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557 results for “data reporting”
Dataset for ELGO-DIMITRA Data Management Practices & Requirements: A Scoping Report
<p>This is a comprehensive data repository of the <em>data management survey</em> carried out in Autumn of 2023 through a collaboration between the <a href="https://opensciencestudies.eu/">PHIL_OS</a> project and the <a href="https://agres.elgo.gr/">Research Directorate of the Hellenic Agricultural Organization ELGO-DIMITRA</a>.</p> <p>Please cite as: </p> <blockquote> <p>Tsiroukis F., Leonelli S. and ELGO-DIMITRA (2024) <em>Dataset for ELGO-DIMITRA Data Management Practices & Requirements: A Scoping Report.</em> PHIL_OS Report. DOI: 10.5281/zenodo.14003418</p> </blockquote>
Data for SARS-CoV-2 Reinfection Trends in South Africa: Monthly Report (2022-12-07)
<p>This version contains a single file, with time series data for the most recent <a href="https://www.nicd.ac.za/diseases-a-z-index/disease-index-covid-19/surveillance-reports/sarscov2-reinfection-trends-in-south-africa-monthly-report/">monthly report on SARS­-CoV-­2 Reinfection Trends in South Africa</a>:</p> <ul> <li><code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>)</li> </ul> <p>Note: There may be some inconsistencies with the numbers of infections through time in earlier versions of this data set due to back-filling of late-arriving data.</p> <p> </p> <p>Note: Earlier versions of this data set included data files for Pulliam, JRC, C van Schalkwyk, B Lombard, N Govender, A von Gottberg, C Cohen, MJ Groome, J Dushoff, K Mlisana, and H Moultrie. <a href="https://www.science.org/doi/10.1126/science.abn4947">Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa</a>. DOI: 0.1126/science.abn4947</p> <p>For code and more details see: <a href="https://github.com/jrcpulliam/reinfections/releases/tag/v3.0">https://github.com/jrcpulliam/reinfections/releases/tag/v3.0</a> or <a href="https://zenodo.org/record/6108448">10.5281/zenodo.6108448</a></p> <p>The version of this data set associated with the publication (available via the links above) included the following files:</p> <ul> <li><code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>)</li> <li><code>demog_data.csv</code> - counts of individuals eligible for reinfection (<code>total</code>), who have 0 suspected reinfections (<code>no_reinf</code>) or >0 suspected reinfections (<code>reinf</code>) by province (<code>province</code>), age group (5-year bands, <code>agegrp5</code>), and sex (M = Male, F = Female, U = Unknown, <code>sex</code>)</li> <li><code>posterior_90_null.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript)</li> <li><code>sim_90_null.RDS</code> - simulation results (as used in the manuscript)</li> <li><code>emp_haz_sens_an.RDS</code> - output of sensitivity analysis of relative empirical hazard estimation to assumed observation probabilities (as used in the manuscript)</li> </ul>
Wearable data and self reported fatigue scores from a remote observational study in Sjogren's disease, SLE and healthy participants
<p>Fatigue is a subjective, complex, and multi-faceted phenomenon, commonly experienced as tiredness. However, pathological fatigue is a major debilitating symptom associated with overwhelming feelings of physical and mental exhaustion. To date, there is no consensus about reliable quantitative assessments of fatigue.</p> <p>We collected observational data for a period of one month from 296 participants (healthy volunteers, Sjogren’s Syndrome, and Systemic Lupus Erythematosus patients) in the United States. Data comprised continuous multimodal digital data from Fitbit, including heart rate, physical activity, and sleep daily features, and app-based daily and weekly questions (e.g., pain, mood, general physical activity, and fatigue). When matching both sensor data and PROs, and excluding missing data, the dataset contains data from 183 subjects and 3950 recording days.</p> <p>The analysis of the association of digital data to self-reported fatigue was published at <em><strong>Rao C., et. al. (2023), Association of digital measures and self-reported fatigue: a remote observational study in healthy participants and participants with chronic inflammatory rheumatic disease, Frontiers in Digital Health</strong></em>.</p> <p>Demographics, digital parameters, and other information on this dataset can be found in the aforementioned manuscript and related supplementary material. Details on the data files can be found under README.txt.</p>
MCR LTER: Coral Reef: Growth-predation risk trade-offs constrain the local distribution of a thicket-forming staghorn coral to marginal reef habitats; Data for Ladd et al., 2025, Scientific Reports.
