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3,172 results for “public”
Brazilian Federal legislations and Health Professional Councils regulations about telemedicine, according to historical phases and public policy purposes from 1990 to 2018.
<p>The file contains one excel file with two datasheets, one with legislations from Brazilian Federal Government and other from Health Professional Councils, from 1990 to 2019. Each spreadsheet has the original database, the number ID of the normative, what institution the document is from, its publication date, the abstract (in Portuguese), its public URL, historical phases and the purpose of it.</p>
Experimental data to the publication "Genetic-optimised aperiodic code for distributed optical fibre sensors"
<p>The source data underlying Figs. 3-5 and Supplementary Figs. 6, 8-14 are provided as a Source Data file.</p>
Data set associated to the publication "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology"
<p>Data set of the scientific publication entitled "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology":</p> <p>Seismological sensors</p> <p>Microphones</p> <p>Barometers</p> <p>Accelerometers</p> <p>Detailed test report.</p>
Sistematic Mapping review of literature about public policies innovation and reading competences
<p>The systematic mapping method makes possible to identify what is known about a topic, what has been researched, what aspects remain unknown, and to obtain information about current trends and future challenges regarding this topic. In this Excel , the following articles were included: studies on innovative public policies related to reading skills, those published only in journals that can be found in two databases: Web of Science (WOS) and Scopus, published from January 2015 to December 2019, and related to the educational sector (social sciences, psychology, multidisciplinary, educational research, medical education, environmental education).</p>
Data for the publication "How frequent is natural cloud seeding from ice cloud layers (<-35°C) over Switzerland?"
<p>This repository contains the data for the paper:</p> <p>Authors: Ulrike Proske, Verena Bessenbacher, Zane Dedekind, Ulrike Lohmann, and David Neubauer<br> Titel: How frequent is natural cloud seeding?<br> Date: 2020</p> <p>Note that the scripts arezeno to be found in the accompanying package (https://doi.org/10.5281/zenodo.3987754)</p>
Brazilian Scientific Publication Records and Author Affiliations from Lattes until Feb 2017 (Anonymized)
<p>This file contains anonymized data about researcher profiles and publication records available extracted from the Lattes Platform in in February 2017 using the LattesDataXplorer tool. Lattes is a vast repository of researchers' curriculum vitae, widely adopted in Brazil. This platform is maintained by the Brazilian National Council of Scientific and Technological Development (CNPq) and is an internationally renowned initiative.</p> <p>In the zipped file, there are two files:</p> <ul> <li>anon_authors.csv contains data about researchers. It has 3 columns <ul> <li>profile: researcher anonymized id</li> <li>instituition: researcher affiliation</li> <li>zipcode: institution zip code</li> </ul> </li> </ul> <ul> <li>anon_papers.csv contains data about researchers' publications. It has 4 columns: <ul> <li>profile: researcher anonymized id</li> <li>year: publication year</li> <li>venue: publication venue</li> <li>authors: number of authors</li> </ul> </li> </ul>
Dataset for Freedom to Choose between Public Resources Promotes Cooperation
<p>This dataset contains Matlab codes and Matlab data for "Freedom to choose between Public Resources promotes Cooperation". For a description of the dataset see the text file, "Description", in the dataset.</p>
Disease and lesion maps - part of the Scientific opinion on the evaluation of public and animal health risks in case of a delayed post-mortem inspection in ungulates
<p>EFSA was requested to assess the impact on effectiveness of <em>post-mortem</em> inspection in terms of any change in the sensitivity of detection of animal diseases of domestic and wild ungulates listed according to Article 5 of Regulation (EC) No 2016/429 and septicaemia, pyaemia, toxaemia or viraemia, when carried out after up to 24 hours or up to 72 hours after slaughter in comparison to when it is carried out immediately after slaughter. In order to identify the main lesions associated with the target diseases, a so called “disease map” was built, with the information about the clinical forms of the diseases (acute, subacute, chronic, or latent forms) and clinical signs, as well as potential lesions that could be observed on the respective diseased animals.