Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
29,897
datasets available to search
ShareScore release 0.9.0
Dataset results
29,897 results for “Activities”
Context-Aware Activity Recognition in Logistics (CAARL) – A optical marker-based Motion Capture Dataset
<p><strong>CAARL </strong>is a freely accessible logistics-dataset for human activity recognition, which contains human movement and context information from two subjects. The context information includes the positions of objects such as two picking carts, a packaging table, different racks, a base and three entrances.</p> <p>In the ’Innovationlab Hybrid Services in Logistics’ at TU Dortmund University, two picking and one packing scenarios were recorded using an optical marker based motion capture system. Each subject and object is equipped with several markers. 140 minutes of human movements have been labelled and categorised into 8 activity classes and 19 binary coarse-semantic descriptions, also called attributes. The labelled human movements are synchronised with the context information. They have exactly the same sampling rate (same start and end).</p> <p>The oMoCap data is in csv format. Further formats (e.g. C3D) are available on request.</p> <p>CAARL is based on the set-up and scenarios of the LARa dataset, which contains only human movements. Information about LARa can be found in the dataset and the associated paper:</p> <ul> <li>Dataset: “Logistic Activity Recognition Challenge (LARa) – A Motion Capture and Inertial Measurement Dataset”, Zenodo 2020, DOI: <a href="https://doi.org/10.5281/zenodo.3862782">10.5281/zenodo.3862782</a></li> <li>Paper: “LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes”, Sensors 2020, DOI: <a href="https://doi.org/10.3390/s20154083">10.3390/s20154083</a></li> </ul> <p> </p> <p><strong>If you use the CAARL dataset for research, please cite the following paper: “Context-Aware Human Activity Recognition in Industrial Processes”, Sensors 2021, DOI: <a href="https://doi.org/10.3390/s22010134">10.3390/s22010134</a></strong></p>
Social Network Online Activity of 100+ Users Over Two Years
<p>This dataset contains a precise (error margin is within 5 seconds) activity log of 138 users recorded over a period of approximately two years. It includes users' log in/log off timestamps as well as a device id which was used during the session. An activity heat map is also provided which can be used to determine the online time (in seconds) in a given hour for a given user. The dataset is completely anonymized and is not linked to real peoples' accounts. Russian social network VK was used to record the data.</p> <p>The database is provided in SQLite3 format. The data format is the following:</p> <p><strong>'sessions' </strong>table:</p> <table> <thead> <tr> <th scope="col">Column Name</th> <th scope="col">Data Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>user_id</td> <td>TEXT</td> <td>Unique user's identifier.</td> </tr> <tr> <td>platform</td> <td>INTEGER</td> <td>Device identifier for the session (refer to the table below).</td> </tr> <tr> <td>time_from</td> <td>DATE</td> <td>Timestamp of the session's start.</td> </tr> <tr> <td>time_to</td> <td>DATE</td> <td>Timestamp of the session's end.</td> </tr> </tbody> </table> <p><strong>'map' </strong>table:</p> <table> <thead> <tr> <th scope="col">Column Name</th> <th scope="col">Data Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>user_id</td> <td>TEXT</td> <td>Unique user's identifier.</td> </tr> <tr> <td>hour</td> <td>INTEGER</td> <td>Hour from the 1st Jan 1970 (Unix Epoch / 3600).</td> </tr> <tr> <td>time</td> <td>INTEGER</td> <td>Accumulated online time in the hour (in seconds).</td> </tr> </tbody> </table> <p>Device identifiers:</p> <table> <tbody> <tr> <td>0</td> <td>Unknown</td> </tr> <tr> <td>1</td> <td>Web on Mobile </td> </tr> <tr> <td>2</td> <td>iPhone App</td> </tr> <tr> <td>3</td> <td>iPad App</td> </tr> <tr> <td>4</td> <td>Android App</td> </tr> <tr> <td>5</td> <td>Windows Phone App</td> </tr> <tr> <td>6</td> <td>Windows App</td> </tr> <tr> <td>7</td> <td>Web on Desktop</td> </tr> </tbody> </table> <p> </p> <p>This dataset is associated with the VKWatcher independent research project. The code used to gather the information can be found <a href="https://github.com/Azarattum/VKWatcher-Backend">on GitHub</a>.</p>
