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.
9,882
datasets available to search
ShareScore release 0.9.0
Dataset results
9,882 results for “scales”
Classification and frequency of climate change drivers and responses of small-scale fishers found in literature review
<p>Climate change hazards were classified into resource availability and fishing operations or both following the framework proposed by Cheung et al (2012). Response units were firstly classified into overarching responses and then categorized as suggested by the adaptive-transformative framework of Barnes et al.<sup> </sup>(2020). Adaptation units that did not represent an active adaptation response were classified as remaining. More than one hazard could be attributed to each fishers' response.</p>
LSPO: A Large-Scale Physics ORCiD-Linked Dataset for Author Name Disambiguation
<p>The LSPO dataset, a Large-Scale Physics ORCiD-Linked Dataset for Author Name Disambiguation is comprised of 554,962 NASA/ADS publications linked to 125,486 unique researchers through ORCiD identifiers. The available meta-data fields are: ORCiD identifier, author name, affiliation, title, asbtract, and name block. The dataset can be utilized to make pairs or triplets for training a author name disambiguation model. </p>
Datasets for "Advancing Drug-Target Interactions Prediction: Leveraging a Large-Scale Dataset with a Rapid and Robust Chemogenomic Algorithm"
<p>All datasets required to reproduce the results of publication "Drug-Target Interactions Prediction at Scale: the Komet Algorithm with the LCIdb Dataset"</p>
Fine-scale anthropogenic nutrient input data for 8 watersheds along the Saint Lawrence River (1981, 2021)
<p>We quantified Net Anthropogenic Nitrogen and Phosphorus Inputs (NANI-NAPI) at two scales (the finest one, the municipality, and a coarser one, the county) for all municipalities and counties of 8 watersheds in Québec, Canada, for 1981 and 2021.</p> <p>The datasets here report 1) watershed NANI and NAPI values accounted from both scales for 1981 and 2021, 2) municipality-scale NANI values for each municipality in the 8 watersheds, and 3) certain components of the municipality-scale NANI for all municipalities in the Yamaska watershed.</p>
Dataset for Spectral scaling of unstably-stratified atmospheric flows: turbulence anisotropy and the low frequency spread
<p>30 min turbulence statistics and spectra of 13 datasets from flat to highy complex terrain. Data only cover unstable stratification. </p> <p>Dataset is a companion to the manuscript Charrondiere, C., Stiperski, I., 2024: Spectral scaling of unstably-stratified atmospheric flows: turbulence anisotropy and the low frequency spread. Quarterly Journal of the Royal Meteorological Society, https://doi.org/10.1002/qj.4811<strong><br></strong></p> <p> </p>
GEOLAB-PEBSTER-Deltares: Small-scale experiments on Piled Embankments with Basal Steel Mesh Reinforcement - Measurements.
<p><strong>DOI: 10.5281/zenodo.12627309</strong></p> <h1><strong>Small-scale experiments on basal steel-mesh reinforced piled embankments (PEBSTER, a transnational access project of GEOLAB)</strong></h1> <p>Welcome to the GEOLAB-PEBSTER-Deltares project on Zenodo! This repository contains the measurement data from four small-scale experiments on Piled Embankments with Basal Steel Reinforcement (PEBSTER). Pile-supported (PS) embankments with basal geosynthetic reinforcement (GR) are commonly built in soft soil areas (van Eekelen and Han, 2020). Steel mesh reinforcement (SR) is particularly appealing for high embankments (Topolnicki et al., 2019). Its high axial stiffness, compared to geosynthetics, reduces horizontal deformation at the embankment base, minimizes bending moments in the piles, and ultimately enhances embankment stability.</p> <h2>PEBSTER</h2> <p>This dataset includes measurements from four small-scale tests on steel-reinforced piled embankments, conducted in the Deltares laboratory (van Eekelen et al., 2024a,b). These tests are part of a broader research initiative called Piled Embankments with Basal Steel Reinforcement (PEBSTER), which also includes a large-scale test (Schneider et al., 2024a,b). The small-scale tests were conducted at Deltares, Delft, Netherlands, while the large-scale test was conducted in Darmstadt, Germany, at the Institute of Geotechnics, Technical University of Darmstadt.