</p> <p>The following information is indicated for each disease/condition in the disease map:</p> <ol> <li>The susceptible animal species</li> <li>Whether there is any surveillance programme in place in the EU</li> <li>The signs associated with the disease that could be detected at <em>ante mortem </em>inspection;</li> <li>The lesions associated with the disease that could be detected at <em>post mortem</em> inspection</li> <li>The probability of detecting the disease during the PMI as normally carried out.</li> <li>Whether carcass swabbing and/or laboratory tests are normally carried out.</li> </ol> <p>From the disease map, the list of organs to be considered at <em>post mortem</em> inspection and the lesions, a “lesion map” was built by connecting animal species with organs, lesions and corresponding disease. This was done to facilitate the construction of a questionnaire where, for each organ and the five types of lesions, the respondents (meat inspectors) had to provide a numerical answer about how many carcasses out of 100 with the given lesion, will be still detected after 24- or 72-h of refrigerated storage.</p>
Dataset related to publication "Heating of hip joint implants in MRI: the combined effect of radiofrequency and switched-gradient fields"
<p>The datasets reported in the figures of the article "Heating of hip joint implants in MRI: the combined effect of radiofrequency and switched-gradient fields" by Alessandro Arduino, Umberto Zanovello, Jeff Hand, Luca Zilberti, Rüdiger Brühl, Mario Chiampi and Oriano Bottauscio, accepted for publication to Magnetic Resonance Imaging on Dec. 9th, 2020. A link to the article will be given after its final publication.</p>
Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite"
<p>Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite", by Paulina Szymoniak, Brian R. Pauw, Xintong Qu, and Andreas Schönhals.</p> <p>Datasets are in three-column ascii (processed and azimuthally averaged data) from a Xenocs NanoInXider SW instrument. Monte-Carlo analyses were performed using McSAS 1.3.1, other analyses are in the Python 3.7 worksheet. Graphics and result tables are output by the worksheet. </p>
Datasets of Twitter mentions and publications in Information Science & Library Science and Microbiology
<p>Datasets used in the study 'Identifying and characterizing social media communities: a socio-semantic network approach to altmetrics'.</p> <p><strong>Microbiology publications (mic_publiccations.tsv).</strong> Dataset of 101,206 Microbiology publications with their author keywords.</p> <p><strong>Microbiology mentions (mic_mentions.tsv).</strong> Dataset of 328,110 Twitter mentions to Microbiology publications.</p> <p><strong>Information Science & Library Science publications (lis_publications.tsv).</strong> Dataset of 8452 Information Science & Library Science publications with their author keywords.</p> <p><strong>Information Science & Library Science mentions (lis_mentions.tsv).</strong> Dataset of 35,411 Twitter mentions to Information Science & Library Science publications.</p>
Piburgersee core meta data repository for the publication "Seismic control of large prehistoric rockslides in the Eastern Alps"
<p>This dataset comprises the core meta data of Plansee, which is the basis for the publication Oswald et al. "Seismic control of large prehistoric rockslides in the Eastern Alps".</p> <p>The core meta data belongs to a 8m long sediment core composed of 12 individual core sections (see Plansee_core_data.xlsx). For each individual core section the core image (_coreimage.jpg), the CT data (_CT.rar), XRF data, (_XRF.txt) and multi-sensor core logging data (_MSCL.csv) are provided.</p>
Plansee seismic and core meta data repository for the publication "Seismic control of large prehistoric rockslides in the Eastern Alps"
<p>This dataset comprises the raw seismic data and core meta data of Plansee, which is the basis for the publication Oswald et al. "Seismic control of large prehistoric rockslides in the Eastern Alps".</p> <p>Seismic profiles are provided as .SGY files (Plansee_seismics_SGYfiles.rar)</p> <p>The core meta data belongs to a 7m long sediment core composed of 10 individual core sections (see Plansee_core_data.xlsx). For each individual core section the core image (_coreimage.jpg), the CT data (_CT.rar) and multi-sensor core logging data (_MSCL.csv) are provided.</p>
Values and attitudes associated with public support for biodiversity and ecosystem services as goals of ecological restoration
<p>Source data and metadata for "Public support for restoration: Does including ecosystem services as a goal engage a different set of values and attitudes than biodiversity protection alone?", accepted to PLOS One in December 2020. </p>
Dataset for English Health-Related Advice Directed to the General Public on Twitter During the Early Spread of COVID-19 [Dataset]