Copernicus EMS fire activations delimitations (2012 - 2020) rasterised at 30m and aggregated per year and season
<p>This dataset was created as part of the <a href="https://opendatascience.eu/">Geo-harmonizer project</a>, with the scope of making open data easier to access. It contains all the fire activations (forest fire, wild fire, wildfire) mapped by the<a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid"> Copernicus Emergency Rapid Mapping Service</a> between 2012 and 2020. To obtain these GeoTIFFs, the vector data packages from CEMS were individually downloaded, rasterized and mosaicked per year and season, resampled at 30-m and reprojected to <a href="https://epsg.io/3035">EPSG 3035: ETRS89-extended / LAEA Europe</a>. If no CEMS fire activation was identified in a specific year and season, the raster was not created. The rasters are provided as COG files, type=16Int, nodata value is 255.</p> <p>To allow an easier and faster search through all 2012 - 2020 CEMS fire activations, we have prepared a point vector layer (geojson) containing one point for each fire activation area of interest with the following attributes attached: CEMS identification number <ems_id>, area of interest defined by CEMS <ems_aoi>, URL link to the CEMS activation <ems_link>, year of the event <year_start>, <year_end> , <season> and the name of the <geo_harmonizer_raster> where the 30m rasterised delimitations of the burned areas of the corresponding fire activation can be found. </p> <p>For any additional questions regarding the data please contact the author at codrina.ilie[at]terrasigna.com.</p> <p>The Copernicus Emergency Rapid Mapping Service data access policy is available <a href="https://emergency.copernicus.eu/mapping/sites/default/files/files/CopernicusEMS-Data_and_Dissemination_Policy.pdf">here</a>.</p>
Data for: Epicardial slices: an innovative 3D organotypic model to study epicardial cell physiology and activation.
<p>Raw data set for Npj Regenerative Medicine article: Epicardial slices: an innovative 3D organotypic model to study epicardial cell physiology and activation.</p>
EU Taxonomy on Sustainable Activities (Tidy)
<p>In order to meet the EU’s climate and energy targets for 2030 and reach the objectives of the European green deal, it is vital that we direct investments towards sustainable projects and activities. To achieve this, a common language and a clear definition of what is ‘sustainable’ is needed. This is why the action plan on financing sustainable growth called for the creation of a common classification system for sustainable economic activities, or an EU taxonomy.</p> <p>The EU taxonomy is a classification system, establishing a list of environmentally sustainable economic activities in the areas of Climate mitigation, Climate adaptation, Biodiversity, Circular economy, Water, Pollution prevention. It could play an important role helping the EU scale up sustainable investment and implement the European green deal. The EU taxonomy would provide companies, investors and policymakers with appropriate definitions for which economic activities can be considered environmentally sustainable. It was defined by the Regulation (EU) 2020/852</p> <p>The European Commmission created an EU Taxonomy Compass provides a visual representation of the contents of the EU Taxonomy, starting with the Delegated Act on the climate objectives, as adopted on 4 June 2021. Whilst you can download the EU Taxonomy in xlsx or json format, they are not tidy datasets, and they are not particularly well-suited for calculations or filtering.</p> <p>Reprex created a tidy version of the EU Taxonomy for developing better sustainability indicators into the Green Deal Data Observatory. This tidy version has subjective weights given to broader NACE sections and divisions. We plan to add better, more objective weights for NACE sections or divisions which are only partially matching the EU Taxonomy. For example, H49.32 is part of the sustainable taxonomy, but the entire H49 division is not. Therefore, we use weight=0.5 for this division. The A2 division is fully part of the taxonomy, and we use a weight=1 to refer to this fact. A better weighting would consider the weight of the H492.32 activity within the H49 division in the European economy. What would make such a weighting tricy is that this weight is different for each European country and the EU as a whole.</p>