This dataset is in support of the manuscript: Growth-predation risk tradeoffs constrain the local distribution of a thicket-forming staghorn coral to marginal reef habitats. These data were collected to 1) document how Acropora pulchra is distributed around the island of Moorea, and 2) to better understand the ecological processes that shape that distribution. Data include 1) results from surveys around the island of Moorea documenting the presence and size distribution of Acropora pulchra thickets, 2) results from an experiment measuring the growth and survivorship of Acropora pulchra fragments in the presence and absence of fish predators at nearshore fringing reef sites and adjacent sites in the mid lagoon (n = 20 sites in total), and 3) ancillary data on nitrogen content and dN15 in the tissue of the macroalgae Turbinaria ornata, sediment accumulation, and corallivore biomass at the experimental sites. All data were collected in 2016 and 2017.
IPBES Data Management Tutorials - Session 3.4: Data management report details: File formats
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management reports </em>chapter provides an overview and discussion of specific elements of IPBES data management reports.</p> <p>This session, <em>Data management report details: File formats</em>, focuses on specific recommended file formats for text, tabular data, images, sound, and geospatial data. </p>
IPBES Data Management Tutorials - Session 3.7: Data management report details: Long-term storage details
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management reports </em>chapter provides an overview and discussion of specific elements of IPBES data management reports.</p> <p>This session, <em>Data management report details: Long-term storage details</em>, explores the reasons why IPBES recommends Zenodo as a long-term repository. </p>
Continuous multi-sensor wearable data and daily subject-reported fatigue of heathy adults
<p>Fatigue is a broad, multifactorial concept encompassing feelings of reduced physical and mental energy levels. Fatigue strongly impacts health-related quality of life across a huge range of conditions, yet, to date, tools available to understand fatigue are limited. We collected a total of 28 healthy adult subjects and 973 recording days. Recorded data included continuous multimodal wearable sensor time series on physical activity, vital signs, and other physiological parameters at 1-minute temporal resolution, and daily questionnaires (patient-reported outcome scores, PROs) on fatigue. When matching both sensor data and PROs, the datasets contains data from 27 subjects and 405 recording days.</p> <p>Analysis of these multimodal digital data to inform, quantify, and augment subjectively captured non-pathological fatigue measures were published at <em>Luo H., et. al. (2020), Assessment of Fatigue Using Wearable Sensors: A Pilot Study. Digit Biomark</em>.</p> <p>Demographics, sensor parameters and other information on this dataset can be found in the aforementioned manuscript and related supplementary material.</p> <p>Files included are</p> <ul> <li><em>fatiguePROs.csv</em>: daily PROs for all subjects</li> <li>subjectID_*.csv: sensor time series for each subject</li> </ul>
IPBES Data Management Tutorials - Session 3.6: Data management report details: Data sharing and access considerations
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The<em> Tools for data management c</em>hapter provides an overview and discussion of specific elements of IPBES data management reports.</p> <p>This session <em>Data sharing and access considerations </em>covers details on licenses, exceptions to data sharing, and intellectual property considerations. </p>
Experimental data for "Measurement Report: Influence of particle density on secondary ice production by graupel and ice pellet collisions"
<p>This dataset includes measurement data on secondary ice production due to bare graupel - bare graupel, and ice pellet - ice pellet collisions carried out in the Mainz Cold Room (M-CR) of the Johannes Gutenberg University of Mainz. </p>
Supplementary data to Dating the timbers from the 'Sparrow-Hawk', a shipwreck from Cape Cod, USA. Journal of Archaeological Science: Reports 103374
<p>This record gives access to all supplementary data that forms the background to the paper: Daly, A., Hocker, F. & Mires, C., 2022. Dating the timbers from the ‘Sparrow-Hawk’, a shipwreck from Cape Cod, USA. Journal of Archaeological Science: Reports https://doi.org/10.1016/j.jasrep.2022.103374</p> <p>In 1626, a vessel making its way to Virginia was forced off course and damaged in a storm, which drove the ship onto the eastern shore of the Cape Cod peninsula, Massachusetts. Onboard were two English merchants and some servants and farmers, many of whom were Irish. In 1863, a storm exposed the weathered remains of a vessel at Old Ship Harbor. At the time, it was hailed as the same ship that had brought the Virginia-bound passengers to Plymouth in 1626. Recent wiggle-match C14 dating and dendrochronology suggests that this is indeed a ship from the early seventeenth century.</p>
IPBES Data Management Tutorials - Session 3.5: Data management report details: Sensitive data, anonymization, and ethical considerations
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data and knowledge management policy. They cover topics ranging from data and knowledge management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management reports </em>chapter provides an overview and discussion of specific elements of IPBES data management reports.</p> <p>This session on <em>data management report details: Sensitive data, anonymization, and ethical considerations </em>captures specific considerations and processes for IPBES experts regarding sensitive data and Indigenous and local knowledge within data management reports. </p>
