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1,742 results for “activity data”
Supplementary data and codes for "Confinement-induced accumulation accumulation and de-mixing of microscopic active-passive mixtures"
<p>This file contains the data and codes used in the paper</p> <p>“Confinement-induced accumulation accumulation and de-mixing of microscopic active-passive mixtures”, S. Williams et al, 2022.</p> <p>It includes the data used in all the figures and supplementary figures, as well as the codes used for simulations and escape rate estimation. The data files are in .mat format. The codes are Matlab codes with the exception of the analytical estimate of the escape rate which is a Mathematica worksheet.</p>
A map of active cropland and short-term fallows across Northern Mozambique derived from PlanetScope data
<p><strong>Overview</strong></p> <p>A map of smallholder-dominated landscapes covering the provinces Niassa, Zambezia, Cabo Delgado, and Nampula in Northern Mozambique. The map includes active cropland and short-term fallows as separate classes, as well as five land cover classes (herbaceous vegetation, open woodlands, closed woodlands, non-vegetated land, water). The map is based on PlanetScope mosaics and consequently comes at 4.77m spatial resolution.</p> <p>The download contains the following files:</p> <ul> <li>ps_lc_nmoz.tif / .qml: land cover map and associated QGIS style file</li> <li>ps_lc_nmoz_probmargins.tif / .qml: probability margins and associated QGIS style file</li> <li>training.gpkg: training samples with class labels</li> <li>LICENSE.pdf: NICFI data program user license</li> </ul> <p><strong>Map accuracy</strong></p> <p>We conducted an area-adjusted accuracy assessment based on a stratified random sample, which yielded important insights regarding accuracies and error types. The area-adjusted overall accuracy of the map is 88.9%, but users should be aware of the most important error types:</p> <ul> <li>Active cropland were overestimated, whereas local topographical depressions with moist soils, and regions with exposed soils/rocks and sparse vegetation cover were found to be falsely classified.</li> <li>Short-term fallows were underestimated, particularly in regions with high growth rates and extensive land management, such as parts of the northern and north-eastern study region.</li> </ul> <p><strong>Further resources</strong></p> <p>The production of this map was made possible through the <a href="https://www.planet.com/nicfi/">NICFI data program</a>, providing the PlanetScope mosaics and the Google Earth Engine cloud computing platform for preprocessing of the satellite data and classification. As such, the use of the map falls under the <a href="https://assets.planet.com/docs/Planet_ParticipantLicenseAgreement_NICFI.pdf">NICFI data program license agreement</a> included in the download. The code for preprocessing the PlanetScope mosaics is based on the Google Earth Engine Python API and made available at <a href="https://github.com/philipperufin/eepypr/">https://github.com/philipperufin/eepypr/</a>.</p> <p>We advise map users to read the <a href="https://eartharxiv.org/repository/view/3174/">preprint</a> or the <a href="https://doi.org/10.1016/j.jag.2022.102937">open access paper</a> for detailed insights. In case of questions please consult these resources or contact the lead author of the work.</p>
Raw Data - High resolution electrochemical additive manufacturing of microstructured active materials: case study of MoSx as a catalyst for the hydrogen evolution reaction
<p>The dataset contains raw data that complements the article:</p> <p>High resolution electrochemical additive manufacturing of microstructured active materials: Case study of MoSx as a catalyst for the hydrogen evolution reaction, J. Mater. Chem. A, 2021, 9, 22072-22081.</p> <p>C. Iffelsberger and M. Pumera*</p> <p>https://doi.org/10.1039/D1TA05581J</p> <p>Related to the MSCA Project: 888797 LoCatSpot</p>
Data affiliated with "Evolution of Flare Activity in GKM Stars Younger than 300 Myr over Five Years of TESS Observations"