Molecular Dynamics simulations suggest possible activation and deactivation pathways in hERG channel
<ol> <li>equil_gating_4_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 4 equilibration trajectory</li> <li>equil_gating_6_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 6 equilibration trajectory</li> <li>equil_gating_8_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 8 equilibration trajectory</li> <li>equil_gating_4.dcd: 100 ns NPT trajectory of the hERG closed state with gating charge 4</li> <li>equil_gating_6.dcd: 100 ns NPT trajectory of the hERG closed state with gating charge 6</li> <li>equil_gating_8.dcd: 100 ns NPT trajectory of the hERG closed state with gating charge 8</li> <li>equil_open_assembly_xleap.prmtop: file topology of the hERG open state equilibration trajectory</li> <li>equil_open.dcd: 100 ns NPT trajectory of the hERG open state</li> <li>herg_closed_gating_4.pdb: PDB file of hERG closed state with gating charge 4 after Steered MD simulations</li> <li>herg_closed_gating_6.pdb: PDB file of hERG closed state with gating charge 6 after Steered MD simulations</li> <li>herg_closed_gating_8.pdb: PDB file of hERG closed state with gating charge 8 after Steered MD simulations</li> <li>TMD_O-C_closed_gating_8_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 8 TMD trajectory</li> <li>TMD_O-C_closed_gating_6_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 6 TMD trajectory</li> <li>TMD_O-C_closed_gating_4_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 4 TMD trajectory</li> <li>TMD_O-C_closed_gating_8.dcd: TMD trajectory of the hERG closed state with gating charge 8</li> <li>TMD_O-C_closed_gating_6.dcd: TMD trajectory of the hERG closed state with gating charge 6</li> <li>TMD_O-C_closed_gating_4.dcd: TMD trajectory of the hERG closed state with gating charge 4</li> </ol> <p>MD trajectories (equilibration and Targeted MD trajectories) in dcd format can be visualized using visualization tools such as VMD or PyMol after uploading the topology file.</p> <p>The directory data_supplementary-note-4.tar.bz2 contains the files related to the Supplementary Notes 4: "A practical example of pathway calculation".</p>
Original single session datasets from "Slowly evolving dopaminergic activity modulates the moment-to-moment probability of reward-related self-timed movements."
<p>This archive contains the original single-session recording datasets associated with the paper "Slowly evolving dopaminergic activity modulates the moment-to-moment probability of reward-related self-timed movements" by Allison E Hamilos, Giulia Spedicato, Ye Hong, Fangmiao Sun, Yulong Li, and John A Assad (https://doi.org/10.1101/2020.05.13.094904). Files can be loaded and collated with code from our GitHub repository to reproduce all analyses (https://www.github.com/harvardschoolofmouse).</p>
Net-activism and whistleblowing dataset from Youtube
<p>Files contained in this archive is about net activism and whistleblowing</p> <p>version 1</p> <p>Documents of this dataset have been downloaded from Youtube mainly in 2015 with an update in 2018.</p> <p>Please cite usage of this dataset by</p> <p>Turenne N Net activism and whistleblowing on YouTube: a text mining analysis (2022).<br> </p>
Output data of the models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 1: moderately surface active organics" by Vepsäläinen et al. (2022)
<p>Output data of the different models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 1: moderately surface active organics" by Vepsäläinen et al. (2022).</p> <p>Output data is included for 50 nm particles containing malonic acid (mna), succinic acid (sca) and glutaric acid (glutarica), mixed with ammonium sulphate (AS) in different organic mass fractions. </p> <p>A plotter that allows the user to plot the Köhler curves, surface tensions and organic<br> partitioning factors during droplet growth from the model output data provided is included. </p>
dataset related to article "Seizure activity and brain damage in a model of focal non-convulsive status epilepticus"
<p>Excell files with the values used on the construction of the graphical illustrations for the publication</p>
