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22,038 results for “relativity”
Lexical Relations from the Wisdom of the Crowd 1.1
<p>A set of 300 most frequent nouns has been extracted from the Russian National Corpus. Then, each method or resource, including RuThes and RuWordNet, produced at most five hypernyms, if possible. In case it is not possible, missing answers treated as empty results. This resulted in 10,600 unique non-empty subsumption pairs that have been passed for crowdsourcing annotation on the Yandex.Toloka microtask platform. Each pair has been annotated by seven different annotators whose mother tongue is Russian and the age is at least 20 by February 1, 2017.</p> <p>The layout of the human intelligence task (HIT) design assumes the direct answer to a simple question: does the given pair of words represent a meaningful <em>is-a</em> relation? Since the crowd workers are not expert lexicographers and this question might be difficult for them, it has been rephrased as “Is it correct that a <em>kitten</em> is a kind of <em>mammal</em>?” (in Russian).</p> <p>The answers have been aggregated using the Yandex.Toloka proprietary answer aggregation mechanism. As the result, 4,576 out of 10,600 pairs have been annotated as positive while the rest 6,024 have been annotated as negative.</p> <p>Interestingly, the workers were more confident in negative answers rather than in the positive ones. These negative answers are extremely useful for both training and testing different relation extraction methods. To the best of our knowledge, this is the first dataset of this kind made for the Russian language using microtask-based crowdsourcing.</p>
GEroNIMO project EP database related to KERs
<p>European Patents dataset performed using <a href="http://www.lens.org">www.lens.org</a> free database for the 7 Key Exploitable Results (KER) identified on the Grant Agreement and selected keywords for GEroNIMO projects. Set up parameters included.</p>
Temperature-related mortality exposure-response functions for 854 cities in Europe
<p>This repository contains data to reconstruct the exposure-response functions (ERF) of temperature-related mortality by five 5 age groups in 854 cities in Europe.</p><p>These ERFs have been derived in the study by Masselot et al. 2023, <i>Excess mortality attributed to heat and cold: a health impact assessment study in 854 cities in Europe</i>, The Lancet Planetary Health (<a href="https://protect-eu.mimecast.com/s/zqg2Cg204i4ZMYKf3NUKN?domain=doi.org">https://doi.org/10.1016/S2542-5196(23)00023-2</a>). An associated semi-replicable GitHub repository is available at <a href="https://github.com/PierreMasselot/Paper--2023--LancetPH--EUcityTRM">https://github.com/PierreMasselot/Paper--2023--LancetPH--EUcityTRM</a> to reproduce part of the analysis and the full results, as well as to provide technical details on the derivation of these ERFs.</p><p><strong>Note: </strong>This updated version contains revised data after the correction of an error in the code related to the computation of the age-specific baseline mortality rates. Details about the error can be found in the GitHub repository linked above. This correction only affects the figures of excess mortality (found in the `results.zip` archive) while the ERFs are negligibly affected. The originally published results can be found in V1.0.0 of this repository.</p><p><strong>Extraction of the ERFs</strong></p><p>The ERFs are provided as coefficients of B-spline functions that can be used to reconstruct the ERFs, along with variance-covariance matrices and quantiles from location-specific temperature distributions. The parametrisation associated with these coefficients is a quadratic B-spline (degree 2), with knots located at the 10th, 75th and 90th percentiles of the temperature distribution. In R, the associated basis can be constructed using the <i>dlnm</i> package, with a temperature series <i>x</i>, as follows:</p><blockquote><p>library(dlnm) </p><p>basis <- onebasis(x, fun = "bs", degree = 2, knots = quantile(x, c(.1, .75, .9)))</p></blockquote><p>The main files associated with ERFs are the following:</p><p><i>coefs.csv</i>: The B-spline coefficients for each age group and city.</p><p><i>vcov.csv</i>: The variance-covariance matrix of the coefficients in each city and age group. It is provided here as the lower triangular part of the matrix with names indicating the position of each value (v[row][column]). In R, assuming <i>x</i> is a row of this file, the matrix can be reconstructed using <i>xpndMat(x)</i> after loading the <i>mixmeta</i> package.</p><p><i>coef_simu.csv</i>: 1000 simulations from the distribution of each city and age-specific coefficients. Useful to derive empirical confidence intervals for derived measures such as excess deaths or attributable fractions.</p><p><i>tmean_distribution.csv</i>: The city-specific temperature percentiles representing the distribution of the data derived from the ERA5-Land dataset.