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6,704 results for “reports”
Phenopackets for case reports of structural variants
<p>A collection of 188 published deleterious structural variants based on 182 cases published in 146 clinical case reports that describe individuals with Mendelian diseases.</p>
Experimental results dataset reported in the paper: "Temporal teleportation with pseudo-density operators: how dynamics emerges from temporal entanglement."
<p><strong>Temporal teleportation with pseudo-density operators: how dynamics emerges from temporal entanglement.</strong></p> <p>Experimental results data-set (as reported in Figure 2 of the main paper).</p> <p>Caption of the Figure: Classical bound violation ∆S<em><sup>(l)</sup></em>(n) =S<em><sup>(l)</sup></em>(2n)−(2n−2) (<em>l=T,H,S</em>) for the multi-parameter <em>CHSH</em> inequalities in the temporal (red), spatial (grey) and “hybrid” (blue) domain. The dots represent the experimental results, with the uncertainty bars evaluated as statistical fluctuations among repeated measurement sets, while the solid curves show the theoretically-expected values (for the correlations belonging to the spatial domain, deviations from the ideal case due to the V<em><sub>s</sub></em>= 0.982 estimated visibility of the generated |<em>ψ<sub>−</sub></em>〉state were considered)</p> <p>Data-set description:</p> <p>Column 1 = X-coordinate: n (Number of Settings)</p> <p>Column 2 = Y-coordinate: ∆S<em><sup>(S)</sup></em>(n), Space-like (S) CHSH Violation [Grey color in the figure on the paper]</p> <p>Column 3 = Uncertainty on ∆S<em><sup>(S)</sup></em>(n) (Space-like, S, Grey in the figure on the paper), </p> <p>Column 4 = Y-coordinate: ∆S<em><sup>(H)</sup></em>(n), Hybrid (H) CHSH Violation [Blue color in the figure on the paper]</p> <p>Column 5 = Uncertainty on ∆S<em><sup>(H)</sup></em>(n) (Hybrid, H, Blue in the figure on the paper)</p> <p>Column 6 = Y-coordinate: ∆S<em><sup>(T)</sup></em>(n), Time-like (T) CHSH Violation [Red color in the figure on the paper]</p> <p>Column 7 = Uncertainty on ∆S<em><sup>(T)</sup></em>(n) (Time-like, T, Red in the figure on the paper)</p>
Visualization and perception of data gaps in the context of Citizen Science projects: Gradation of Reporting Activity
<p>Online experiment about the influence of different numbers of levels of representation of reporting activity (total number of reports for all birds in the given time span and region) on proportion of correct responses and subjective evaluation of the task (NASA-TLX). Effects of representation with three (3) levels and effects of representation with five (5) levels are investigated. Two groups of members of ornitho.de were tested: experts - persons with access to database (more than 10 reports per month in average) and novices - persons without access to database (less than 10 reports per month in average). Two different tasks were given. The evaluation of statements on a map and the selection of grid fields that met a given requirement.</p>
Database of non-communicable disease reports contributing to UN high-level meeting process (2000-2020)
<p>Database of the key non-communicable disease reports, policy papers, strategies and journal series published 2000-2020 that fed into the UN high-level meeting process</p>
An open dataset of IPCC reports 'references (6th Assessment Cycle) – Version 1
<p>We provide a first version of an open dataset of publications cited by the IPCC reports of the 6<sup>th</sup> Assessment Cycle (<a href="https://www.ipcc.ch/reports/">Reports — IPCC</a>)</p> <p>The lists were extracted from the reference sections of three special reports and the first assessment report. [see Figure 1]. The data are presented in two formats: one on hand in text files with a list of references for each section of the reports (generally each chapter) and one other hand a structured format (json) with identifiers for the documents and the sections, the reference in string format as well as the extracted digital object identifiers (dois). In this first version, the dois extracted are mainly those which are provided in the references. The table 1 show the number of references and doi for each report.</p> <p>We plan, for subsequent releases, following enhancements:</p> <ul> <li>Further quality assurance of dois. We note that some entries are provided in references of the IPCC reports without dois although they are indexed in Crossref. The table 2 shows substantial differences in doi coverage among reference sections. Spot checks of the dataset suggest that those differences are mainly due to referencing behaviour of the section’s authors rather than on type of documents cited. We aim in the next version to complete those missing dois and systematically verify the dois included in the references.