Data set for "On the Use of Pulsed UV or Visible Light Activated Gas Sensing of Reducing and Oxidising Species with WO3 and WS2 Nanomaterials"
<p>This excel file contains the raw data gathered with the measurements performed under different conditions of illumination for the different sensors. These data have been exploited in the paper "On the Use of Pulsed UV or Visible Light Activated Gas Sensing of Reducing and Oxidising Species with WO3 and WS2 Nanomaterials" DOI: 10.3390/s21113736</p>
Visual-Evoked Potential (VEP) Event-Related Files from the General Anesthesia and Brain Activity (GABA) Study and Infant Sibling Project (ISP)
<p>HAPPE+ER software was optimized for developmental data using a subset of EEG files from 4-month and 10-month old infants in the General Anesthesia and Brain Activity (GABA) Study. While medically necessary, 1-2 million infants each year undergo general anesthesia – a process that sedates brain activity and impacts early sensory experiences during a time typically characterized by rapid neurocognitive development. The GABA study examines sensory and socioemotional neurodevelopment longitudinally from infancy through childhood in individuals who have and who have never undergone general anesthesia during different windows in the first year of life. The GABA study was carried out in accordance with the recommendations of the Institutional Review Board at Boston Children’s Hospital. All caregivers provided assent for their child’s participation in the GABA study and for the release of the deidentified data. </p> <p>To facilitate the use and understanding of HAPPE+ER software, we have provided a subset of the validation files from the GABA study to serve as a tutorial dataset for how to run event-related potential (ERP) data through this automated processing pipeline. Five files (a.raw - e.raw) are from four 4-month and one 10-month old infants during a pattern reversal visual-evoked potential (VEP) paradigm. Pattern reversal occurred every 500 milliseconds. The pattern stimulus onset is indicated in each file by the code: vep+. Data was collected using a 128-channel EGI HydroCel Geodesic Sensor Net and EGI Net Amps 400, sampled at 1000Hz with an online reference to channel CZ. </p> <p>We have also included a subset of files from the Infant Sibling Project (ISP), an investigation examining infants at high versus low familial risk for autism spectrum disorder over the first 3 years of life. Baseline EEG data was collected while a young child sat in a parent’s lap watching a research assistant blow bubbles or show toys for several minutes. The Infant Sibling Project was carried out in accordance with the recommendations of the Institutional Review Board at Boston University and Boston Children’s Hospital (#X06-08-0374), with written informed consent from all caregivers prior to their child’s participation in the study. All files here have been deidentified, including alteration of exact acquisition dates. Acquisition times have not been altered. For additional information about data collection paradigms, and sample studies published on the larger ISP data set, please see the following references:</p> <ol> <li>Levin, A. R., Varcin, K. J., O’Leary, H. M., Tager-Flusberg, H., and Nelson, C. A. (2017). EEG power at 3 months in infants at high familial risk for autism. J. Neurodev. Disord. 9, 1–13.</li> <li>Gabard-Durnam, L.J., Wilkinson, C., Kapur, K. et al. Longitudinal EEG power in the first postnatal year differentiates autism outcomes. Nat Commun 10, 4188 (2019). <a href="https://doi.org/10.1038/s41467-019-12202-9">https://doi.org/10.1038/s41467-019-12202-9</a></li> </ol> <p>Here we provide a subset of the full dataset with a simulated VEP signal added into the data, as example files for HAPPE+ER. To create these files, we selected a subset of 39 spatially-distributed channels in the baseline EEG files and created sixteen 30-second files using continuous segments of relatively artifact-free (clean) baseline data from the full-length files. Next, from 30-second sections of the same individuals’ EEG that were artifact-laden, we ran ICA and extracted artifact independent components (identified by an expert and labeled artifact by both ICLabel and MARA automated algorithms). We inserted the artifact ICs into