</p><p><strong>Health impact assessment results</strong></p><p><i>results.zip</i>: A summary of the results from the health impact assessment reported in the analysis. The dataset includes several impact measures provided in files representing different geographical levels, including city, country and regional level. Different files are also provided for age-group specific or all age results.</p><p><strong>Additional data</strong></p><p>We provide additional data that are useful to reproduce or extend the analysis. Please note that due to restrictive data-sharing agreements for the mortality series, only a part of the code is reproducible. See the <a href="https://github.com/PierreMasselot/Paper--2023--LancetPH--EUcityTRM">associated GitHub repository</a> for more details.</p><p><i>metadata.csv</i>: City-specific metadata used to create the ERFs and perform the health impact assessment.</p><p><i>additional_data.zip</i>: contains further data used to replicate the second stage of the analysis and the final health impact assessment. It includes the full city-level daily temperature series (<i>era5series.csv</i>), the detail of extracted metadata for available years (<i>metacityyear.csv</i>), a description of the city-level characteristics (<i>metadesc.csv</i>), and the first-stage ERF coefficients for all available city and age-groups (<i>stage1res.csv</i>). Additionally, the file <i>meta-model.RData</i> contains R object defining the second-stage model that can be used to predict new ERFs. </p>
Relation Extraction Dataset for Dutch Biographical Texts
<p>A manually annotated dataset with relations relevant for biographical texts. The texts are in Dutch and are originally available in the Biographical portal of the Netherlands (http://www.biografischportaal.nl/)</p>
GWAS Summary Statistics from "Sex and statin-related genetic associations at the PCSK9 gene locus – results of genome-wide association meta-analysis"
<p>GWAMA summary statistics of PCSK9 levels stratified by sex and statin useage in Europeans.</p> <p>When using this data, please cite:</p> <p>Pott, J., Kheirkhah, A., Gadin, J.R. <em>et al.</em> Sex and statin-related genetic associations at the <em>PCSK9</em> gene locus: results of genome-wide association meta-analysis. <em>Biol Sex Differ</em> <strong>15</strong>, 26 (2024). https://doi.org/10.1186/s13293-024-00602-6</p> <p>All txt files contain the following columns:</p> <ul> <li>markername (unique SNP ID)</li> <li>chr</li> <li>bp_hg19 (base position according to hg19)</li> <li>EA (effect allele)</li> <li>OA (other allele)</li> <li>EAF (effect allele frequency)</li> <li>info (minimal info score across all used studies)</li> <li>nSamples (sample size per SNP)</li> <li>nStudies (in case of double-stratified data: number of studies; in case of single-stratified data: 2, as it is a meta-analysis of the two double-stratified data sets)</li> <li>beta (effect estimate)</li> <li>SE (standard error)</li> <li>pval (p-value)</li> <li>I2 (SNP heterogeneity across studies)</li> <li>invalidAssoc (TRUE/FALSE flag if this variant was excluded in our analysis)</li> <li>reason4exclusion (reason why this SNP was excluded)</li> <li>phenotype (phenotyp setting)</li> </ul>
Disinformation on YouTube: A dataset of YouTube comments on videos related to claims made by Trump and Vance on Haitian immigrants
<div> <div> <div> <div> <div> <p>The corpus contains three files. First, the youtube_haitian_disinformation_videos_meta.csv file includes comments and YouTube video metadata. Data is organized around per video information. The columnar values are: </p> </div> </div> </div> <div> <ul> <li> <p>video_id </p> <p> </p> </li> <li> <p>date (video publication date) </p> <p> </p> </li> <li> <p>title </p> <p> </p> </li> <li> <p>description </p> <p> </p> </li> <li> <p>channel_title </p> <p> </p> </li> <li> <p>transcript </p> <p> </p> </li> <li> <p>transcript_str (Video transcript without timestamps) </p> <p> </p> </li> <li> <p>views </p> <p> </p> </li> <li> <p>likes </p> <p> </p> </li> <li> <p>comments (all comments per video) </p> </li> </ul> </div> <div> <div> <div> <p>Second, the youtube_haitian_disinformation_comment_reply_metadata.csv file includes comments, replies, and comment metadata. Each comment occupies its own row in the spreadsheet. The columnar field are: </p> </div> </div> </div> <div> <ul> <li> <p>video_id </p> <p> </p> </li> <li> <p>comment </p> <p> </p> </li> <li> <p>comment_date </p> <p> </p> </li> <li> <p>comment_like_count </p> <p> </p> </li> <li> <p>author </p> <p> </p> </li> <li> <p>comment_id </p> <p> </p> </li> <li> <p>in_reply_to </p> <p> </p> </li> <li> <p>neg, neu, pos, compound (VADER polarity scores) </p> <p> </p> </li> <li> <p>named_entities (spaCy named entity tags with tokens) </p> <p> </p> </li> <li> <p>emoji (spaCy Emojis and token spans) </p> </li> </ul> </div> </div> <div> <div> <div> <div> <p>Comments and associated metadata are represented in individual rows. </p> </div> <div> <p>The third file contains the results of the TFIDF analysis described herein. The TFIDF analysis features the top 5000 terms weights for the comments to each video in a .csv file. </p> </div> </div> </div> <div> <ul> <li> <p>youtube_disinfo_comments_tfidf_results_per_video.csv </p> </li> </ul> </div> </div> </div>