</li> <li>Expand the references lists to reports from past assessment cycles</li> </ul> <p>In addition, we plan a more detailed documentation of our dataflow & extraction process and to demonstrate how it can be used to create open, community curated, datasets of references of others non-scholarly documents.</p> <p>The json files have each two keys (1) schema with the structure of the table and (2) data: with the records.</p> <p>A simple way to read them into table is via a pandas dataframe</p> <p><em>import pandas as pd</em></p> <p><em>df = pd.read_json(file_name.json, orient = ‘table’)</em></p> <p> </p> <p><strong>Acknowledgment</strong></p> <p>We thank Valentin Hancu (EC/DG ECFIN) - for fruitful discussions on the data extraction process.</p> <p><strong>Disclaimer: </strong></p> <p>The views expressed in this paper are the author’s. They do not reflect the views or official positions of the European Commission.</p>
CT Fishing Report Occurrences
<p>Dataset of gamefish occurrences as compiled from the Connecticut Fishing Report (2006-2018) and Trophy Fish Report (2009-2018), both published by the Connecticut Department of Energy and Environmental Protection. Compiled as thesis project by Rebecca Hedreen for a Masters of Science in Biology from Southern Connecticut State University, with advisor Dr. Sean Grace.</p>
Dataset of traffic accidents reported on Twitter Bogotá Colombia
<p><strong>1 Classification Dataset</strong></p> <p>This dataset for the classification model contains 3,804 tweets, where 1,902 are related to traffic accident reports (TA, positive class) and 1,902 are unrelated (NTA, negative class).</p> <p>For training the tweet classification model, a collaborative labeling strategy was designed. Here, 30 people labeled data according to the instructions given. Each participant had to evaluate a tweet to manually classify it into one of three categories defined as: traffic accident related, unrelated and don´t know/no response. Each tweet was evaluated by 3 participants. The correct label was selected by voting; the 3 people must agree on the selected label, otherwise the tweet was excluded from training. This process took a month and required the development and deployment of a web application.</p> <p><strong>2 NER Dataset (Named Entity Recognition)</strong></p> <p>For the entity recognition model training, a sample of the filtered tweets resulting from the previous classification phase was taken. 1,340 tweets were extracted, where 800 are from “unofficial” users, almost 60% of the sample. These tweets were user reports on traffic incident occurred in Bogota from October 2018 to July 2019, including other tweets that contained some location references such as reports on the state of road infrastructure; some tweets from the years 2016 and 2017 were also included. Although these posts were not related to accidents per se, they were selected because they contained location information. The purpose was to train a model that would recognize these entities, because a classifier of accident-related tweets was previously created. Additionally, the dataset was split, reserving 1,072 tweets for training and 268 for evaluation.</p> <p>This dataset was manually labeled using the IOB (Inside-outside- beginning) format. The labeling tool called Brat Annotation Tools was used for this task. The labels defined are Location, which refers to the location of the report; and Time, which refers to the time or date of the incident. Accordingly, 5 labels were generated: B-loc, I-loc, B-time, I-time and O. The O label refers to Others.</p> <p><strong>3 Traffic accident Twitter geolocation</strong></p> <p>A dataset with 26362 traffic accident tweets with the coordinates of the incident and the date of publication.</p>
Ouseley mission to Persia 1810-15 – Analysis of reports on journeys
<p>These files form part of an archive of research data relating to the diplomatic mission to Persia 1810-15, led by Sir Gore Ouseley as ambassador. The data have been compiled by independent researchers W H (Bill) Martin and Sandra Mason; their contact details are in the README file.. The files comprise a number of searchable listings relating to the reports produced by members of the mission and other relevant data. The data formed the basis for a published report on the Ouseley Mission referenced below, which is also part of the archive. </p> <p>This section of the archive contains databases, under the general heading <em>ousjourney</em>, with analyses of the four reports on the journey to Persia produced respectively by Sir Gore Ouseley, Sir William Ouseley, James Justinian Morier, and William Price. The explanatory README text file contains details of these reports and information on the fields included in the databases, their definitions and of the codings used in different cases. </p>