Project Tycho Level 2 data: Counts of multiple diseases reported in UNITED STATES OF AMERICA, 1888-2014
Project Tycho data include counts of infectious disease cases or deaths per time interval. A count is equivalent to a data point.<p></p><p>Project Tycho level 2 version 1.1.0 data include data counts that have been filtered from the raw data to render standardized data that can be used immediately for analysis. All level 2 data were originally reported in a consistent format and have not been transformed into a standard format by Project Tycho staff, except for smallpox records that included repeated counts for the same location and week, but sometimes with different numbers. These duplicate smallpox records have been averaged into one count for each location and week. Level 2 data include counts for a wide variety of diseases and locations for varying time periods. Because we removed data in an inconsistent format from level 2 data, counts may be missing for certain diseases, locations, or years. For the most complete collection of standardized data, we encourage users to use Project Tycho version 2.0 datasets.</p><p>More detailed methods and additional information about the origin of Projec Tycho level 2 version 1.1.0 data can be found in our original publication in the New England Journal of Medicine: <a href="http://www.nejm.org/doi/full/10.1056/NEJMms1215400">http://www.nejm.org/doi/full/10.1056/NEJMms1215400</a></p><p>Level 2 version 1.1.0 data is represented in a CSV file with 11 columns:</p><ul><li>epi_week: a six digit number that represents the year and epidemiological week for which disease cases or deaths were reported (yyyyww)</li><li>country: a two digit country abbreviation, only including "US" in version 1.1.0</li><li>state: the two digit postal code state abbreviation that represents the state for which a count has been reported</li><li>loc: the name of a state or city for which a count has been reported, capitalized</li><li>loc_type: the type of location (STATE or CITY) for which a count has been reported</li><li>disease: the disease for which a count has been reported, in all capitals</li><li>event: an indicator representing the disease outcome reported, including "CASES" or "DEATHS"</li><li>number: the reported number of cases or deaths</li><li>from_date: the start date of the time interval for which a count was reported, as yyyy-mm-dd</li><li>to_date: the end date of the time interval for which a count was reported, as yyyy-mm-dd</li><li>url: the URL of the source document from which the count was obtained</li></ul><p></p>
Project Tycho Level 1 data: Counts of multiple diseases reported in UNITED STATES OF AMERICA, 1916-2011
<p>Project Tycho data include counts of infectious disease cases or deaths per time interval. A count is equivalent to a data point. Project Tycho level 1 data include data counts that have been standardized for a specific, published, analysis. Standardization of level 1 data included representing various types of data counts into a common format and excluding data counts that are not required for the intended analysis. In addition, external data such as population data may have been integrated with disease data to derive rates or for other applications.</p><p>Version 1.0.0 of level 1 data includes counts at the state level for smallpox, polio, measles, mumps, rubella, hepatitis A, and whooping cough and at the city level for diphtheria. The time period of data varies per disease somewhere between 1916 and 2011. This version includes cases as well as incidence rates per 100,000 population based on historical population estimates. These data have been used by investigators at the University of Pittsburgh to estimate the impact of vaccination programs in the United States, published in the New England Journal of Medicine: <a href="http://www.nejm.org/doi/full/10.1056/NEJMms1215400">http://www.nejm.org/doi/full/10.1056/NEJMms1215400</a>. See this paper for additional methods and detail about the origin of level 1 version 1.0.0 data.</p><p>Level 1 version 1.0.0 data is represented in a CSV file with 7 columns:</p><ul><li>epi_week: a six digit number that represents the year and epidemiological week for which disease cases or deaths were reported (yyyyww)</li><li>state: the two digit postal code state abbreviation that represents the state for which a count has been reported</li><li>loc: the name of a state or city for which a count has been reported, capitalized</li><li>loc_type: the type of location (STATE or CITY) for which a count has been reported</li><li>disease: the disease for which a count has been reported: HEPATITIS A, MEASLES, MUMPS, PERTUSSIS, POLIO, RUBELLA, SMALLPOX, or DIPHTHERIA</li><li>cases: the number of cases reported for the specified disease, epidemiological week, and location</li><li>incidence_per_100000: the number of cases per 100,000 people, computed using historical population counts for cities and states as reported by the US Census Bureau</li></ul><p></p>
Data for manuscript Marmet, Studer, Lemoine, Grazioli, Bertholet & Gmel (2019). Reconsidering the associations between self-reported alcohol use disorder and mental health problems in the light of co-occurring addictions in young Swiss men. Plos One. DOI: 10.1371/journal.pone.0222806.
