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136 results for “trend analysis”
[Dataset] APACPH-23-136: RESEARCH TREND ON STROKE CAREGIVER BURDEN, A BIBLIOMETRIC ANALYSIS
<p>Supplementary Material for Poster Presentation</p><p>APACPH-23-136: RESEARCH TREND ON STROKE CAREGIVER BURDEN, A BIBLIOMETRIC ANALYSIS</p>
Emerging trends on workplace ethics and mental health: A bibliometric analysis using CiteSpace
<p>The Dataset is utilized to do a bibliometric analysis in combining Workplace Ethics and Mental Health during the period of 1989 to 2023.</p>
Bibliometric analysis of research trends in aquafeed formulation
<p>A bibliographic comparison could be conducted between the two considered time periods, 2010-2019 and 2020-2025, to explore how research priorities and trends might have evolved. The earlier period would provide a foundation of historical developments, while the more recent one could focus on current trends and innovations. This analysis could reveal shifts in scientific and industrial priorities, offering insights into how emerging challenges and opportunities might be influencing the aquaculture field. The findings would provide valuable perspectives on the direction of research, particularly regarding sustainability and efficiency in aquaculture. Such analyses, based on database resources, could be applied to track trends, identify gaps, and guide future research directions.</p>
Synthetic temporal dataset for temporal trend analysis and retrieval
<p>This repository contains a synthetic, temporal data set that was generated by the authors by sampling values from the Gaussian distribution. The dataset contains eight nontemporal dimensions, a temporal dimension, and a numerical measure attribute. The data set was generated according to the scheme and procedure detailed in this source paper: Kaufmann, M., Fischer, P.M., May, N., Tonder, A., Kossmann, D. (2014). TPC-BiH: A Benchmark for Bitemporal Databases. In: Performance Characterization and Benchmarking. TPCTC 2013. Lecture Notes in Computer Science, vol 8391. Springer, Cham. https://doi.org/10.1007/978-3-319-04936-6_2. The data set can be used for analyzing and locating temporal trends of interest, where a temporal trend is generated by selecting the desired values of the nontemporal dimensions, and then selecting the corresponding values of the temporal dimension and the numerical measure attribute. Locating temporal trends of interest, e.g., unusual trends, is a common task in many applications and domains. It can also be of interest to understand which nontemporal dimensions are associated with the temporal trends of interest. To this end, the data set can be used for analyzing and locating temporal trends in the data cube induced by the data set.</p>
CONUS and sub-regional monthly cloudiness supporting for the trend analysis: "CONUS Cloud Pattern Change 1980-2020" Vo T.T., Hu L., Xue L., Chen S. (2024)
<p> </p> <p>The dataset supporting for the publication: "CONUS Cloud Pattern Change 1980-2020" Vo T.T., Hu L., Xue L., Chen S. (2024), Journal of Climate. </p> <ul> <li>The data format is in tabular form (.csv, comma delimited). Each row is the <strong>monthly </strong>aggregated for <strong>each </strong>sub-region More specifically, the description of each column with associated with its unit formatted in the table are listed as follows: <ul> <li><strong>datetime</strong>: time of the observation (formatted as month/day/year)</li> <li><strong>original_time_series</strong>: original cloud coverage before processing using the trend analysis mentioned in the paper (unit: percentage, %)</li> <li><strong>enso_neutral</strong>: cloud coverage removing the ENSO effect using linear regression method followed by Gu and Adler 2011 (unit: percentage, %)</li> <li><strong>cloud_coverage</strong>: cloud coverage removing the ENSO effect and seasonality and remainder (refer to the cloud coverage used in the manuscript) using Seasonal Decomposition of Time Series by Loess (STL) method (unit: percentage, %)</li> <li><strong>region_name_list</strong>: name of the sub-region</li> <li><strong>cloud_type</strong>: certain cloud type (all clouds refers to total clouds)</li> </ul> </li> </ul> <p> </p> <p><span>References: </span></p> <p>Gu, G., & Adler, R. F. (2011). Precipitation and temperature variations on the interannual time scale: Assessing the impact of ENSO and volcanic eruptions. <em>Journal of Climate</em>, <em>24</em>(9), 2258–2270. https://doi.org/10.1175/2010JCLI3727.1</p> <p>Cleveland, R. B., Cleveland, W. S., & Terpenning, I. (1990). STL: A Seasonal-Trend Decomposition Procedure Based on Loess. <em>Journal of Official Statistics</em>, <em>6</em>(1), 3. http://ezproxy.montevallo.edu:2048/login?url=https://www.proquest.com/scholarly-journals/stl-seasonal-trend-decomposition-procedure-based/docview/1266805989/se-2?accountid=12538</p> <p> </p>
Current trends in scientific research on global warming: A bibliometric analysis (2005-2014)
<p>This dataset was created in the context of the project: " Current trends in scientific research on global warming: A bibliometric analysis (2005-2014)".</p> <p>---</p> <p>Global warming is a topic of increasing public importance, but there have not been published scientometric studies on this topic. The objective of this paper is to contribute to a better understanding of the scientific knowledge in global warming and his effect, as well as to investigate its evolution through the published papers included in Web of Science database. Items under study were collected from Web of Science database from Thomson Reuters. A bibliometric and social network analyses was performed to obtain indicators of scientific productivity, impact and collaboration between researchers, institutions and countries. A subject analysis was also carried out taking into account the key words assigned to papers and subject areas of journals. 1,672 articles were analysed since 2005 until 2014. The most productive journals were Journal of Climate (n=95) and Geophysical Resarch Letters (n=78). The most frequent keywords have been Climate Change (n=722), Model (n=216) and Temperature (n=196). The network of collaboration between countries shows the central position of the United States, together with other leading countries such as United Kingdom, Germany, France and Peoples Republic of China. The research on global warming had grown steadily during the last decade. A vast amount of journals from several subject areas publishes the papers on the topic, including journals of general purpose with high impact factor. Almost all the countries have USA as the main country with which one collaborates. The analysis of key words shows that topics related with climate change, impact, temperature, models and variability are the most important concerns on global warming.</p> <p>---</p> <p>The dataset consist of the following:</p> <p>1) The list of papers included in the analyses: Papers.xlsx</p> <p>This file contains 1672 titles, each line representing a paper (including title of the paper, journal ISSN and year of publication).</p> <p>2) The list of authors: Authors.xlsx</p> <p>This file contains all 4488 authors, each line representing an author (including full name, total number of papers and year of publication).</p> <p>3) The list of scientific journals: Journals.xlsx</p> <p>This file containts all 687 journals, each line representing a journal (including name of the journal, ISSN, total number of papers and year of publication).</p> <p>4) The list of countries: Country.xlsx</p> <p>This file contains all 84 countries, each line representing a country (including country name, total number of papers, total number of citations, and number of citations per paper).</p> <p>5) The list of keywords: Keywords.xlsx</p> <p>This file contains all 6422 keywords, each line representing a keyword (including keywords, number of papers and year of publication)</p>
Fig 8 in Publishing trends and productivity in insect taxonomy from 1946 through 2012 based on an analysis of the Zoological Record for four species-rich families
Fig 8. Number of co-authored papers with authors from different continents from 1946 to 2012.
Fig. 7 in Publishing trends and productivity in insect taxonomy from 1946 through 2012 based on an analysis of the Zoological Record for four species-rich families
Fig. 7. Percentage of articles with co-authors from 1946 to 2012.