<p>Health-related advice directed to the public on twitterprovides insight into the use of social media duringa pandemic. This paper describes our data collection, sampling, and analysis of 44 million tweets in English in March 2020. We make reference to a parallel dataset and analysis of tweets in Arabic during thesame period. The contribution of this paper is a description of our dataset, our coding process to indicate tweets with health related advice, and our analysis and comparisons of the characteristics of the tweets with and without health-related advice. These contributions providethe basis for future research on semi-automated classifiers for health-related advice and efforts to reduce thespread of harmful health advice.</p>
AraHealth: A Dataset for Arabic Health-Related Advice Directed to the General Public on Twitter During the Early Spread of COVID-19 [Dataset]
<p>Health-related advice directed to the general public on Twitter provides insight into the use of social media during health emergencies. This paper describes our data collection, sampling, and analysis of 24 million tweets in Arabic in March and early April 2020. We make reference to a parallel dataset and analysis of tweets in English during the same period. The contribution of this paper is a description of our dataset, our coding process to indiciate tweets with health related advice, and our analysis and comparisons of the characteristics of the tweets with and without health-related advice. These contributions provide the basis for future research on semi-automated classifiers for health-related advice and efforts to reduce the spread of harmful health advice.</p>
Publication rate and consistency of registered trials of motor-based stroke rehabilitation
<p><strong>Table e1 - Eligible records</strong></p> <p>A list of registered randomized controlled trials (RCTs) meeting the following criteria:</p> <ol> <li>RCTs of motor-based interventions in individuals with stroke (including transient ischemic attack);</li> <li>Started on or after 1 July 2005;</li> <li>Completed before 1 April 2017 (actual or expected end date);</li> <li>Included human adult participants (≥18 years old);</li> <li>Included at least one outcome (primary or secondary) related to motor control, mobility, or physical functioning and performance of upper and/or lower extremities or the body as a whole; and</li> <li>Registered in English.</li> </ol> <p>This list was obtained by searching the following registries between 23 November 2017 and 22 February 2018: the International Clinical Trials Registry Platform, Clinicaltrials.gov (USA), Australian New Zealand Clinical Trial Registry, Chinese Clinical Trial Registry, Clinical Research Information Service (Republic of Korea), Clinical Trial Registry of India, Cuban Public Registry of Clinical Trials, European Union Clinical Trials Register, German Clinical Trials Register, Iranian Registry of Clinical Trials, International Standard Randomised Controlled Trials Number registry (UK), Center for Clinical Trials-Japan Medical Association, University Hospital Medical Information Network-Clinical Trial Registry (Japan), Thai Clinical Trials Registry, Netherlands Trials Registry, Pan African Clinical Trials Registry, Peruvian Clinical Trials Registry, and Sri Lanka Clinical Trials Registry. </p> <p><em>Variable definitions</em></p> <p>UIN: Unified identification number (obtained from the trial registry)</p> <p>Status: whether or not a peer-reviewed publication reporting the trial findings for the primary outcome/outcome was found</p> <ul> <li>Paper available: a publication reporting the trial findings for the primary outcome/objective was found</li> <li>Paper available (not English): a publication, published in a language other than English, reporting the trial findings for the primary outcome/objective was found</li> <li>Secondary paper available: a publication reporting study findings is available, but not for the primary objectives/outcome</li> <li>Results available: trial findings for the primary objective/outcome are available in a non-peer reviewed format (e.g., conference publication, non-peer reviewed journal, or uploaded to the trial registry)</li> <li>Discontinued: the trial registry record indicates that the trial was discontinued, so no publication is expected</li> <li>No paper/results: none of the above apply</li> </ul> <p>Source: database or method we used to find the publication reporting the trial findings for the primary objective/outcome</p> <ul> <li>Pubmed: the publication was found by searching for the UIN in Pubmed</li> <li>EMBASE/OVID: the publication was found by searching for the UIN in Embase or OVID Medline</li> <li>Google Scholar: the publication was found by searching for the UIN in Google Scholar</li> <li>Registry: the publication was listed in the trial registry</li> <li>Internet: the publication was found by a superficial internet search for the UIN</li> <li>Protocol: the publication was found through a cited reference search for the published protocol</li> <li>Author: the publication was found by contacting the trial investigators</li> <li>Other: the publication was found by some other means</li> <li>None: no publication reporting the trial findings for the primary objective/outcome was found</li> </ul> <p><strong>Table e2 - Published papers consistency</strong></p> <p>The subset of trials from Table e1 where an English-language publication reporting the trial findings for the primary objective/outcome was found.