Driving social and economic development in the MENA region: the role of international organizations - Stakeholders' engagement meeting report
<p>On 15 November 2018, the Center for Public Policy and Democracy Studies (PODEM) hosted a stakeholders’ meeting in Istanbul with twenty-nine participants, including academics, experts, and representatives from civil society and international humanitarian and development organizations from Europe and the MENA region, as part of the Middle East North Africa Regional Architecture Project (MENARA). Through two panels of expert presentations and facilitated discussion, the participants addressed present challenges, ongoing transformations and future opportunities facing societies across the region, as well as the role of international organizations in addressing these challenges and opportunities. This report will summarize the discussion during that meeting in two broad parts, focusing on the region’s challenges and positive developments respectively and is further divided into sections by issue subject. Two sets of policy recommendations are provided at the end of the report.</p>
Dataset from: Reporting of patient involvement: A mixed-methods analysis of current practice in health research publications
<p>This record includes the data associated with the study "Reporting of patient involvement: A mixed-methods analysis of current practice in health research publications". In this study, we evaluated the extent and quality of patient involvement reporting in examples of current practice in health research. We used a targeted search strategy to identify publications that report on patient involvement using the following three samples:</p> <ul> <li>Publications published in 2019 in <em>The BMJ, </em>which requires reporting on patient involvement in research articles</li> <li>Publications listed in the PCORI database. We filtered for topic: example of engagement in health research; stakeholder involvement: patients; year: 2019</li> <li>Publications citing the <a href="https://www.bmj.com/content/358/bmj.j3453">GRIPP2 reporting checklist</a> or a <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/j.1369-7625.2010.00607.x">critical appraisal guideline to assess the quality and impact of user involvement in research</a></li> </ul> <p>After applying our inclusion and exclusion criteria, the final sample consisted of 87 publications that reported on patient involvement. Included publications were coded according to three coding schemes.</p> <p>This deposit includes the following:</p> <ul> <li>Overview of publications that did not meet our inclusion criteria across all 3 samples (BMJ, PCORI, and citation)</li> <li>Overview of publications and additional documents (if applicable) that met our inclusion criteria across all 3 samples (BMJ, PCORI, and citation)</li> <li>Coded segments and analysis for the coding scheme "Phase of involvement" across all 3 samples (BMJ, PCORI, and citation). This includes the count of each sub-code across publications and the final results table. </li> <li>Coded segments and analysis for the coding scheme "GRIPP2-SF reporting guidelines according to Staniszewska et al., 2017" across all 3 samples (BMJ, PCORI, and citation). This includes the count of each sub-code across publications and the final results table.</li> <li>Coded segments and analysis for the coding scheme "Critical appraisal tool according to Wright et al., (2010)" across all 3 samples (BMJ, PCORI, and citation). This includes the count of each sub-code across publications and the final results table.</li> </ul>
Report on a Survey among Organisers of Citizen Science Projects - Dataset and Report
<p>In this publication you will find the report on a survey among organisers of citizen science projects developed by Michael Strähle & Christine Urban (alphabetical order), Wissenschaftsladen Wien - Science Shop Vienna. You will also find, the datasets which contain the responses obtained from the very short questionnaire "VSQ" and the responses used for the report.</p>
Codes to replicate statistical analysis in UPLIFT project Deliverable 2.4 Synthesis report, Chapter 6