that individual’s clean 30-second data segment to create an additional 16 artifact-added files. We then selected a channel from a simulated VEP dataset (included here as simulated_full.set) with a stereotyped and prominent simulated VEP waveform, in this case Oz, and added its timeseries (included here as simulated_singleChan.set) to each channel of the clean and artifact-added files to create two VEP datasets with a known ERP morphology (sim-artifact_a-p and sim-clean_a-p). For additional information about the creation of this simulated data and VEP data with a known ERP morphology, please refer to Monachino et al., in revision; DOI: https://doi.org/10.1101/2021.07.02.450946.</p> <p>Additional files included below are the HAPPE+ER data and pipeline quality metric output spreadsheets for the five GABA study data files for an example run, the output spreadsheet containing the ERP timeseries from the generateERPs script, the .mat file containing the parameter settings for HAPPE+ER for that run, an Excel file with the bad channels for each file, and a tutorial document illustrating the results of this example run. </p>
Potential Metabolic Activity, Catalase Activity, Performance traits and Morphological variables of 94 individuals belonging to Podarcis muralis species used in the analysis
<p>Potential Metabolic Activity (ETS26_P, ETS31_P, ETS36_P), Catalase Activity (CAT_P), Performance traits (BITE, SPRINT,CLIMB, MANO) and Morphological variables (snout-vent length (SVL), trunk length (TRL), pileus length (PL), head length (HL), head width (HW), head height (HH), fore limb length (FLL) and hind limb length (HLL) of 94 individuals belonging to <em>Podarcis muralis</em> species. The data was used in the analysis of the paper entitled: Is It Function or Fashion? An Integrative Analysis of Morphology, Performance, and Metabolism in a Colour Polymorphic Lizard, by authors Verónica Gomes, Anamarija Žagar, Guillem Pérez i de Lanuza, Tatjana Simčič and Miguel A. Carretero, published in the journal Diversity 2022, 14, 116. <a href="https://doi.org/10.3390/d14020116">https://doi.org/10.3390/d14020116</a></p>
A saturation-mutagenesis analysis of the interplay between stability and activation in Ras
<p>Dataset for the Hidalgo et al. eLife paper DOI: <a href="https://doi.org/10.7554/eLife.76595">https://doi.org/10.7554/eLife.76595</a></p>
Dataset: A New Infrared Criterion for Selecting Active Galactic Nuclei to Lower Luminosities
<p>The dataset accompanying the paper "A New Infrared Criterion for Selecting Active Galactic Nuclei to Lower Luminosities" (Hviding et al. in prep)</p>
Biological data science courses at UMONS, Belgium: student's activity for 2019-2020
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2019-2020.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software. The courses were also taught at the Campus Charleroi by Raphaël Conotte (<a href="mailto:raphael.conotte@umons.ac.be">raphael.conotte@umons.ac.be</a>) that also contributed to a part of the learnr exercises and of the inline course.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io/">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io/">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Biological data science courses at UMONS, Belgium: student's activity for 2020-2021
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2020-2021.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software. The courses were also taught at the Campus Charleroi by Raphaël Conotte (<a href="mailto:raphael.conotte@umons.ac.be">raphael.conotte@umons.ac.be</a>) that also contributed to a part of the learnr exercises and of the inline course.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Biological data science courses at UMONS, Belgium: student's activity for 2018-2019
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2018-2019.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io/">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io/">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Luangwa Rift Active Fault Database v1.0
