Data set 7, but for chimpanzee genes.</p> <p><strong>Data set 10. Differential expression analyses across human RNA-Seq sample groups: false discovery rates. </strong>The file contains false discovery rates (FDR) for the differential expression analyses summarized in Data set 7. Format, column and row names as in Data set 7.</p> <p><strong>Data set 11. Differential expression analyses across murine RNA-Seq sample groups: false discovery rates. </strong>As in Data set 10, but for mouse genes.</p> <p><strong>Data set 12. Differential expression analyses across simian RNA-Seq sample groups: false discovery rates. </strong>As in Data set 10, but for chimpanzee genes.</p> <p><strong>Data set 13. Quantification of alternative splicing events across human RNA-Seq samples. </strong>The file contains ‘percent spliced in’ (PSI) values computed by SUPPA (<a href="https://github.com/comprna/SUPPA">https://github.com/comprna/SUPPA</a>) for annotated alternative splicing events (inferred from the transcript annotation of the human genome, Ensembl release 84; <a href="http://www.ensembl.org/">http://www.ensembl.org/</a>). The format is a compressed (GZIP) tab-separated transcript-by-sample matrix. SUPPA-provided event identifiers and a combined Sequence Read Archive study/sample name identifier serve as row and column names, respectively.</p> <p><strong>Data set 14. Quantification of alternative splicing events across murine RNA-Seq samples. </strong>As in Data set 13, but for mouse alternative splicing events.</p> <p><strong>Data set 15. Differential splicing analyses across human RNA-Seq sample groups: differences in ‘percent spliced in’ (ΔPSI). </strong>The file contains ΔPSI values for human alternative splicing events (as in Data set 13). The RNA-Seq sample group contrasts are listed in Additional file 3 of the accompanying publication. Values were inferred by SUPPA’s diffSplice functionality (<a href="https://github.com/comprna/SUPPA">https://github.com/comprna/SUPPA</a>). The format is a compressed (GZIP) tab-separated gene-by-comparison matrix. SUPPA event identifiers and a descriptive contrast identifier serve as row and column names, respectively.</p> <p><strong>Data set 16. Differential splicing analyses across murine RNA-Seq sample groups: differences in ‘percent spliced in’ (ΔPSI). </strong>As in Data set 15, but for mouse alternative splicing events.</p> <p><strong>Data set 17. Differential splicing analyses across human RNA-Seq sample groups: P values. </strong>The file contains P values for the differential splicing analysis of human alternative splicing events summarized in Data set 15. Format, column and row names as in Data set 15.</p> <p><strong>Data set 18. Differential splicing analyses across murine RNA-Seq sample groups: P values. </strong>The file contains P values for the differential splicing analysis of mouse alternative splicing events summarized in Data set 16. Format, column and row names as in Data set 15.</p> <p><strong>Data set 19. Transcript expression across murine RNA-Seq time course data: estimated read counts. </strong>As in Data set 2, but for the time course data generated for the accompanying publication.</p> <p><strong>Data set 20. Transcript expression across murine RNA-Seq time course data: estimated transcript abundances. </strong>As in Data set 5, but for the time course data generated for the accompanying publication.</p> <p><strong>Data set 21. Quantification of alternative splicing events across murine RNA-Seq time course data. </strong>As in Data set 14, but for the time course data generated for the accompanying publication.</p>
Real-time deformability cytometry reference data
<p>This dataset consists of four exemplary real-time fluorescence and deformability cytometry measurements. The HDF5-files can be opened with dclab [1] or Shape-Out [2].</p> <p><strong>calibration_beads.rtdc</strong><br> The calibartion beads (8 Peaks, PolyAN) consist of eight bead populations with different mixtures of fluorophores.</p> <p><br> <strong>CD34_HSPC.rtdc</strong><br> Hematopoietic stem and progenitor cells (HSPCs) were obtained using apheresis. The cells were tagged with a fluorescently labeled antibody that binds to the CD34 transmembrane protein. CD34-positive HSPCs are gated with `fl3_max > 90`. Set `area_ratio < 1.05` to remove aggregates. Data were used in [3].</p> <p><br> <strong>leukocytes.rtdc</strong><br> The leukocyte population (white blood cells) of this blood sample can be visualized by setting `aspect < 2` and `area_ratio < 1.05`. For more information, see e.g. [4].