Dataset for Careers and Related Aptitudes
<p>This dataset was developed as part of a project to create a career recommendation system. It includes information on 612 real-world careers, detailing the required differential aptitude values for each. The attributes covered are Numerical Ability, Abstract Reasoning, Verbal Reasoning, Mechanical Reasoning, Spatial Ability, and Verbal Ability.</p>
GENEActiv accelerometer file related to the #120 OxWearables / stepcount issue
<p>An example of .bin file that have an IndexError when processing.</p> <p>Consider <a title="#120 OxWearables / stepcount issue" href="https://github.com/OxWearables/stepcount/issues/120" target="_blank" rel="noopener">#120 OxWearables / stepcount issue</a> for more details.</p> <p>The .csv files are 1-second epoch conversions from the .bin file and contain <em>time</em>, <em>x</em>, <em>y</em>, <em>z</em> columns. The conversion was done by: </p> <ol> <li>reading the .bin with the <a title="GENEAread R package" href="https://www.rdocumentation.org/packages/GENEAread/" target="_blank" rel="noopener">GENEAread R package</a>.</li> <li>keeping only the time, x, y and z columns.</li> <li>saving the data.frame into a .csv file.</li> </ol> <p>The only difference between the .csv files is the column format used for the time column before saving:</p> <ul> <li>time column in XXXXXX_....csv had a string class</li> <li>time column in XXXXXT....csv had a "POSIXct" "POSIXt" class</li> </ul>
Twitter dataset of flood-related images for September 2021, Thailand and June/July 2021, Nepal floods
<p>Twitter dataset related to flood events onsets in Thailand and Nepal, focused on September 26/27, 2022, June 16/17 2021 and July 01/02 2021. The dataset has been processed with a VisualCit pipeline in order to automatically filter a relevant subset of posts through automated image analysis, using deep learning techniques. The posts were then geolocated using the CIME algorithm. Additional information about the data collection and data processing are described in <a href="http://arxiv.org/abs/2202.12014">http://arxiv.org/abs/2202.12014</a></p>
Relative Humidity from Copernicus Essential Climate Variables for July months from 1980 to 2018
<p>This dataset can be used if you have issues with the Essential Climate Variables Galaxy Tool for the Training "Getting your hands-on climate data" in the section "Essential Climate Variables". You can then upload this dataset in your Galaxy history and skip the 1st step (<strong>Copernicus Essential Climate Variables</strong>) and directly start with 2. <strong>map plot gridded (lat/lon) netCDF data.</strong></p>
Genetic association analysis of anti-VEGF treatment response in neovascular age-related macular degeneration
<p>Summary statisics of an association study of 6,908,005 genetic variants with anti-VEGF nAMD treatment response in 179 treatment-naïve nAMD probands. This dataset supplements the publication "Genetic Association Analysis of Anti-VEGF Treatment Response in Neovascular Age-Related Macular Degeneration" (DOI: 10.3390/ijms23116094). Details regarding the methods and version numbers can be found in the corresponding manuscript.</p>
Dataset of The latent factor structure and assessment of childbirth-related PTSD in fathers and co-parents: psychometric characteristics of the City Birth Trauma Scale – French version (partner version)
<p>Little is known about the latent factor structure of CB-PTSD symptoms in co-parents (i.e., (a non-expecting mother or father). The City Birth Trauma Scale (City BiTS) was developed to assess childbirth-related posttraumatic stress disorder following childbirth (CB-PTSD), based on the PTSD criteria of the DSM-5. Still, no validated French questionnaire exists to assess CB-PTSD symptoms in co-parents. This study aimed (1) to establish the latent factor structure of CB-PTSD, and (2) to validate the French version of the City BiTS (partner version). </p> <p>This dataset contains data on the mental health (i.e., CB-PTSD, depression, anxiety) of 282 co-parents who had an infant within the last 12 months. Sociodemographic data such as age, marital status, educational level, weeks of gestation, type of delivery, history of traumatic childbirth, or history of a traumatic event is available. </p>
Knowledge gaps on trade-offs of soil carbon sequestration related to soil management strategies