<p>First release of the Luangwa Rift Active Fault Database for the submission of a manuscript to EGU Solid Earth.</p> <p>Active fault database for the Luangwa Rift, Zambia compiled by Tess Turner, Luke Wedmore and Juliet Biggs at University of Bristol.</p> <p>The Luangwa Rift Active Fault Database (LRAFD) is a freely available open-source geospatial database of active fault traces within the Luangwa Rift, Zambia.</p> <p>The active fault database has been designed and released in line with the Global Earthquake Model standards. Full details of the criteria used to assess activity will be released in a publication that is currently in preparation.</p> <p><strong>Citation</strong><br> Please cite the latest release of this database on Zenodo in addition to the following manuscript:<br> Turner, T. Wedmore, L.N.J., Biggs, J. Williams, J.N., Sichingabula, H.M., Kabumbu, C., Banda, K. The Luangwa Rift Active Fault Database and fault reactivations along the southwestern branch of the East African Rift. _Submitted to EGU Solid Earth_</p> <p><strong>Data Format</strong><br> The LRAFD is a geospatial database containing a collection of active fault traces in GIS vector format. Each fault is mapped as a single continuous GIS feature, and has associated metadata that describe the geometry of the fault and various aspects of its exposure and the methodology used to map the fault.</p> <p>The list below describes the attributes within the LRAFD. These attributes are based on the <a href="https://github.com/cossatot/gem-global-active-faults">Global Earthquake Model Global Active Faults Database</a> (<a href="https://github.com/cossatot/gem-global-active-faults">GEM-GAFD</a>; <a href="https://doi.org/10.1177%2F8755293020944182">Styron and Pagani, 2020</a>). Note, we do not currently include all attributes from the <a href="http://github.com/cossatot/gem-global-active-faults">GEM-GAFD</a> as these data have not been collected in the Luangwa Rift. It is the intention that future versions of this database will include more attributes. No assessment is made of the seismogenic properties of the faults in the LRAFD as this is subjective. These data have been compiled in the publication associated with this database.</p> <p><br> <strong>Data Table</strong></p> <table> <caption>Luangwa Rift Active Fault Database Attributes</caption> <thead> <tr> <th scope="col">Attribute</th> <th scope="col">Data Type</th> <th scope="col">Description</th> <th scope="col">Notes</th> </tr> </thead> <tbody> <tr> <td>LRAFD_ID</td> <td>integer</td> <td>Unique Fault IDentification number assigned to each fault trace</td> <td> </td> </tr> <tr> <td>Fault_Name</td> <td>string</td> <td>Name of Fault</td> <td>Assigned using local geographic features or towns</td> </tr> <tr> <td>Dip_Direction</td> <td>string</td> <td>Compass quadrant of fault dip direction</td> <td> </td> </tr> <tr> <td>slip_type</td> <td>string</td> <td>kinematic type of fault</td> <td>e.g. normal, reverse, sinistral-strike slip, dextral-strike slip</td> </tr> <tr> <td>Fault_Length</td> <td>decimal</td> <td>Straight line distance between the tips of the fault</td> <td> </td> </tr> <tr> <td>GeomorphicExpression</td> <td>string</td> <td>Geomorphic feature/features used to identify the fault trace and its extent</td> <td>e.g. escarpment, fault scarp, offset sedimentary feature</td> </tr> <tr> <td>Method</td> <td>string</td> <td>DEM or geologic dataset used to identify and map the fault trace</td> <td>e.g. digital elevation model hillshade, slope map</td> </tr> <tr> <td>Confidence</td> <td>integer</td> <td>Confidence of recent (Quaternary) activity</td> <td>Ranges from 1-4, 1 if high certainty, 4 if low certainty</td> </tr> <tr> <td>ExposureQuality</td> <td>integer</td> <td>Fault exposure quality</td> <td>1 if high, 2 if low</td> </tr> <tr> <td>EpistemicQuality</td> <td>integer</td> <td>Certainty of whether a fault exists there</td> <td>1 if high, 2 if low</td> </tr> <tr> <td>Accuracy</td> <td>integer</td> <td>Coarsest scale at which fault trace can be mapped, expressed as the denominator of the map scale</td> <td>reflects the prominence of the fault's geomorphic expression</td> </tr> <tr> <td>GeologicalMapExpression</td> <td>string</td> <td>extent of correlation between fault traces and legacy geological map</td> <td>whether faults have been previously mapped and/or follow geological contacts</td> </tr> <tr> <td>Notes</td> <td>string</td> <td>Any additional or relevant information regarding the fault</td> <td> </td> </tr> <tr> <td>References</td> <td>string</td> <td>Relevant