<p>Data and Python scripts affiliated with the publication "Evolution of Flare Activity in GKM Stars Younger than 300 Myr over Five Years of TESS Observations" in the American Astronomical Journals. The manuscript pre-print can be found on <a href="https://arxiv.org/abs/2405.00850">arXiv</a>.</p> <p>This repository contains all of the data used to complete the analysis of the aforementioned manuscript, along with the Python scripts used to create all of the figures in the manuscript. Many of the data products from this manuscript are saved as CSVs, with appropriate column names and units, when applicable.</p> <p>Additionally, we include the light curves for all targets in this sample, along with the 'probability light curves,' which were used to identify flares in the TESS data. These data products can be found in the zip file 'TESS_stella_outputs.zip'. The rest of the data product is structured as it is on the <a href="https://github.com/afeinstein20/young-stellar-flares/tree/paper">associated GitHub repository</a>.</p>
Replication data for Global variation in the preferred temperature for recreational outdoor activity
<p><strong>Description</strong></p> <p>This dataset contains the processed data used for the statistical analysis in Linsenmeier, M. (2024): <a href="https://doi.org/10.1016/j.jeem.2024.103032">Global variation in the preferred temperature for recreational outdoor activity</a>, published in the Journal of Environmental Economics and Management.</p> <p>The main data on temperature and rainfall are from ERA5 reanalysis (Hersbach et al. 2018). Data on mobile phone activity are from the Google Mobility Reports. Data on GDP per capita are from the World Bank.</p> <p><strong>Acknowledgements</strong></p> <p>The data contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p><strong>Bibliography</strong></p> <ul> <li>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on single levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 10.24381/cds.adbb2d47</li> </ul>
Genome-wide de novo L1 Retrotransposition Connects Endonuclease Activity with Replication: insertion data and derivative models
<p>This data repository provides access to the LINE-1 (L1) insertion site data from Flasch, et al., 2019:<br><a href="https://www.sciencedirect.com/science/article/pii/S0092867419302338?via%3Dihub">https://www.sciencedirect.com/science/article/pii/S0092867419302338?via%3Dihub</a></p> <p>Please see file <strong>L1_actual_and_random_insertions_help.docx</strong> for more detailed information.</p> <p>Files <strong>weighted_model_142_102917_corrected.txt</strong> and <strong>weighted_model_142_102917_uncorrected.txt</strong> carry a list of all possible 7-mer insertion sites, one per row, with site weights calculated based on observed L1 insertion sites. The files are either corrected or uncorrected for the frequency of those sites as found in the human genome, respectively. A header line defines the columns.</p> <p>The weight files described above were used to construct the simulated <strong>hg19 </strong>insertions sets described below.</p> <p>Archive <strong>all_insertion_sets.tar</strong> contains a series of insertion files, each with the complete data set used to analyze insertions from the named cell line.</p> <p>Column 5 is the iteration number, where:</p> <ul> <li>iteration == 0 identifies the actual, observed insertions</li> <li>each iteration > 0 identifies one round of simulation, up to 10K total simulations</li> <li>each iteration has the same number of insertions</li> </ul> <p>Column 4 is the insertion number within each actual or simulated insertion set.</p> <p>Please see the companion Zenodo data set:<br>10.5281/zenodo.12538130<br>for more information about creating insertion site models from your own insertion data.</p>
Data: Homochiral metal-organic frameworks coated double-plasmon active optical fiber for in-situ enantioselective detection
<p>This dataset is focused on utilization of optical fiber with double-plasmon activity (ensured by a spatially separated gold and silver nanocoating of the fiber core) and subsequent surface grafting by HMOFs for enantioselective capture of organic enantiomers.</p>
CATCH-EyoU: Exploiting European data and testing the integrated theory of youth active EU citizenship: PIDOP subset reanalysis
<p>This is a subset of the full PIDOP dataset. The derived subset contains cross-sectional survey results from the PIDOP questionnaire survey that were collected in 9 European countries (incl. Turkey) during a period of 16-26 year old in 2011. The data set includes 9060 individual cases. The questionnaire used in the survey is published in Barrett, M. & Zani, B. (Eds.) (2015). <em>Political and civic engagement: Multidisciplinary perspectives.</em> Hove: Routledge (p.519-534).</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Longitudinal Survey Data: Wave 1 & Wave 2: Estonia