</p> <p><br> <strong>reticulocytes.rtdc</strong><br> Blood contains mostly red blood cells (RBCs) and about 1% reticulocytes (which develop into mature RBCs). Reticulocytes contain ribosomal RNA which was stained with Syto13 for this measurement. Set `area_ratio < 1.05` to remove aggregates. Data were used in [3].</p> <p><br> [1] <a href="https://github.com/ZellMechanik-Dresden/dclab">https://github.com/ZellMechanik-Dresden/dclab</a></p> <p>[2] <a href="https://github.com/ZellMechanik-Dresden/ShapeOut">https://github.com/ZellMechanik-Dresden/ShapeOut</a></p> <p>[3] Rosendahl et al., "Real-time fluorescence and deformability cytometry". Nature Methods, 15(5):355–358, 2018. doi:<a href="https://dx.doi.org/10.1038/nmeth.4639">10.1038/nmeth.4639</a>.</p> <p>[4] Toepfner et al., "Detection of human disease conditions by single-cell morpho-rheological phenotyping of whole blood". eLife, 7:e29213, 2017. doi:<a href="https://dx.doi.org/10.1101/145078">10.1101/145078</a>.</p> <p><br> SHA256 sums:<br> 08c2ef13eed903ef0f9e451727ab8484df09b5d3b39227dab726e0164dcbe244 calibration_beads.rtdc<br> 663b44a9db88d85996500045489e37a317cf115719223a531d617f8e3d450e79 CD34_HSPC.rtdc<br> 68bd538b42ffb990f1db52d5f3b21f37c9aff31208ab284f3910fd6872c40fdb leukocytes.rtdc<br> 5c323ea75bf7eeb2a28d922730772d50270dd872d6957e60d6062663f3628fb3 reticulocytes.rtdc</p>
Homologous membrane protein structures (HOMEP) version 1
<p><strong>Table 1</strong> = List of membrane protein structures in the <strong>HOMEP</strong> data set (version 1).<br> From Forrest, Tang & Honig 2006 Biophysical Journal (Supplementary Table 1)<br> <a href="https://www.ncbi.nlm.nih.gov/pubmed/16648166">https://www.ncbi.nlm.nih.gov/pubmed/16648166</a></p> <p>Contains the following columns:<br> PDB-Code Protein-Name Source Res-(Å) Length (Num-TM) Number-of-TM-domains Family</p> <p><strong>Table 2</strong> = List of pairs of membrane protein structures in the <strong>HOMEP</strong> data set (version 1).<br> From Forrest, Tang & Honig 2006 Biophysical Journal (Supplementary Table 2)</p> <p>Contains the following columns:<br> Model Family Query Template ID(%) RMS(Å) GDT_TS(%) TM-ID(%) TM-RMS(Å) TM GDT_TS(%)</p> <p><strong>Table 3 </strong>= Manually-defined transmembrane regions in the <strong>HOMEP</strong> data set (version 1), listed for each family by transmembrane segment number. From Forrest, Tang & Honig 2006 Biophysical Journal (Supplementary Table 3).</p> <p>Contains the columns defined as follows:<br> Protein chain identifier, start (-s) and end (-e) residues for each PDB structure in the family</p>
Seawater chromium concentrations and isotope compositions in the Southern Ocean during the austral summer of 2016/2017, on board the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Dissolved seawater chromium (Cr) concentrations and stable isotope compositions measured on samples collected with a trace metal clean rosette system in the Southern Ocean. Stations TM 7 to TM 12 reflect a north-south transect from Hobart, Tasmania to Mertz Glacier in Antarctica. Stations TM 14 and TM 15 neighbour the Balleny Islands. Stations TM 18 and TM 20 are located in the Drake Passage. Water samples were collected down to a depth of 1000 metres. The water was filtered in a class 100 clean container aboard the ship through pre-rinsed Supor Acropak capsule filters (0.2 um). Subsequently the samples were acidified and stored at a pH < 2 for several months prior to analysis. Reported values therefore represent bulk seawater chromium (Cr III and Cr VI). The data was obtained using the double-spike technique.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_chromium_isotope_concentration.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This chromium concentration and isotope composition dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>
Long-term immunity against yellow fever in children vaccinated during infancy: a longitudinal cohort study