<p>Policies attempting to mitigate the effects of urban inequality, often disregard affected citizens’ experiences, and thus fail to achieve maximum impact. By incorporating these perspectives into the policy design process, the project "Urban PoLicy Innovation to address inequality with and for Future generaTions" (UPLIFT), funded under the EU Horizon 2020 program aims to find innovative interventions in a bottom-up approach. The aims of UPLIFT project are to understand patterns and trends of inequality across Europe and to understand how individuals experience and adapt to inequality through participatory research. Moreover the project will together with the communities in four locations, co-design a policy tool aimed at addressing and reducing inequality and socio-economic divisions. The activity and results of the project can be followed at <a href="https://www.uplift-youth.eu/">https://www.uplift-youth.eu/</a>.</p> <p>Deliverable 2.4 (Synthesis report: socioeconomic inequalities in different urban contexts) is the final deliverable of work package 2 of the UPLIFT project, which aims to synthetize the main outcomes of the urban reports that described the policy environment around vulnerable individuals in the fields of education, employment and housing in 16 functional urban areas of the EU. In addition section 6.2 "Statistical analysis of linkages between economic development of cities, their public policy performance and inequality outcomes" of the report provides a statistical analysis of how local economic competitiveness and the local policy context affect urban deprivation and inequality among the young in European cities. The analysis is based on data from 2006, 2009, 2012, 2015 and 2019 of the Quality of Life in European Cities survey.</p>
The MET-Pottery–Site Report
<p>The 'MET-Pottery–Site Report' is part of the PhD thesis submitted by C. Heitz at the University of Bern in 2018 and was coded by M. Hinz. The associated dataset was compiled within the SNSF project No 100011_156205 ‘Mobilities, Entanglements and Transformations in Neolithic Societies of the Swiss Plateau (3900-3500 BC)’, short ‘MET-project’, conducted at the Institute of Archaeological Sciences, University of Bern between 2014 and 2018 (<a href="https://data.snf.ch/grants/grant/156205">https://data.snf.ch/grants/grant/156205</a>; <a href="https://boris.unibe.ch/77649/">https://boris.unibe.ch/77649/</a>).</p> <p>It represents the largest and temporally most highly resolved collection of morphological pottery data of the Central European Neolithic. It offers data of 1046 ceramic vessels of different styles that originate out of 44 archaeological features of wetland and dryland sites of the northern Alpine Space and adjacent regions. Most of the archaeological contexts – anthropogenic layers of settlements, pits, and ditches – are typology-independent dated using dendrochronology or C14-dates. The data set includes a spreadsheet of nominal and numeric morphological variables, the collection of the vessels’ semi-profile silhouettes and the typological drawings from which all data was collected. In the scope of the MET-project ‘the data was used to elaborate a new mixed method research (MMR) methodology to investigate social relations beyond problematic concepts of homogeneous ‘archaeological cultures’. It is highly relevant for further methodological morphology-based research on pottery.</p>
Tagged Twitter timelines for users reporting SARS-CoV-2 infections and related data
<p>Twitter data was collected through the Twitter API v2.0, specifically through the timeline endpoint. Details about the inference of SARS-CoV-2 self-reports and the tagging of the full timeline of each user can be found in the <a href="https://github.com/digitalepidemiologylab/content_changes_paper">GitHub repository</a>. The larger dataset ("preprocessed_data.csv") consists of a total of 8,534,171 tweets posted by 30,856 users from January 1, 2020 to October to September 30, 2021.</p> <p>The raw data from Twitter, including tweet and user IDs, has been removed or anonymized in order to comply with the EPFL guidelines for data sharing.</p> <p>In particular, the date of the tweets was removed, the text of the tweets, URLs and URL domains have been substituted with the "text", "<URL>" and "<URL_DOMAIN>" token respectively.</p> <p>User IDs have been substitued with new IDs in the [0, number of users] range (e.g. U0, U1, ...) .</p> <p>Tweet IDs have been substitued with new IDs in the [0, number of tweets] range (e.g. T0, T1, ...) .</p> <p>In addition to self-explanatory columns about topics, emotions, URL classification and symptoms we tagged, we also share the columns:</p> <ul> <li>pdate: date of the SARS-CoV-2 infection self-report for that user (adjusted with SUTime)</li> <li>effective_date: date of the tweet adjusted with SUTime, when the SUTime columns is available.</li> <li>rel_effective_day(week, month): days (weeks, months) computed with respect to the positivity date (i.e. "pdate" column). Negative numbers refer to tweets posted before the user reported a COVID-19 infection on Twitter.</li> </ul>
Datasets for "Targeted insertion and reporter transgene activity at a gene safe harbor of the human blood fluke, Schistosoma mansoni"