The Rising Influence of AI in Higher Education: Trends and Insights from a Bibliometric Analysis
<p>The purpose of this research was to examine the evolution, scope, and orientation of the scientific production on artificial intelligence applications in university students. The methodology, with a non-experimental design and qualitative approach, involved a search in Scopus, identifying 643 documents between 1975-2024, analyzed through VOSviewer and Bibliometrix. The results show an emerging field, but with rapid growth (4.59% per year), with notoriety of Kong, Abdulrahman and Chai. Research is predominantly in computer science (61%), social sciences (33%) and engineering (23%) from China, USA, Spain and Taiwan. Current applications focus on the use of AI in education, machine learning, support for academic decisions and student mental health. However, it is necessary to expand the approach towards ethical and regulatory aspects and the evaluation of multifaceted effects on different student profiles. In conclusion, although production is growing rapidly, more comprehensive perspectives are required to responsibly enhance the impact of these technologies on the university educational experience.</p>
Precipitation trend analysis for different Mediterranean countries
<p>Independent files with detailed numeric analysis of precipitation evolution and trends for different countries in the Mediterranean region.</p>
Global Trends in research of work-related musculoskeletal disorders among surgeons: Bibliometric analysis and visualization from 1991 to 2024
<p><span><span>This file contains all the information relating to the 184 articles included in the bibliometric analysis.</span></span></p>
Data for "Effects of forest dieback on deadwood patterns: large scale trends from a cross-analysis of European databases"
<p><strong><span>Aims</span></strong></p> <p><span>We carried out an opportunistic correlative study between past crown conditions and current deadwood volumes.</span></p> <p><span>Our aim was to mobilise available data on site factors and long-term monitoring of crown vitality indicators in Europe to investigate the influence of current and recent local defoliation levels on plot-level deadwood volume.</span></p> <p><span>For a subset of level I, 16*16-km monitoring plots located throughout Europe, we benefitted from data on both (i) deadwood measurements carried out within the framework of the Forest Focus Biosoil Project </span><span>(Galluzzi et al., 2019)</span><span>, pre-processed into a consistent and harmonized deadwood dataset by </span><span>Puletti et al. (2019)</span><span>, and (ii) defoliation assessments provided yearly since 1989 by the International Co-operative Program on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests), the most comprehensive European monitoring network for the large-scale assessment of forest ecosystem health </span><span>(Vitale et al., 2014)</span><span>. </span></p> <p><span>Biosoil data on deadwood and ICP data on defoliation have never been crossed before.</span></p> <p><span>We used defoliation level as a proxy for the severity of stand dieback. Deadwood patterns can be addressed through deadwood profiles, which subdivide local deadwood stocks into classes based on size, position and decay stage.</span></p> <p><a name="_Toc175840512"></a><a name="_Toc116027761"></a><span><strong><span>ICP database and defoliation protocol</span></strong></span></p> <p><span>The International Cooperative Program to assess and monitor air pollution effects on the forest (ICP Forests) is responsible for an extensive level I monitoring system of forest sites </span><span>(Hauβmann & Fischer, 2004)</span><span>, which has been in operation since 1986. This large-scale level I network is made up of dense, spatially representative sampling points placed throughout European forests on a 16 × 16 km virtual grid, and is dedicated to monitoring forest conditions. The sampling points cover most European forested areas and encompasses ca. 6000 monitoring plots in 42 countries. In each plot, a visual evaluation of defoliation and discoloration of tree crowns is performed annually to survey forest health status (<a href="http://icp-forests.net/page/largescale-forest-condition">http://icp-forests.net/page/largescale-forest-condition</a>). Data management is presently carried out at the Programme Co-ordinating Centre (PCC) of ICP Forests in Eberswalde, Germany, and all data are available upon request. Since 1989, a standardized procedure for “annual surveys of crown condition’’ has been applied to 24 selected dominant and co-dominant trees with a minimum height of 60 cm and showing no significant mechanical damage. The defoliation and discoloration level of each tree crown is visually assessed on a sliding scale of 5% increments