<p>The database contains 87 unique literature items (29 reviews, 42 meta-analyses, 16 original papers) describing the effect of a soil management strategy (tillage management, cropping systems, water management, cover crops, crop residues, livestock manure, slurry, compost, biochar, liming) on the trade-offs between soil carbon sequestration or SOC change and N2O emission, CH4 emission and nitrogen leaching. Since some literature items describe effects of several SMS categories, the database_summary tab comprises a total of 112 unique inputs. For each input it is indicated in the Database_summary tab if it was used as input for the "Soil management effect assessment" in Maenhout et al. (2024) [Maenhout, P., Di Bene, C., Cayuela, M. L., Diaz-Pines, E., Govednik, A., Keuper, F., Mavsar, S., Mihelic, R., O'Toole, A., Schwarzmann, A., Suhadolc, M., Syp, A., & Valkama, E. (2024). Trade-offs and synergies of soil carbon sequestration: Addressing knowledge gaps related to soil management strategies. European Journal of Soil Science, 75(3), e13515. https://doi.org/10.1111/ejss.13515] and/or to define knowledge gaps ("Knowledge gap in tab"-column). Knowledge gaps and research recommendations are gouped per soil management strategy in different tabs in this database. Per soil management strategy, knowledge gaps are clustered per theme in groups. These themes include: the specific soil management strategy, pedoclimatic conditions, establishment of experiments, other soil management strategies, meta-analysis, modelling and other</p>
SPHERA High Resolution Reanalysis over Italy - Hourly surface relative humidity (2-meter height) 2013-2020
<p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface relative humidity at 2-meter height for the period 2013-2020. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>Other fields currently available on Zenodo are the surface air temperature at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
SPHERA High Resolution Reanalysis over Italy - Hourly surface relative humidity (2-meter height) 2004-2012
<p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface relative humidity at 2-meter height for the period 2004-2012. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>Other fields currently available on Zenodo are the surface air temperature at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
SPHERA High Resolution Reanalysis over Italy - Hourly surface relative humidity (2-meter height) 1995-2003
<p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface relative humidity at 2-meter height for the period 1995-2003. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>Other fields currently available on Zenodo are the surface air temperature at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
Dataset for Sandboxing use case SUC2 related to cyber attacks affecting Wide Area Protection
<p><span>This dataset is related to the operation of the second KIOS CoE sandboxing use case (SUC2) which inclused 3 scenarios (S1-S3) which examins the behavious a WAP scheme of power grids in case of a short circuit fault and in case of two types of cyber attacks. The description of the architecture of the University of Cyprus/ KIOS CoE sandboxing environmnet used for extracting these datasets along with the full list of scenarios and their detailed implementation are described in the supporting documents.</span></p> <p><span>Brief description of each of the 3 scenarios of this SUC2 are provided below.</span></p> <p><span>The datasets for the first scenario (S1) of SUC2</span><span> examines the operation of a wide area protection scheme in a transmission line which receives data sent from PMUs at the two ends of the lines, when a short-circuit fault occurred in the range of the transmission line between buses 7 and 8 of the system. More details about the scenario SUC2/S1 related to this scenario's dataset can be found in Section </span><span>1.3.1</span><span> of the SUC2 supporting document. </span><span><span>The dataset includes electrical measurements of the current flow in line 7-8 (of the IEEE 9-bus system), in both magnitude and sinusoidal form</span><span>.</span><span> The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files, which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of RMS values were recorded by the Typhoon controller as they were sent by the two PMUs, while the sine wave measurements were recorder through the OPAL-RT</span></span></p> <p><span>The datasets for second scenario (S2) of SUC2 investigates the operation of a wide area protection scheme which receives data sent from PMUs when a MITM FDI cyber-attack is conducted on the measurements of bus 7</span><span>, virtually implemented within the sandboxing, and introduces a multiplicative change to the current measurements before they are received by the Typhoon controller via IEEE C37.118 protocol</span><span>. Section 1.3.2 of the SUC2 supporting document provides more details about the scenario related to this dataset. </span><span>This dataset includes electrical measurements of the current flow, in magnitude and sinusoidal format, of the transmission line between buses 7 and 8 of the <span>digital twin of the IEEE 9-bus system.