<p>Dataset for the manuscript Marmet, Studer, Lemoine, Grazioli, Bertholet & Gmel (2019). Reconsidering the associations between self-reported alcohol use disorder and mental health problems in the light of co-occurring addictions in young Swiss men. Plos One. DOI: 10.1371/journal.pone.0222806.</p> <p>The dataset contains all data needed to reproduce the results in the above cited manuscript. Variable description and labels can be found in the codebook. For further information on the instruments used please refer to the manuscript.</p> <p>The data was collected between April 2016 and March 2018 in Switzerland by the C-SURF study (<a href="http://www.c-surf.ch">www.c-surf.ch</a>). Participants were on average 25 years old when they answered the questionnaires. The final sample size used in the manuscript is 5516. Please note that the dataset contains 25 datasets created with multiple imputation, therefore there are no missing values in the dataset.</p> <p>The research protocol for this study was approved by the Human Research Ethics Committee of the Canton Vaud (Protocol No. 15/07). Data collection was funded by the Swiss National Science Foundation (FN 33CSC0-122679, FN 33CS30_139467, FN 33CS30_148493)</p>
Data Report: "Health care of Persons Deprived of Liberty" Course from Brazil's Unified Health System Virtual Learning Environment
<p><strong>Dataset name: </strong>asppl-dataset.csv</p> <p><strong>Version: </strong>1.0</p> <p><strong>Dataset period: </strong>06/07/2018- 05/25/2021</p> <p><strong>Dataset Characteristics: </strong>Multivalued</p> <p><strong>Number of Instances: </strong>4861</p> <p><strong>Number of Attributes: </strong>33</p> <p><strong>Missing Values: </strong>Yes</p> <p><strong>Area(s): </strong>Health and education </p> <p><strong>Sources: </strong></p> <ul> <li> <p><strong>Primary</strong>: Unified Health System Virtual Learning Environment (AVASUS, in Portuguese: Ambiente Virtual de Aprendizagem do Sistema Único de Saúde) [1];</p> </li> <li> <p><strong>Secondary: </strong></p> <ol> <li> <p>Brazilian Classification of Occupations (CBO, in Portuguese: Classificação Brasileira de Ocupação) [2];</p> </li> <li> <p>National Registry of Health Establishments (CNES, in Portuguese: Cadastro Nacional de Estabelecimentos de Saúde) [3]; and </p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE, in Portuguese: Instituto Brasileiro de Geografia e Estatística) [4].</p> </li> </ol> </li> </ul> <p><strong>Description: </strong>The data contained on the asppl-dataset.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health care of Persons Deprived of Liberty”. The course is available on the Unified Health System Virtual Learning Environment [1]. This dataset provides elementary data for analyzing the course’s impact and reach, as well as the profile of its participants.</p> <p> </p>
Data from systematic audit for paper: Insights into the quantification and reporting of model-related uncertainty across different disciplines
<p>This upload contains 7 data files (each contains cleaned and compiled data for a given scientific field) and 2 R scripts. These files support the paper: Insights into the quantification and reporting of model-related uncertainty across different disciplines.</p> <p> </p> <p><strong>Description of the data</strong></p> <p>Compiled data files for each field contain all reviewers audit answers for eligible papers. All papers that met exclusion criteria have been removed.</p> <p>Data checks have been performed and formatting errors corrected either in R or manually, following steps detailed in the STAR methods.</p> <p>Column names and description:</p> <ul> <li>Number: number of question from 1 to 9</li> <li>Questions: question text – question to be answered by the reviewer</li> <li>QuestionCode: shortened code for each question</li> <li>Paper: paper code - first author surname/initial and surname and year</li> <li>Initials: initials of reviewer</li> <li>Answer: answer to the question</li> <li>Details: extra details to support the answer</li> <li>Location: where in the text the uncertainty was presented</li> <li>Presentation: how the uncertainty was presented</li> <li>ModelType: type of model (focal model)</li> <li>Comments: any other comments from the reviewer</li> <li>Checks: checks of whether NA or no have been included in correct places e.g. if answers to questions 1:4 are no then question 9 is NA, if question 7 is no then 8 is NA</li> <li>Check 1 = when Answer = No, Location is NA</li> <li>Check 2 = when Answer to Number 1-4, 6 or 8-9 is Yes that Details are not NA</li> <li>Check 3 = when Answer = No, Presentation = NA</li> <li>Check 4 = when Location is not NA, presentation is not NA</li> <li>Check 5 = if the Answer to 5 or 7 is "No" then Answer to 6 and 8 = "NA"</li> <li>Check 6 = if Answer for 1-4 is "No", then Answer for 9 = "NA"</li> </ul> <p><strong>Code description</strong></p> <p>Two scripts are included, the first is theme_script.R, this includes code to set up a ggplot theme for the figures. The second is Figure_code.R, this script contains all code to plot and save the three figures from the paper.</p>