as the percentage of needle/leaf loss in the assessable crown as compared to a reference tree with full foliage. Mean defoliation at the plot scale was defined as the proportion of “damaged” trees i.e., with a defoliation rate of more than 25%, and used as a proxy for plot decline level. In the ICP database, the factors associated with observed defoliation related to natural disturbances or management (i.e., vertebrate or insect herbivory, fungal or fire damage, drought impacts, signs of removal of coarse woody debris, past landscape) were not recorded in a sufficiently standardized way to be used as covariates in our models. Similarly, plot-level living tree density and above-ground biomass for standing living trees (expressed in kg.ha<sup>−1</sup>), presumably surveyed in subplot 2, were not available.</span></p> <p><a name="_Toc175840513"></a><a name="_Toc116027762"></a><span><strong><span>Biosoil database and deadwood protocol</span></strong></span></p> <p><a name="_Toc116027763"></a><span>In the framework of the large collaborative European Forest Focus BioSoil-Biodiversity project</span><span>, a system of circular concentric subplots was built around certain ICP level I plots to collect additional data on stand structure and biodiversity between 2005 and 2008 (Figure 1). </span><span><span>The individual countries were responsible for selecting the ICP level I plots to be included in the BioSoil project </span></span><span><span>(Galluzzi et al., 2019)</span></span><span><span>. Overall, a total of 3243 geocoded Level I plots were considered in 19 European countries </span></span><span><span>(Puletti et al., 2017)</span></span><span><span>: Austria, Belgium (Flanders only), Cyprus, the Czech Republic, Denmark, Finland, France, Germany (eight federal states only), Hungary, Ireland, Italy, Latvia, Lithuania, Poland, Slovakia, Slovenia, Spain, Sweden and the United Kingdom (Figure 1). BioSoil project results are recorded in the multi-dimensional LI-BioDiv geodatabase that contains raw data on forest structure and vegetation records used to calculate simple plot-level structural and compositional forest variables (i.e., biomass, deadwood volume, plant alpha-diversity; </span></span><span><span>Bastrup-Birk et al. 2007; Hiederer & Durant 2010)</span></span><span><span>. At each plot, deadwood was quantified on an area of 400 m<sup>2</sup> (BioSoil subplots 1 and 2, radius of 11.28 m; </span></span><span><span>Puletti et al., 2017)</span></span><span><span>. The deadwood survey included coarse woody debris (including lying dead trees), snags (including standing dead trees) and stumps more than 10 cm in diameter. Only snags and stumps more than 130 cm in height were considered. Diameter, length or height, tree species and decay stage (5 classes) were recorded for each deadwood piece. The raw ICP deadwood data were processed by </span></span><span><span>Puletti et al. (2017, 2019)</span></span><span><span> into a consistent and harmonized pan-European deadwood dataset, which we used in this study. The dataset provides total deadwood volume and the volume of several deadwood types for each plot. Further details can be found in the ICP Forests manual (</span></span><a href="http://icp-forests.net/page/icp-forests-manual"><span><span>http://icp-forests.net/page/icp-forests-manual</span></span></a><span><span>), </span></span><span><span>Puletti et al. (2019)</span></span><span><span> and </span></span><span><span>Augustynczik et al. (2024)</span></span><span><span>.</span></span></p> <p><span><span>In our study, we considered the following response variables</span></span><span>: (i) total deadwood volume, (ii) </span><span>standing deadwood (snags) volume, (iii) volume of ground-lying deadwood, (iv) </span><span>fresh deadwood volume </span><span>(= Vm3_dec1_Biosoil + Vm3_dec2_Biosoil), and (v) decayed deadwood volume = (= Vm3_dec4_Biosoil + Vm3_dec5_Biosoil).</span></p> <p><span>A few environmental covariates were collected from the Biosoil data: (i) management intensity (grouped into two classes: recently harvested, i.e., with management evidence within the last 10 years; and not recently harvested, i.e., unmanaged (no management evidence) or managed a long time ago (management evidence but more than 10 years previously), (ii) average stand age (separated into 3 classes: mature [>100 yrs], mid-aged [41-100 yrs], young [1-40 yrs]), (iii) elevation (above sea level, a.s.l.), a continuous quantitative variable, (iv) dominant tree genus, and (v) forest type, depending on the dominant tree species: coniferous, deciduous or mixed.