</span> The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of magnitude values were recorded by the Typhoon controller, while the data from the sinusoidal waveform were recorder by OPAL-RT. </span></p> <p><span>Thie dataset of the SUC2/S3 examines the operation of a wide area protection scheme which receives data sent from PMUs when a combined MITM with DoS cyber-attack is conducted, as actual attack, in the isolated communication network of the sandboxing environment, disrupting the C37.118 UDP communication exchanged between OPAL-RT 5707, where the digital twin of IEEE 9-bus system was implemented, and Typhoon controller. More details about this scenario associated to this dataset can be found in Section </span><span>1.3.3<span></span></span><span> of the supporting document of SUC2.</span></p> <p><span>This dataset includes electrical measurements of current’s flow magnitude of the transmission line between buses 7 and 8 of the <span>digital twin of the IEEE 9-bus system.</span> The dataset was recorded by the Typhoon controller, and it is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. In addition, the dataset includes network traffic packets captured as .pcapng<span> </span>and .csv files. <span> </span></span></p>
CrowdTruth Corpus for Open Domain Relation Extraction from Sentences
<p>This repository contains a ground truth corpus for open domain relation extraction from sentences, acquired with crowdsourcing and processed with <strong><a href="http://crowdtruth.org/">CrowdTruth</a></strong> metrics that capture ambiguity in annotations by measuring inter-annotator disagreement.</p> <p>The dataset contains annotations for 4,100 sentences sampled from Angeli et al. (1) and Riedel et al. (2), over 16 relations, with each sentence annotated by 15 workers. The sentences have been pre-processed with Distant Supervision (3) using the Freebase knowledge base, in order to identify the term pairs in each sentence that are likely to express a relation. The crowdsourced data was collected from <a href="http://figure-eight.com/">Figure Eight</a> and <a href="https://www.mturk.com/">Amazon Mechanical Turk</a>.</p> <p>This corpus has been discussed in the following papers:</p> <ul> <li>Anca Dumitrache, Lora Aroyo and Chris Welty: <strong><a href="https://arxiv.org/abs/1809.00537">Crowdsourcing Semantic Label Propagation in Relation Classification</a></strong>. <a href="http://fever.ai/">FEVER</a> Workshop at <a href="http://emnlp2018.org/">EMNLP 2018</a>.</li> <li>Anca Dumitrache, Lora Aroyo and Chris Welty: <strong><a href="https://arxiv.org/abs/1711.05186">False Positive and Cross-relation Signals in Distant Supervision Data</a></strong>. <a href="http://www.akbc.ws/">AKBC</a> Workshop at <a href="http://nips.cc/">NIPS 2017</a>.</li> <li>Anca Dumitrache, Lora Aroyo and Chris Welty: <strong><a href="http://crowdtruth.org/wp-content/uploads/2017/03/collint17-open-domain.pdf">Disagreement in Crowdsourcing and Active Learning for Better Distant Supervision Quality</a></strong>. <a href="http://collectiveintelligenceconference.org/">Collective Intelligence 2017</a>.</li> </ul> <p>Sentence-level data is available in file: <code>|--data/output/aggregated_sentences.csv</code></p> <p>Worker-level data is available in file: <code>|--data/output/aggregated_workers.csv</code></p> <p>Raw crowdsourcig data is available in folder: <code>|--data/input/</code></p> <p>Results of the relation classification model are available in folder: <code>|--data/model_results/</code></p> <p> </p> <p>References</p> <p>(1) Angeli, Gabor, et al. "Combining distant and partial supervision for relation extraction." Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014.</p> <p>(2) Riedel, Sebastian, et al. "Relation extraction with matrix factorization and universal schemas." Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). 2013.</p> <p>(3) Mintz, Mike, et al. "Distant supervision for relation extraction without labeled data." Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2-Volume 2. Association for Computational Linguistics, 2009.</p>
Datasets for paper 'Cabello, V., Renner, A., Giampietro, M. 2019. Relational analysis of the resource nexus in arid land crop production. Advances in Water Resources 130:258-629'
<p>Datasets produced for the paper Cabello, V., Renner, A., Giampietro, M. 2019.<em> </em>Relational analysis of the resource nexus in arid land crop production. <em>Advances in Water Resources </em>130:258-269</p>
MiRoR11 - P2 - Annotated corpus for the relation between reported outcomes and their significance levels
<p>Corpus of relations between outcomes and significance levels</p> <p>This dataset contains annotations of the relations between reported outcomes and their significance levels.<br> Tab-separated format is used. The file contains the following comumns:<br> filename, sentence text, outcome, primary outcome start position, primary outcome end position, reported outcome, reported outcome start position, reported outcome end position, label</p> <p>The folder out_sig_rel contains the dataset splits for 10-fold cross-validation.</p>
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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.