</p> <h2>GEOLAB</h2> <p>GEOLAB is a project of the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 101006512, addressing Europe's Critical Infrastructure (CI) challenges in the water, energy, urban, and transport sectors.</p> <p>The GEOLAB Research Infrastructure (RI) consists of 11 unique installations across Europe to study subsurface behavior and its interaction with structural CI elements and the environment. During the GEOLAB Transnational Access (TA), users outside the consortium gained access to the GEOLAB installations to perform research and innovation.</p> <h2>Dataset of four small-scale experiments</h2> <p>Here, we share the measurement data from the small-scale experiments that were part of one of the GEOLAB TA projects: PEBSTER. Four piles (diameter 0.1 m, centre to centre 0.55m) passed through a steel plate, that supported a foam cushion, that was sealed and soaked. A tap allowed for drainage of the foam cushion, simulating the consolidation of the subsoil between the piles. The 0.55 m high embankment consisted of medium coarse sand, and was reinforced at its base with a steel mesh reinforcement. A surcharge load up to 100 kPa was applied with a water cushion.<br><br><span>The load distribution is measured by pressure cells and load transducers. </span>Soil strains and displacements were monitored at five elevations within the fill, utilizing distributed fibre optic sensing (DFOS) technology from the Nerve-Sensors family, as depicted in Figure 2. Additionally, the steel mesh reinforcement was extensively instrumented with optic fibres.</p> <h2>PEBSTER Research group</h2> <p>The project was conducted by a research group that includes Deltares, Netherlands, the Institute of Geotechnics of the Technical University of Darmstadt, Germany, Keller (Germany, France, Poland), SHM System, Poland, and FOLAB, Germany.</p> <p> </p>
Archetypes of climate change adaptation among large-scale arable farmers in southern Romania
<p>Supplementary material belonging to the publication.</p> <p>Two files:</p> <p>1. Excel file with database containing raw data and information resulted from surveying a sample of 30 farmers/farm managers in southern lowlands of Romania between April and June 2020.</p> <p>2. PDF with interview guideline</p>
Functionalized mm-scale vapor cells for alkali-metal spectroscopy and magnetometry
<p>The entry contains numerical data displayed in Figures 3 and 4 of the manuscript titled above, see arXiv preprint: <a href="2405.10715" target="_blank" rel="noopener">https://arxiv.org/abs/2405.10715</a></p> <p><strong>Authors:</strong> <a href="https://arxiv.org/search/physics?searchtype=author&query=Raghavan,+H">Harini Raghavan</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Tayler,+M+C+D">Michael C. D. Tayler</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Mouloudakis,+K">Kostas Mouloudakis</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Rae,+R">Rachel Rae</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=L%C3%A4hteenm%C3%A4ki,+S">Sami Lähteenmäki</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Zetter,+R">Rasmus Zetter</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Laine,+P">Petteri Laine</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Haesler,+J">Jacques Haesler</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Balet,+L">Laurent Balet</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Overstolz,+T">Thomas Overstolz</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Karlen,+S">Sylvain Karlen</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Mitchell,+M+W">Morgan W. Mitchell</a></p> <p><strong>Abstract:</strong> We describe micro-fabricated rubidium vapor cells with integrated temperature-control functionality and demonstrate their suitability for use in miniaturized ultra-sensitive magnetometers. These functionalized vapor cells (FVCs) embody a dual-chamber design in low-conductivity silicon with anti-permeation coatings and micro-structured thin-film platinum surface traces as resistive heaters and temperature sensors. Thermal tests show our ability to control alkali metal distribution within the FVCs, ensuring a clean sensing chamber for optical measurements. Optical absorption spectroscopy is used to correlate the temperature readings with vapor density and to measure buffer gas pressure, of interest for optimizing sensitivity. Finally, we demonstrate zero-field resonance magnetometry with 18 fT/√Hz sensitivity in the 10 Hz to 100 Hz band, limited by laser noise and magnetic shield noise, which indicates that the functionalization does not introduce significant magnetic noise.</p>