</span></p> <p><a name="_Toc175840514"></a><a name="_Toc116027764"></a><span><strong><span>Database joint</span></strong></span><span><strong><span>: <a name="_Toc116027765"></a>plot matching in time series</span></strong></span></p> <p><span>After harmonizing plot names and coordinates in the two datasets (ICP-defoliation and Biosoil-deadwood), only plots with matched data in both datasets were selected. Plots with a maximum of one year’s discontinuity in the data were retained, and the missing values were reconstructed from the average values in contiguous years. Plots with discontinuities in defoliation measurements of more than 2 years were deleted. We matched defoliation measurements for the Biosoil-ICP datasets from 1989 to 2007 and finally obtained 2,070 five-year, 1,804 ten-year and 1,399 fifteen-year time series. This approach made it possible to define three 10-year time series [1995-2005, 1996-2006, 1997-2007] with plots in 17 countries, from five plots in Ireland and nine in the United Kingdom, to 337 plots in Finland and 461 in France.</span></p> <p><a name="_Toc175840515"></a><a name="_Toc116027766"></a><span><strong><span>Calculation of global defoliation metrics</span></strong></span></p> <p><span>We calculated 16 univariate metrics to summarize changes in defoliation throughout the 10-year period prior to the Biosoil deadwood measurements. Some of the selected parameters describe the immediate possible effects of defoliation severity in the recent past on a given year: (i) defoliation level of the previous year (n-1), (ii) defoliation level of the year before the previous year (n-2), (iii) defoliation level of the year two years before the previous year (n-3). Other defoliation metrics relate to the cumulative effects of defoliation levels in the near or the distant past: (i) average defoliation level over the last two years, (ii) average defoliation level over the last three years, (iii) average defoliation level over the last five years, (iv) average defoliation level over the first five years of the 10-year time series, and (v) time elapsed since last peak defoliation. Several other parameters depict general trends in the level of defoliation over the 10-year time series: for cumulative metrics: (i) arithmetic mean of annual defoliation level; (ii) geometric mean of annual defoliation level; (iii) Area Under the defoliation time Curve (AUC), i.e., the cumulative sum of defoliation levels; and for the overall trend: (iv) the estimated slope of the linear regression line for defoliation level over time. Finally, some of the metrics reflect defoliation severity and repetition along the 10-year time series, and their potentially time-lagged effects: (i) maximum defoliation level; (ii) total number of years elapsed after the dieback peak level, whether successive or not; (iii) the number of peaks, consecutive or discontinuous, i.e., the number of severe defoliation events and defoliation frequency; and (iv) duration of the longest peak, i.e., the longest continuous time during which the level of defoliation was greater than the relative threshold.</span></p> <p><span>A peak in defoliation was defined as a year in which the level of defoliation exceeded a relative threshold, i.e., the third quartile value. In our 10-year time series, the peak value was 25% and above. <span><span> </span></span></span></p>
Temporal and regional trends of antibiotic use in long-term aged care facilities across 39 countries, 1985-2019: systematic review and meta-analysis
<p><b>Background</b></p> <p>Antibiotic misuse is a key contributor to antimicrobial resistance and a concern in long-term aged care facilities (LTCFs). Our objectives were to: i) summarise key indicators of systemic antibiotic use and appropriateness of use, and ii) examine temporal and regional variations in antibiotic use, in LTCFs (PROSPERO registration CRD42018107125).</p> <p><b>Methods & Findings</b></p> <p>Medline and EMBASE were searched for studies published between 1990-2021 reporting antibiotic use rates in LTCFs. Random effects meta-analysis provided pooled estimates of antibiotic use rates (percentage of residents on an antibiotic on a single day [point prevalence] and over 12 months [period prevalence]; percentage of appropriate prescriptions). Meta-regression examined associations between antibiotic use, year of measurement and region. </p> <p>A total of 90 articles representing 78 studies from 39 countries with data between 1985-2019 were included. Pooled estimates of point prevalence and 12-month period prevalence were 5.2% (95% CI: 3.3-7.9; n=523,171) and 62.0% (95% CI: 54.0-69.3; n=946,127), respectively. Point prevalence varied significantly between regions (Q=224.1, df=7, p<0.001), and ranged from 2.4% (95% CI: 1.7-2.7) in Eastern Europe to 9.0% in the British Isles (95% CI: 7.6-10.5) and Northern Europe (95% CI: 7.7-10.5). Twelve-month period prevalence varied significantly between region (Q=15.1, df-3, p=0.002) and ranged from 53.9% (95% CI: 48.3-59.4) in the British Isles to 68.3% (95% CI: 