literature/geological maps where the fault is mentioned/described</td> <td> </td> </tr> </tbody> </table> <p> </p> <p><strong>File Formats</strong><br> Following the <a href="http://github.com/cossatot/gem-global-active-faults">GEM-GAFD</a>, this database is provided in a variety of GIS vector file formats. <a href="http://geojson.org/">GeoJSON</a> is the version of record, and any changes should be made in this version, before they are converted to other filed formats using the <a href="https://github.com/LukeWedmore/luangwa_rift_active_fault_database/blob/main/convert.sh">convert.sh</a> shell script available in this repository. This script uses the <a href="https://gdal.org/">GDAL</a> tool <a href="https://gdal.org/programs/ogr2ogr.html">ogr2ogr</a> and is adapted from a script posted by Richard Styron (<a href="https://github.com/cossatot/central_am_carib_faults/blob/master/convert.sh">https://github.com/cossatot/central_am_carib_faults/blob/master/convert.sh</a>), who we thank for making this publicly available. The other versions available are <a href="https://support.esri.com/en/white-paper/279">ESRI Shapefile</a>, <a href="https://earth.google.com">KML</a>, <a href="https://www.generic-mapping-tools.org/">GMT</a> and <a href="https://www.geopackage.org/">Geopackage</a>.</p> <p>Note that in the <a href="http://support.esri.com/en/white-paper/279">ESRI Shapefile</a> format, the length of the attribute are restricted in length by the format, so we advise against using this format.</p> <p><strong>Version Control</strong><br> This version of the database is v1.0 and is associated with the release of the data for submission of the associated manuscript.</p> <p>It is intended that this database is updated in future versions by both the authors and other users. As such we encourage edits of the [GeoJSON] file and the submission of pull requests on the <a href="https://github.com/LukeWedmore/luangwa_rift_active_fault_database">associated github site</a>. Please contact Luke Wedmore (<<a href="mailto:luke.wedmore@bristol.ac.uk?subject=Luangwa%20Rift%20Active%20Fault%20Databse%20Zenodo%20Release">luke.wedmore@bristol.ac.uk</a>>) for information or to report errors in the database.</p> <p><strong>References</strong><br> Styron, Richard, and Marco Pagani. “The GEM Global Active Faults Database.” Earthquake Spectra, vol. 36, no. 1_suppl, Oct. 2020, pp. 160–180, doi:10.1177/8755293020944182.<br> </p> <p> </p>
Multi-omic analysis of the Arabidopsis clock activator mutant rve 4 6 8 reveals connections to carbohydrate metabolism and proteasome regulation
<p>Plants are able to sense changes in their light environments, such as the onset of day and night, as well as anticipate these changes in order to adapt and survive. Central to this ability is the plant circadian clock, a molecular circuit that precisely orchestrates plant cell processes over the course of a day. REVEILLE proteins (RVEs) are recently discovered members of the plant circadian circuitry that activate the evening complex and PRR genes to maintain regular circadian oscillation. The RVE 8 protein and its two homologs, RVE 4 and 6, have been shown to limit the length of the circadian period, with rve 4 6 8 triple-knockout plants possessing an elongated period along with increased leaf surface area, biomass, cell size and delayed flowering relative to wild-type Col-0 plants. Here, using a multi-omics approach consisting of phenomics, transcriptomics, proteomics, and metabolomics we draw novel connections between RVE8-like proteins and a number of core plant cell processes. In particular, we reveal that loss of RVE8-like proteins results in altered carbohydrate, organic acid and lipid metabolism, including a starch excess phenotype at dawn. We further demonstrate that rve 4 6 8 plants have lower levels of 20S proteasome subunits and possess significantly reduced proteasome activity, potentially explaining the increase in cell-size observed in RVE8-like mutants. Overall, this robust, multi-omic dataset, provides substantial new insights into the far reaching impact RVE8-like proteins have on the diel plant cell environment.<br> <br> This dataset has the raw search outputs for the mass-spec analysis for this manuscript. </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.