Final model for "Automated Large-Scale Full Seismic Waveform Inversion for North America and the North Atlantic" by Krischer et al. (2018)
<p>The HDF5 file contains the final model of the paper "Automated Large-Scale Full Seismic Waveform Inversion for North America and the North Atlantic" by Krischer et al. (2018), soon to be published in the Journal of Geophysical Research - Solid Earth.</p> <p>The "coordinates_0", "coordinates_1", and "coordinates_2" data sets are the coordinates along each dimension, here colatitude in degree, longitude in degree, and radius in meter, respectively. The regularly sampled data is available in five 3D-arrays in the "data" group: "vp", "vsv", "vsh", "rho", and "Q". Velocities are defined at 1 Hertz and are given in km/s, the density in kg/m^3. Q is Q_mu.</p> <p>The coordinates have to be rotated to yield true spherical Earth coordinates. They have to be rotated around on axis vector of 0.766044443118978/0.6427876096865393/0.0 in cartesian x/y/z coordinates by -30.0 degrees. Conversion of spherical to cartesian coordinates happens with the standard convention:</p> <p>x = r sin(theta) cos(phi)<br> y = r sin(theta) sin(phi)<br> z = r cos(theta)</p>
Spectral irradiance at Lammi Biological Station Research Forest 2015: for assessing scale-wise similarity of curves with a thick pen
<p>This dataset contains records of the solar spectral energy irradiance (W m<sup>-2</sup> nm<sup>-1</sup>) in the understorey of forest stands at Lammi Biological Station, southern Finland (61◦ 3.24’ N, 25◦ 118 2.23’ E) during the spring of 2015. These spectra allow the change in spectral energy irradiance to be followed through the period of canopy leaf flush. Records are the average of recorded spectra from four points recorded at 40-cm above the forest floor using a Maya 2000 Pro array spectrometer. Spectra were recorded from exactly the same location on three dates, 2015-04-25, 2015-05-22, and 2015-06-05, before, during and after leaf flush. Data were recorded from the understorey of a young Betula stand, an old Betula stand, an old mixed Betula stand, a Quercus stand, and a Picea stand, in three positions: shade, semi-shade from leaves, and full sun in a sunfleck. On each occasion control measurements of spectral energy irradiance in full sun of an open field were also recorded at the beginning, middle and end of each measurement period. All measurements were made during the 2 hours either side of solar noon, on clear-sky days. Details of the sampling method and interpretation are given in the paper, Hartikainen et al., (2018) in Ecology and Evolution, which showcases the use of Thick Pen Transform to compare spectra.</p>
Assessment of mutation probabilities of KRAS G12 missense mutants and their long-time scale dynamics by atomistic molecular simulations and Markov state modeling: Datasets.
<p>Datasets related to the publication [1].<br> Including:</p> <ul> <li>KRAS G12X mutations derived from COSMIC v.79 [http://cancer.sanger.ac.uk/cosmic/] (KRAS_G12X_mut_COSMICv79..xlsx)</li> <li>RMSFs (300-2000ns) of GDP-systems (300_2000rmsf_GDP_systems_RAW_AVG_SE.xlsx)</li> <li>RMSFs (300-2000ns) of GTP-systems (300_2000RMSF_GTP_systems_RAW_AVG_SE.xlsx)</li> <li>PyInteraph analysis data for salt-bridges and hydrophobic clusters (.dat files for each system in the PyInteraph_data.zip-file)</li> <li>Backbone trajectories for each system (residues 4-164; frames for every 1ns). Last number (e.g. _1) refers to the replica of the simulated system.</li> <li>backbone_4-164.gro/.pdb/.tpr -files (resid 4-164) </li> </ul> <p><br> [1] Pantsar T et al. Assessment of mutation probabilities of KRAS G12 missense mutants and their long-time scale dynamics by atomistic molecular simulations and Markov state modeling. <em>PLoS Comput Biol Submitted</em> (2018)</p>
Chapter 3: Scale Separation Reliability: What Does it Mean in the Context of Comparative Judgement?
<p>This is the supplementary material for Chapter 3 of the dissertation "Beyond a Mere Rank Order: The Method, the Reliability and the Efficiency of Comparative Judgment" and the article "Scale separation reliability: What does it mean in the context of comparative judgment?" published in "Applied Psychological Measurement".</p>
GPP: Site-scale and global model outputs from P-model used for Stocker et al. (2019) Nature Geosci.