<p>To identify sites that could serve as potential genomic safe harbours (GSHs) for transgene integration, we conducted a genome-wide bioinformatic search based on established, widely accepted criteria, along with newly introduced criteria (below), that would satisfy benign and stable gene expression. </p> <p>At the outset, we identified <strong>euchromatic</strong> regions in all developmental stages of <em>S. mansoni </em>to avoid silencing genes to be integrated upon CRISPR/Cas manipulation. With these criteria, we enriched for regions that were, (i) close to peaks of H3K4me3, a histone modification that is associated with euchromatin and transcription start sites, (ii) regions that did not include H3K27me3, a histone modification that is associated with heterochromatin, (iii) regions of open euchromatin accessible to Tn5 integration, in an Assay of Transposase Accessible Chromatin sequencing (ATAC-seq) providing a positive display of integration events, and (iv) given that HIV-1 integrates preferentially into euchromatin in human cell lines, we used sites of HIV proviral integration known from <em>S. mansoni</em> to likewise support predictions of euchromatic regions.</p> <p>Examination of the draft genome of <em>S. mansoni</em> in Worm Base Parasite, version 7 (WormBase Parasite) identified 6,884 regions with enrichment of H3K4me3 in the absence of H3K27me3 in available developmental stages (H3K4me3 not K3K27me3). In mature, adult schistosomes, we found consistently 10,533 ATAC positive regions. There were 4,027 ATAC regions that overlapped with H3K4me3 but not K3K27me3, and 2,915 genes overlapped with (ATAC and H3K4me3 not H3K27me3). Forty-two unambiguous HIV integration sites were identified, and eight genes were ≤ 11 kb upstream or downstream from these integration sites. Repeats were masked with RepeatMasker V4.1.0 using a specific repeat library produced with RepeatModeler2 V2.0.1 and stored as a GFF file.</p> <p>To identify intergenic GSH, we located 10,149 intergenic regions. There were 9,985 regions beyond 2 kb upstream and 8,837 regions outside long non-coding-RNA (lncRNA), which were intersected to 95,587 unique intergenic regions outside 2 kb and lncRNA of ≥100 bp. Two hundred regions were identified intersecting with merged ATAC H3K4me3 signal. Four of these were situated ≤ 11 kb distance from HIV integration sites. </p> <p>Made at George Washington University, Justus Liebig University Giessen, Khon Kaen University, Naresuan University, Aberystwyth University, Schistosomiasis Resource Center, IHPE. </p>
GIRT-Data: Sampling GitHub Issue Report Templates
<p><strong>GIRT-Data</strong> is the first and largest dataset of <strong>issue report templates (IRTs)</strong> in both YAML and Markdown format. This dataset and its corresponding open-source crawler tool are intended to support research in this area and to encourage more developers to use IRTs in their repositories. The stable version of the dataset, containing <code>1_084_300</code> repositories, that <code>50_032</code> of them support IRTs.</p> <p>For more details see the GitHub page of the dataset: <a href="https://github.com/kargaranamir/girt-data">https://github.com/kargaranamir/girt-data</a></p> <p><br> The dataset is accepted for <a href="https://conf.researchr.org/track/msr-2023/msr-2023-data-showcase">MSR 2023</a> conference, under the title of "GIRT-Data: Sampling GitHub Issue Report Templates" <a href="https://scholar.google.com/scholar?q=GIRT-Data:+Sampling+GitHub+Issue+Report+Templates">Search in Google Scholar</a>.</p>
HyUSPRe Report & Data on 'New experimental data on reactions between H2 and well cement and effects on fluid flow and mechanical properties of well cement
<p>In this study, new experimental data is presented of the effects of H<sub>2</sub> exposure and cyclic loading on mechanical properties of oil well (class G) cement, relevant for underground hydrogen storage operations. Changes in mechanical properties (Young’s modulus, Poisson’s ratio and ultimate strength) have been analyzed using unconfined compressive strength (UCS) tests and confined cyclic loading tests on class G cement samples that were unreacted (cured for 3 days at 80°C) and exposed to lime-saturated brine and N<sub>2</sub> or H<sub>2</sub> for 1 and 2 months. Changes in cement mineralogy were analyzed by XRD analysis of the unreacted and exposed samples. The mechanical properties of elastic modulus and Poisson’s ratio are within the expected range of an oil well cement. Differences in Young’s modulus, Poisson’s ratio and ultimate strength are limited between unreacted, N<sub>2</sub>-exposed and H<sub>2</sub>-exposed samples, when comparing UCS tests or confined cyclic loading tests. Repeated UCS