<p>The data represent the concentrations of specific neutralizing antibodies following infant immunization against yellow fever. We used a microneutralization assay to measure protective antibodies against yellow fever virus in 587 Malian and 436 Ghanaian children vaccinated around age 9 months, and followed for 4.5 years (Mali), or 2.5 and 6 years (Ghana). We standardized antibody concentrations with reference to the yellow fever WHO International Standard.</p> <p>The serum samples used in this study, and the sample metadata included in the present dataset originate from trials of the meningococcal group A conjugate vaccine, MenAfriVac, namely the PsATT-004 (phase II) and Pers-004 (phase IV) studies in Ghana, and the PsATT-007 (phase III) and Pers-007 (phase IV) studies in Mali (clinical trial registry numbers ISRCTN82484612, ISRCTN10763234, PACTR201110000328305, and ISRCTN37623829). MenAfriVac was developed by PATH and Serum Institute India Pvt. Ltd. (SIIPL).</p> <p>This dataset consists of three files:</p> <p>1. Ghana group data | Tab-delimited text file: Yellow_fever_nAb_Ghana.csv</p> <p>2. Mali group data | Tab-delimited text file: Yellow_fever_nAb_Mali.csv</p> <p>3. Data dictionary | PDF file: Yellow_fever_nAb_Data_Dictionary.pdf</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
EMU Positions
<p>The <em>EMU Position</em> data report positions of member states and EU institutions as well as salience scores for 47 contested issues related to EMU reforms between 2010 and 2015. Interactive data portal available at <a href="https://emuchoices.eu/data/emup/">EMUchoices.eu/data/emup/</a>. The dataset was compiled by the EMU Choices consortium, which has received funding from the European Union’s Horizon research and innovation programme under grant agreement No. 649532.</p>
Collection of figures to explore intra-regime weather variability of North Atlantic-European year-round weather regimes as Supplementary Dataset for Gerighausen et al. (2024)
<p>This is a supplementary dataset accompanying the publication <strong>Gerighausen et al. (2024) </strong>submitted to Meteorological Applications. It contains a collection of browsable figures, complementing selected regimes, seasons, and countries in the paper. The figures are provided as a zipped archive. The ZIP-File (1.2 GB) contains 4 subfolders and 4 auxiliary files as described in <strong>readme.md </strong>in the main folder. Once downloaded and unpacked, the .html navigation panels can be used in any browser to navigate through the plots. </p> <p>Data and methods used to generate the figures are explained in Gerighausen et al. (2024). In brief the analysis is based on ERA5 reanalysis 1979-2021 at 1° grid spacing and 6h temporal resolution aggregated to daily data. Anomalies are computed with respect to a 31-day running mean climatology. The figures are explained in the table below and in the navigation panel.</p> <p><strong>Gerighausen</strong>, J., J. Dorrington, M. Osman, and C. M. Grams, <strong>2024</strong>: Quantifying intra-regime weather variability for energy applications, <em>submitted to Meteorological Applications.</em> <a href="https://doi.org/10.48550/arXiv.2408.04302">doi:10.48550/arXiv.2408.04302</a></p>
Damage Localisation in Fresh Cement Mortar Observed via In Situ (Timelapse) X-ray uCT imaging.
<p>This is dataset to paper: Damage Localisation in Fresh Cement Mortar Observed via In Situ (Timelapse) X-ray uCT imaging.</p>
Dataset of "Thermal Stability of Valuable Metals in Lithium-Ion Battery Cathode Materials: Temperature Range 100-400 °C"
<p>Lithium is crucial in lithium-ion batteries (LIBs), serving as a main component of the electrolyte and cathode. Elements such as cobalt, nickel, and manganese are also vital for high performance, energy density, and stability. This study aimed to examine the behaviour of end- of-life cathode material (LiNi0.6Mn0.2Co0.2O2) and its valuable metals after exposure to temperatures between 100 and 400 °C, comparing it with untreated material. The lithium content cannot be reliably determined by conventional analytical methods, so inductively coupled plasma optical emission spectroscopy (ICP-OES) was chosen for this purpose. For ICP-OES measurements, samples were dissolved in different solvents for a specified time, and the concentrations of lithium, nickel, manganese, and cobalt were measured. From the measured values, their theoretical yields were calculated. Due to the annealing at given temperatures and subsequent dissolution, this step can be considered as the first stage of the pyrometallurgical- hydrometallurgical process used in battery recycling. The study was complemented by further analyses to monitor the effect of annealing temperatures on the properties of the material. Based on the results, it was found that the highest theoretical yield in this temperature range was for material annealed at 400 °C and dissolved in 20% nitric acid for 4 hours.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.