</p> <p><em>Variable definitions</em></p> <p>UIN: Unified identification number (obtained from the trial registry)</p> <p>DOI: digital objective identifier of the publication</p> <p>First_author: last name of the first author of the publication</p> <p>Year: year of the publication</p> <p>Journal: journal of the publication (abbreviated journal names are used, where available)</p> <p>Reg_Trial_End_Date: end date of the trial, as stated in the trial registry (format DD-MMM-YY)</p> <p>Date_Submitted: date when the paper was submitted to the journal for publication (where available; format: DD-MMM-YY)</p> <p>Time_To_Submit: difference, in days, between Reg_Trial_End_Date and Date_Submitted</p> <p>Date_Published: date when the paper was published, either online or in print, whichever is earlier (format: DD-MMM-YY)</p> <p>Time_To_Publish: difference, in days, between Reg_Trial_End_Date and Time_To_Publish</p> <p>UIN_Paper_Location: location of the UIN in the publication</p> <p>Reg_Pilot: whether the trial was defined as a pilot or feasibility study in the registry record (0=no, 1=yes)</p> <p>Paper_Pilot: whether the trial was defined as a pilot or feasibility study in the publication (0=no, 1=yes)</p> <p>Consistency_pilot: whether Reg_Pilot = Paper_Pilot (0=no, 1=yes)</p> <p>Consistency_Primary_Objective: whether the trial registry record and publication were consistent in terms of the primary objective (0=no, 1=yes)</p> <p>Consistency_Primary_Outcome: whether the trial registry record and publication were consistent in terms of the primary outcome (0=no, 1=yes)</p> <p>Target_N: target sample size, as indicated in the trial registry record</p> <p>Paper_N: number of participants recruited to the study, as indicated in the publication</p> <p>Consistency_N: whether the trial registry record and publication were consistent in terms of the sample size (i.e., Paper_N is within +/-10% of Target_N; 0=no, 1=yes)</p> <p>N_direction: for those trials that were inconsistent in terms of the sample size, whether Paper_N was less than (Under) or more then (Over) Target_N</p> <p>Eligibility_Consistency: whether the trial registry record and publication were consistent in terms of the eligibility criteria</p> <p>UIN_in_paper: the UIN that was included in the paper, exactly as it was published</p> <p>UIN_consistency: whether UIN = UIN_in_paper (0=no, 1=yes)</p> <p>Reg_Type: whether the trial was registered before recruiting the first participant (Prospective), after recruiting the first participant but before the trial was completed (Retro - pre-completion), or after the trial was completed (Retro - post-completion)</p> <p><strong>Table e3 - Inconsistency details</strong></p> <p>The subset of trials from Table e2 where the trial registry record and publication were inconsistent on only one of the criteria examined. This table provides further details of these inconsistencies, and details of any explanations for changes to the protocol since registration, if available.</p>
Softcite Dataset: A dataset of software mentions in research publications
<p>The Softcite dataset is a gold-standard dataset of software mentions in research publications, a free resource primarily for software entity recognition in scholarly text. This is the first release of this dataset.</p> <p><strong>What's in the dataset</strong></p> <p>With the aim of facilitating software entity recognition efforts at scale and eventually increased visibility of research software for the due credit of software contributions to scholarly research, a team of trained annotators from Howison Lab at the University of Texas at Austin annotated 4,093 software mentions in 4,971 open access research publications in biomedicine (from PubMed Central Open Access collection) and economics (from Unpaywall open access services). The annotated software mentions, along with their <em>publisher</em>, <em>version</em>, and access <em>URL</em>, if mentioned in the text, as well as those publications annotated as containing no software mentions, are all included in the released dataset as a TEI/XML corpus file.</p> <p>For understanding the schema of the Softcite corpus, its design considerations, and provenance, please refer to our paper included in this release (preprint version).</p> <p><strong>Use scenarios</strong></p> <p>The release of the Softcite dataset is intended to encourage researchers and stakeholders to make research software more visible in science, especially to academic databases and systems of information retrieval; and facilitate interoperability and collaboration among similar and relevant efforts in software entity recognition and building utilities for software information retrieval. This dataset can also be useful for researchers investigating software use in academic research.