tests seem to indicate that the variation in Young’s modulus and ultimate strength increases after N<sub>2</sub> and H<sub>2</sub> exposure, but this observation needs to be confirmed in additional tests. During cyclic axial loading of confined cement samples, irreversible (plastic) deformation (compaction) occurs that affect static Young’s modulus. Also, effects of exceeding yield and failure strength on Young’s modulus are observed. Dynamic Young’s moduli and Poisson’s ratios derived from acoustic velocity measurements during confined cyclic tests show limited variation, in particular if static and dynamic Young’s modulus are compared. The mineralogical changes as identified using XRD analysis suggest minor changes between unexposed and H<sub>2</sub>- and N<sub>2</sub>-exposed samples, although XRD patterns indicate some minerals that could not be identified. The main conclusion is that effects of H<sub>2</sub> exposure and cyclic loading on mechanical properties and mineralogical changes of class G cement is limited compared to unreacted or N<sub>2</sub> exposed samples for the investigated conditions. There is no indication that changes in mechanical properties of cement are such that cement integrity of wells used for underground hydrogen storage will be significantly affected. It should be emphasized that this conclusion is based on experiments on one type of cement (class G) and a limited set of conditions. In particular, additional tests to assess the reproducibility of current results and tests on samples that were exposed longer to H<sub>2</sub> and N<sub>2</sub> are of interest. Detailed effects of changing properties for the durability and integrity of wells can be derived by performing a parameter sensitivity analysis with well integrity modelling for the range in mechanical properties measured in this study.</p>
PretoxTM Corpus: a gold standard corpus of preclinical treatment-related findings annotated from toxicology reports
<p>The PretoxTM Corpus is a gold standard corpus of preclinical treatment-related findings annotated from toxicology reports.</p> <p>Example documents annotated by domain experts, also known as a gold standard corpus, are needed in order to develop, train, and validate text mining tools. To this aim we designed and performed an annotation activity for the development of the corpus of treatment-related findings: the PretoxTM corpus.</p> <p>A treatment-related finding expression enclose several named entities; the most relevant one is the abnormal effect detected; which depending on the study domain of the finding, can be given by a measurement, test, or examination named Study Test and an abnormal Manifestation result obtained for that study test; or by an abnormal Finding in study domains where there is no associated test or measurement (e.g., clinical, macroscopic and microscopic). Other related named entities that could be present to complete the treatment-related finding are; the Specimen of the abnormal observation, the Sex of the subject, the Group of subjects in which the observation was detected and the Dose level administration of the compound. Examples of sentences with treatment-related findings are: “The decrease in food consumption and body weight of the animals from the mid dose onwards is regarded as evidence of general toxicity.” and "At dose level 3, absolute and relative liver weights were increased in male rats.”.</p> <p>Contributions: The PretoxTM corpus was developed by BSC, with the contribution of IMIM and a team of experts from the eTRANSAFE EFPIA partners.</p> <p>License: Creative Commons Attribution-ShareAlike 4.0 International (cc by sa 4.0).</p> <p>The PretoxTM resources have been developed as part of the eTRANSAFE project.</p> <p>For more information about PretoxTM please visit:</p> <p>PretoxTM Corpus Gitlab: <a href="https://gitlab.bsc.es/inb/etransafe/preclinical-toxicological-corpus/">https://gitlab.bsc.es/inb/etransafe/preclinical-toxicological-corpus/</a></p> <p>PretoxTM central documentation: <a href="https://pretoxtm.gitlab.io/documentation/">https://pretoxtm.gitlab.io/documentation/</a></p>
Dataset for Measurement report: Ion clusters as indicator for local new particle formation
<p>Data for Measurement report: Ion clusters as indicator for local new particle formation. There are two files, negative_ion_concentrations.csv and positive_ion_concentrations.csv. The former (latter) includes absolute number concentrations for 1.87, 2.16, 2.49, and 2.88 nm negative (positive) ions. The unit for these concentrations is #/cm<sup>-3</sup>.</p> <p>Contact Santeri Tuovinen (santeri.tuovinen@helsinki.fi) for more details.</p>
Dataset - What are the Machine Learning best practices reported by practitioners on Stack Exchange?