<p>The data set was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538. The data set is a truncated version of the adolescents’ and young adults’ longitudinal survey that was carried out in Estonia from October 2016 to February 2018. It merges results of two polls (15-19 and 20-30 year olds). Survey was conducted by Univversity of Tartu (UT) within the WP7 research activity which aims at testing processes influencing societal and political engagement of young people.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Survey Data: Estonia: Wave 2
<p>The data set was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538. The data set is a truncated version of the adolescents’ and young adults’ survey that was carried out in Estonia from November 2017 to February 2018. It merges results of two polls (15-19 and 20-30 year olds). Survey was conducted by University of Taartu (UT) within the WP7 research activity which aims at testing processes influencing societal and political engagement of young people.</p>
Data from: Selectivity of Guanine Nucleotide Exchange Factor-mediated Cdc42 activation in primary human endothelial cells
<p>Data that was reported in "Selectivity of Guanine Nucleotide Exchange Factor-mediated Cdc42 activation in primary human endothelial cells" by </p> <p>Nathalie R. Reinhard<sup>1</sup>, Sanne van der Niet<sup>1</sup>, Anna Chertkova<sup>1</sup>, Marten Postma<sup>1</sup>, Theodorus W.J. Gadella Jr.<sup>1</sup>, Peter L. Hordijk<sup>1,2</sup>, and Joachim Goedhart<sup>1*</sup><br> </p> <p><strong>Affiliations:</strong></p> <p><sup>1 </sup>University of Amsterdam, Molecular Cytology, Swammerdam Institute for Life Sciences, van Leeuwenhoek Centre for Advanced Microscopy, Amsterdam, the Netherlands</p> <p><sup>2 </sup>Department of Physiology, Free University Medical Center, Amsterdam, The Netherlands</p> <p> </p> <p>*Correspondence to: j.goedhart@uva.nl</p>
A Panel Data Set of Cryptocurrency Development Activity on GitHub
<p>Contents:</p> <ul> <li><strong>all-sorted-recovered-normalized-2018-01-21-to-2019-02-04.csv</strong>: CSV format of all data, sorted by date. This file contains some imputed values for missing data, and all fields across all repositories and normalized to "null". This is the most convenient form to use.</li> <li><strong>all-sorted-2018-01-21-to-2019-02-04.csv</strong>: CSV format of all, sorted by date. It is the raw data after processing the raw format.</li> <li><strong>raw-data-2018-01-21-to-2019-02-04.tar.gz</strong>: The raw format of data collected (S-expressions). Contains additional contributor data and CoinMarketCap data not currently in the CSV datasets.</li> <li><strong>recovered.patch</strong>: The modification on <strong>all-sorted-2018-01-21-to-2019-02-04.csv</strong> after recovering (imputing) data<strong>, </strong>showing what was recovered.</li> <li><strong>recovered-normalized.patch</strong>: The modification of <strong>all-sorted-2018-01-21-to-2019-02-04.csv </strong>after normalizing the recovered data set. Thus, patching <strong>all-sorted-2018-01-21-to-2019-02-04.csv </strong>with<strong> recovered.patch</strong>, then <strong>recovered-normalized.patch </strong>gives <strong>all-sorted-recovered-normalized-2018-01-21-to-2019-02-04.csv</strong></li> <li><strong>missing-dates.txt</strong>: Days for which we missed GitHub data collection (partial or completely).</li> </ul> <p>Related publications:</p> <pre><code>@inproceedings{van-tonder-crypto-oss-2019, title = {{A Panel Data Set of Cryptocurrency Development Activity on GitHub}}, booktitle = "International Conference on Mining Software Repositories", author = "{van~Tonder}, Rijnard and Trockman, Asher and {Le~Goues}, Claire", series = {MSR '19}, year = 2019 } @inproceedings{trockman-striking-gold-2019, title = {{Striking Gold in Software Repositories? An Econometric Study of Cryptocurrencies on GitHub}}, booktitle = "International Conference on Mining Software Repositories", author = "Trockman, Asher and {van~Tonder}, Rijnard and Vasilescu, Bogdan", series = {MSR '19}, year = 2019 }</code></pre> <p>Related code: <a href="https://github.com/rvantonder/CryptOSS">https://github.com/rvantonder/CryptOSS</a></p>
Muskox body temperature and activity data
<p>Data on muskox (<em>Ovibos moschatus</em>) body temperature (°C) (mean, range, max and min) and activity (mean count) from Zackenberg in Northeast Greenland collected every 4 hours during the period from autumn 2017 to autumn 2018.</p>
Environmental and AIS data collected during the EUMarineRobots Trans-National Access activities experiments using the NATO STO-CMRE Littoral Ocean Observatory Network testbed