</p> <p><strong>Current release content</strong></p> <p><em>softcite-dataset v1.0</em><strong> </strong>release includes<strong>:</strong></p> <ul> <li>The Softcite dataset corpus file: softcite_corpus-full.tei.xml</li> <li><em>Softcite Dataset: A Dataset of Software Mentions in Biomedical and Economic Research Publications</em>, our paper that describes the design consideration and creation process of the dataset: Softcite_Dataset_Description_RC.pdf. (This is a preprint version of our forthcoming publication in the Journal of the Association for Information Science and Technology.)</li> </ul> <p>The Softcite dataset is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p>If you have questions, please start a discussion or issue in the <a href="https://github.com/howisonlab/softcite-dataset">howisonlab/softcite-dataset Github repository</a>.</p>
Nitrogen publications in the Yaqui Valley (1990-2020)
<p>This database contains categorized publications based on an organized search of scientific literature published in Springer (https://www.springer.com/la), Science Direct (https://www.sciencedirect.com/), sciElo (https://scielo.org/), Redalyc (https://www.redalyc.org/), International Maize and Wheat Improvement Center (CIMMYT) Repository (https://www.cimmyt.org/) and Sonora Institute of Technology (ITSON) library (http://sib2.itson.mx/cgi-bin/koha/opac-main.pl) databases. The Google Scholar search was also used to retrieve as many publications as possible; however, we considered the database origin for the analysis. The search was limited in terms of the used databases, since they were chosen for being among the most complete on national and international publications related to the research topic</p>
Review of OpenStreetMap research publications from 2016 to 2019
<p>This dataset consists of a <strong>review of research publications about the <a href="https://www.openstreetmap.org/">OpenStreetMap (OSM) project</a> </strong>published from 2016 to 2019.</p> <p>The dataset was obtained as follows. First, papers published between 2016 and 2019 were extracted using <a href="https://scholar.google.it/">Google Scholar</a> with a query identifying all records with at least one of the keywords “OpenStreetMap” and “OSM” in the title. The extracted records were further filtered to only keep papers having a minimum length of 4 pages and published in academic journals or conference proceedings. In addition, irrelevant papers (e.g. using “OSM” as an acronym for another purpose) and non-English papers were removed from the dataset. The remaining paper were then analyzed and manually classified.</p> <p>The attributes included in the dataset are the following:</p> <ul> <li><strong>id</strong> [paper ID]</li> <li><strong>Paper Citation </strong>[citation of the paper]</li> <li><strong>Authors’ disciplines</strong> — multiplicity: 1-7; allowed values: computer science, informatics, social sciences, geo-information, engineering, exact sciences, interdisciplinary</li> <li><strong>Journal’s discipline</strong> — multiplicity: 1; allowed values: computer science, informatics, social sciences, geo-information, engineering, exact sciences, interdisciplinary</li> <li><strong>Topic(s)</strong> — multiplicity: 1:10; allowed values: application, data quality, contribution behaviours, analyzing contributions, contributors, shaping contributions, OSM effects, data enrichment, development, review</li> <li><strong>Authors' Geography (countries) </strong>— multiplicity: 1:n; allowed values: [list of countries]</li> <li><strong>Authors' Geography (continents) </strong>— multiplicity: 1:n; allowed values: [list of continents]</li> <li><strong>Study Area Geography (countries)</strong> — multiplicity: 1:n; allowed values: [list of countries], [list of continents], Global, NA</li> <li><strong>Geographic Correspondence</strong>— multiplicity: 1; allowed values: no correspondence, partial affiliation/partial location, partial affiliation/full location , full correspondence, NA</li> <li><strong>Perspective on the community</strong> — multiplicity: 1; allowed values: OSM as a data source, data produced by contributors, a collaborative project based on contributors, contributors producing data, a unified community, a diverse community, a social product</li> <li><strong>Evidence of engagement </strong>— multiplicity: 1-8; allowed values: none, acknowledgement, meaningful development, occasional development, object of study - direct, object of study - indirect, contribution, participation</li> <li><strong>General comments </strong>[comments on the paper]</li> </ul>
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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.