<p>The data correspond to the posts (questions and answers) retrieved by querying for posts related to the tag 'machine learning' and the phrase 'best practice(s).' The data were used as the basis for a study currently under review on discussing machine learning best practices as discussed by practitioners in question-and-answer communities such as Stack Exchange. The information from each type of post (i.e., questions and answers) is presented in multiple formats (i.e., .txt, .csv, and .xlsx).</p> <p> </p> <p><strong>Answers - Variables</strong></p> <ul> <li><strong>AID</strong>:<strong> </strong> Unique identification of the answer in the Q&A website.</li> <li><strong>ParentId</strong>: Unique identification of the question associated with the answer in the Q&A website </li> <li><strong>AcceptedAnswerId</strong> : In the case in which an answer is the most voted question associated with the <em>ParentId</em>, and it is different from the accepted answer, a different identifier from the <em>AID</em> is available. In the case in which the accepted question had a <em>score</em> lower than 1, a -1 is assigned. </li> <li><strong>ABody:</strong> HTML text of the answer.</li> <li><strong>Score:</strong> Upvotes - downvotes of the answer.</li> <li><strong>url_Answer:</strong> URL of the answer. The question URL can be from different websites. </li> <li><strong>type:</strong> best or accepted. Accepted in the case that the information belongs to the accepted answer of the <em>ParentId </em>question and best in the case in which it is the most voted question of the <em>ParentId </em>question.</li> <li><strong>Date: </strong>Creation date of the answer.</li> </ul> <p><strong>Questions - Variables</strong></p> <ul> <li><strong>QID</strong>: Unique identification of the question in the Q&A website. </li> <li><strong>AcceptedAnswerId</strong>: Unique identification of the accepted answer for a specific question in the Q&A website. In the case in which a question had a most-voted answer different from the accepted one, and the accepted one had a negative score, a -1 was assigned to the <em>AcceptedAnswerId</em><strong>. </strong></li> <li><strong>BestAnswerId</strong>: Unique identification of the most voted answer for a specific question in the Q&A website. In the case in which the most voted and accepted questions were the same, then a -1 was assigned to the <em>BestAnswerId</em>. </li> <li><strong>Qtitle</strong>: Title of the question.</li> <li><strong>QBody</strong>: HTML text of the question.</li> <li><strong>Score</strong>: Upvotes - downvotes of the questions.</li> <li><strong>QTags</strong>: Tags that are associated with each question.</li> <li><strong>url_question</strong>: URL of the question. The question URL can be from different websites. </li> <li><strong>Date</strong>: Creation date of the question</li> </ul> <p>This dataset is a subset of the Stack Exchange dump of 03.2021 (<a href="https://archive.org/details/stackexchange_20210301">https://archive.org/details/stackexchange_20210301</a>) in which a series of filters were applied to obtain the data used in the study.</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.