63.6-72.7) in Australia. Meta-regression found no association between year of measurement and antibiotic use prevalence. The pooled estimate of the percentage of appropriate antibiotic prescriptions was 28.5% (95% CI: 10.3-58.0; n=17,245) as assessed by the McGeer criteria. Year of measurement was associated with decreasing appropriateness of antibiotic use over time (OR: 0.78, 95% CI: 0.67-0.91). The most frequently used antibiotic classes were penicillins (n=44 studies), cephalosporins (n=36), sulphonamides/trimethoprim (n=31), and quinolones (n=28). </p> <p><b>Conclusions</b></p> <p>Coordinated efforts focusing on LTCFs are required to address antibiotic misuse in LTCFs. Our analysis provides overall baseline and regional estimates for future monitoring of antibiotic use in LTCFs. </p>
Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components
<p>The provided dataset contains the data used by "Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components", in order to evaluate the maintainability degree of a software class and identify software components that will eventually become non-maintainable.</p>
Exploring Holocene temperature trends and a potential summer bias in simulations and reconstructions: TransEBM1.2 simulation data and analysis
<p>The TransEBM1.2 model code, transient climate simulation data of the last 26 ka and Python scripts to reproduce the analysis and Figures. </p>
Geographical trends of soil-associated biodiversity changes due to tree plantations in South America: biome and climate constraints revealed through meta-analysis
<p><strong>Aim</strong></p> <p>Evaluate the interaction between climate and biome structure when explaining changes in species richness of soil-associated communities due to tree plantations developed in different biomes. Compare the response of plants, soil invertebrates, and soil microorganisms, and test whether they should be considered sensitive-coupled biotas. Location Continental South America.</p> <p><strong>Time period</strong></p> <p>1996–2023 </p> <p><strong>Major taxa studied </strong></p> <p>Plants, soil invertebrates and soil microorganisms </p> <p><strong>Methods </strong></p> <p>Through a meta-analysis, the change in species richness (i.e., response ratio) associated with tree plantations was evaluated in 127 points of study across South America, considering soil-associated communities of plants, invertebrates and microorganisms. The influence of biome structure (open vs. closed habitats) on the response ratio and its interaction with the actual evapotranspiration (AET) and temperature seasonality was evaluated. Differentiated responses of different taxa were tested by comparing models with and without an interaction term referring to the taxon studied. The regional agricultural cover and plantation age were considered as anthropogenic variables.</p> <p><strong>Results </strong></p> <p>Models containing the AET were better at explaining the trend of change in species richness than those with temperature seasonality. The response to the change in species richness was oppositely related to the AET in open and closed biomes. Plants presented a higher loss in species richness than soil invertebrates and microorganisms. The three taxa were positively associated with AET, while seasonality was not relevant in any case. Both anthropogenic variables significantly lessened the change in species richness in all models. </p> <p><strong>Main conclusions</strong></p> <p>The structural contrast between the anthropogenic habitat and the biome where it is developed is a key factor influencing the response of soil-associated communities to tree plantations. Nevertheless, its influence must be assessed together with climatic and anthropogenic variables given that their interaction can explain different geographical trends in the change in species richness across regions.</p>
Dataset for "Research Trend of Behavioral Bias in Financial Market: A Bibliometric Analysis "
<p>Dataset for Bibliometric research</p>
Temporal Trends of Thrombolysis Treatment in Chinese Acute Ischemic Stroke (AIS) Patients From 2007-2017: Analysis of China National Stroke Registry (CNSR) I, II, and III; CTP-Draft Review Performed;
ClinicalTrials.gov study NCT04290494. IPD Sharing: YES. Countries: 1. Publications: 0.
Temporal and regional trends of antibiotic use in long-term aged care facilities across 39 countries, 1985-2019: systematic review and meta-analysis
Open the record for dataset details and reuse information.
Retrospective cohort analysis of Spanish national trends of coronary artery bypass grafting and percutaneous coronary intervention from 1998 to 2017
Open the record for dataset details and reuse information.
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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.