South African Public Protector Investigate Reports Data
<p>Please read the Code Book.</p> <p>Author: Tomohiro Hosoi</p> <p> </p> <p><strong>Aim of the Data Set</strong></p> <p>The South African Public Protector Investigate Reports Data is the original data constructed by Tomohiro Hosoi (“the author”), who aims to investigate the role of the Public Protector in South Africa. His research article titled “South African Public Protector: People’s Watchdog or Politicians’ Lapdog?” which is published in <em>African Study Monographs </em>2023, uses this data set.</p> <p>The author aims to share the dataset for researchers to more sophisticated research in the future. The author provides the data in both excel and CSV format.</p> <p> </p> <p><strong>Source of the Data</strong></p> <p>The author downloaded all of the Investigation Reports on the Office of Public Protector’s Home Page on 15 April 2022. (<a href="http://www.publicprotector.org/?q=content/investigation-reports-categories">http://www.publicprotector.org/?q=content/investigation-reports-categories</a>). The data includes 388 reports from 2008 to 2022. The author reads all of the reports and codes the information containing them.</p>
Plot-level field data and model simulation results, archived to accompany Turner et al. manuscript; reports data from summer 2017 sampling of short-interval fires that burned during summer 2016 in Greater Yellowstone.
Subalpine forests in the northern Rocky Mountains have been resilient to stand-replacing fires that historically burned at 100–300-yr intervals. Fire intervals are projected to decline drastically as climate warms, and forests that reburn before recovering from previous fire may lose their ability to rebound. We studied recent fires in Greater Yellowstone (Wyoming, USA) and asked whether short-interval (less than 30 yrs) stand-replacing fires can erode lodgepole pine (Pinus contorta var. latifolia) forest resilience via increased burn severity, reduced early postfire tree regeneration, reduced carbon stocks, and slower carbon recovery. During 2016, fires reburned young lodgepole pine forests that regenerated after wildfires in 1988 and 2000. During 2017, we sampled 0.25-ha plots in stand-replacing reburns (n=18) and nearby young forests that did not reburn (n=9). We also simulated stand development with and without reburns to assess carbon recovery trajectories. Nearly all prefire biomass was combusted ("crown fire plus") in some reburns in which prefire trees were dense and small (≤ 4 cm basal diameter). Postfire tree seedling density was reduced six-fold relative to the previous (long-interval) fire, and high-density stands (greater than 40,000 stems ha-1) were converted to sparse stands (less than 1,000 stems ha-1). In reburns, coarse wood biomass and aboveground carbon stocks were reduced by 65% and 62%, respectively, relative to areas that did not reburn. Increased carbon loss plus sparse tree regeneration delayed simulated carbon recovery by greater than 150 yrs. Forests did not transition to nonforest, but extreme burn severity and reduced tree recovery foreshadow an erosion of forest resilience.
MCR LTER: Coral Reef: Data in support of Edmunds 2018 Scientific Reports
These data are selected from the larger MCR LTER timeseries datasets knb-lter-mcr.4001 and knb-lter-mcr.4 which contain annual surveys of coral recruitment and coral cover and are formatted here in support of this publication: Edmunds, P.J., Implications of high rates of sexual recruitment in driving rapid reef recovery in Mo'orea, French Polynesia, Scientific Reports 8, Article number: 16615 (2018). DOI:10.1038/s41598-018-34686-z This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Data relating to Clyne et al. Quality, scope and reporting standards of randomised controlled trials in Irish Health Research: an observational study
<p>Data relating to the study reported in the paper "Quality, scope and reporting standards of randomised controlled trials in Irish Health Research: an observational 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.