<p>Environmental and AIS data collected during the H2020 project EUMarineRobots Trans-National Access activities experiments using the NATO STO-CMRE Littoral Ocean Observatory Network (LOON) testbed. Environmental data consists of temperature measured across the water column; sound velocity measured close to the surface and close to the sea bottom; meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain). The environmental dataset is complemented with Automatic Identification System (AIS) data for the ships transiting close to the LOON area (Gulf of La Spezia, Italy)</p> <p>Temperature measured across the water column in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) Nov 12, 19-20, 23-24 - 2020<br> ii) Dec 1-4, 14-20 - 2020<br> iii) Jan 12-13, 15, 18-24, 27-28 - 2021</p> <p><br> Meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) Nov 12, 19-20, 23-24 - 2020<br> ii) Dec 1-4, 14-20 - 2020<br> iii) Jan 12-13, 15, 18-24, 27-28 - 2021</p> <p><br> Sound velocity measured close to the surface (SVP1) and close to the sea bottom (SVP2) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) Nov 12, 19-20, 23-24 - 2020<br> ii) Dec 1-4, 14-20 - 2020<br> iii) Jan 12-13, 15, 18-24, 27-28 - 2021</p> <p>SVP2 data missing for Dec 14-20 (2020) and Jan 24, 27-28 (2021).</p> <p>Automatic Identification System (AIS) data for the ships transiting close to the LOON area (Gulf of La Spezia, Italy). The dataset includes AIS data for:<br> i) Nov 12, 19-20, 23-24 - 2020<br> ii) Dec 1-4, 14-20 - 2020<br> iii) Jan 12-13, 15, 18-24, 27-28 - 2021<br> </p> <p>For reference, see: "Environmental data collected on the CMRE LOON tested during the EUMR project: dataset description", Petroccia, Roberto; Zappa, Giovanni; Cimino, Giampaolo; Grati, Alberto; Alves, João. CMRE-DA-2021-001. July 2021, available at https://www.cmre.nato.int/research/publications/latest-techreports/1638-cmre-da-2021-001</p>
Data for "Diversity of Non-Equilibrium Patterns and Emergence of Activity in Confined Electrohydrodynamically Driven Liquids"
<p>Raw data (microscopy videos and image sequences) and scripts used for the analysis for the publication "Diversity of Non-Equilibrium Patterns and Emergence of Activity in Confined Electrohydrodynamically Driven Liquids", Science Advances 7 (38), eabh1642</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>
Supplementary data to "Changing microbial activities during low salinity acclimation in the brown alga Ectocarpus subulatus"
<p>This data set contains supplementary data related to the paper: “Insights into the potential for mutualistic and harmful host–microbe interactions affecting brown alga freshwater acclimation”: https://onlinelibrary.wiley.com/doi/10.1111/mec.16766</p> <p>Metagenome.zip:<br>This archive contains the reconstructed genomes of the different bacterial bins. The ".gbk" file was used for the reconstruction of metabolic networks. The ".fsa" and ".gff" files were used for "read mapping".</p> <p>Metabolic_networks.zip:<br>This archive contains all bacterial networks in the "padmet" format (see Aite et al. 2018). Furthermore, there is one file containing all gene-reaction associations (for all bins).</p> <p>Expression_data.zip:<br>This file contains algal gene expression data, bacterial gene expression data (number of reads mapping to each feature in each sample), and, lastly, the summarized bacterial expression per metabolic reaction. </p>
Data Release of Cosmic evolution of the incidence of Active Galactic Nuclei in massive clusters: Simulations versus observations
<p>Dataset of the paper "Cosmic evolution of the incidence of Active Galactic Nuclei in massive clusters: Simulations versus observations".</p> <p> </p> <p>All the necessary code to deal with these data can be found in: https://github.com/IvanMuro/agn_frac_data_release</p>
Data for: "Carbon dioxide reduction by lanthanide(III) complexes supported by redox-active Schiff base ligands"
<p>RAW DATA FOR ARTICLE</p> <p>DATE: NOVEMBER 2022</p> <p>TITLE: Carbon dioxide reduction by lanthanide(III) complexes supported by redox-active Schiff base ligands</p> <p>AUTHORS: Nadir Jori, Davide Toniolo, Bang C. Huynh, Rosario Scopelliti, and Marinella Mazzanti*</p> <p>JOURNAL: Inorganic Chemistry Frontiers (RSC) 2020</p> <p>DOI: <a href="https://doi.org/10.1039/D0QI00801J">10.1039/D0QI00801J</a> </p> <p> </p>
Supplemental data for "Computational screening of chemically active metal center in coordinated dipyridyl tetrazine network"
<p>Atomic coordinates of structures used in N. Ud Din, D. Le, T. S. Rahman "Computational screening of chemically active metal center in coordinated dipyridyl tetrazine network", J. Phys.: Condens. Matter .(2023). DOI: 10.1088/1361-648X/acb8f3</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.