<p><strong>Data from article Stocker et al. (in review) *Nature Geosci.*</strong></p> <p>The datasets provided here include:</p> <ul> <li>Site-level GPP model results from the P-model (Wang et al., 2017)</li> <li>Model outputs from global simulations with the P-model (Wang et al., 2017) as implemented for the study by Stocker et al. (2019)</li> </ul> <p>This data may be used to partly reproduce results presented in Stocker et al. (2019) <em>Nature Geosci</em>. "Partly" because we used data for our analysis that was not open access but was confidentially shared with us. This includes remote sensing-based GPP estimates from the BESS and VPM models. Other open access data that was used for the analysis may not be distributed under this DOI. This includes FLUXNET 2015 data and MODIS data.</p> <p>For reproducing results of Stocker et al. (2019) regarding site-scale evaluations, run for example the scripts `plot_bias_all.R` and `plot_bias_problem.R`, available from <a href="https://github.com/stineb/soilm_global">Github</a> or <a href="http://doi.org/10.5281/zenodo.1423328">Zenodo</a>, using CSV files provided here (see comments in scripts). For more insight, including analysis of global simulation outputs, see RMarkdown file `si_soilm_global.Rmd`. This renders the supplementary information PDF document provided along with Stocker et al. (2019), which is available also on <a href="http://rpubs.com/stineb/si_soilm_global2">RPubs</a>.</p> <p>The present datasets are prepared by script `prepare_data_openaccess.R ` on <a href="https://github.com/stineb/soilm_global">Github</a> or <a href="https://zenodo.org/record/1286966#.W6TFipMzbUI">Zenodo</a>.</p> <p><strong>Data description</strong></p> <p><em>Site-level data</em></p> <p>Data is provided as CSV files:</p> <ul> <li>`gpp_daily_fluxnet_stocker18natgeo.csv`: Daily data for full time series (not including MODIS GPP)</li> <li>`gpp_8daily_fluxnet_stocker18natgeo.csv`: Data aggregated to 8-day periods corresponding to MODIS dates (including MODIS GPP)</li> <li>`gpp_alg_daily_fluxnet_stocker18natgeo.csv`: Data filtered to periods with substantial soil moisture effects ("fLUE droughts" following Stocker et al. (2018a))</li> <li>`gpp_alg_8daily_fluxnet_stocker18natgeo.csv`: Data aggregated to 8-day periods and filtered to periods with substantial soil moisture effects.</li> </ul> <p>Each column is a variable with the following name and units (not all variables are available in all files):</p> <ul> <li>`site_id`: FLUXNET site ID </li> <li>`date`: Date of measurement, units: YYYY-MM-DD</li> <li>`gpp_pmodel` and `gpp_modis`: Simulated GPP from the P-model and MODIS (see Stocker et al. (2018b), Methods, RS models), units: g C m-2 d-1 (mean across 8 day periods in respective files)</li> <li>`aet_splash`: Simulated actual evapotranspiration from the SPLASH model (Davis et al., 2017), units: mm d-1</li> <li>`pet_splash`: Simulated potential evapotranspiration from the SPLASH model (Davis et al., 2017), units: mm d-1</li> <li>`soilm_splash`: Soil moisture simulated by the SPLASH model (Davis et al., 2017), normalised to vary between zero and one at the maximum water holding capacity, unitless.</li> <li>`flue`: fLUE estimate from Stocker et al. (2018). Estimates soil moisture stress on light use efficiency from flux data, unitless.</li> <li>`beta_a`, `beta_b`, and `beta_c`: Empirical soil moisture stress, used as multiplier to simulated GPP as described in Stocker et al. (2018b), unitless.</li> </ul> <p><em>Global P-model simulation outputs</em></p> <p>GPP and soil moisture output is provided as NetCDF files for simulations s0, and s1b (see Stocker et al. (2018b)). All meta information is provided therein. Files for simulation s1b are names as follows (for outputs from other simulations replace s1b with other simulation name). The fraction of each gridcell covered by land (not open water or ice) is given by separate file `s1b_fapar3g_v2_global.fland.nc`.</p> <ul> <li>`s1b_fapar3g_v2_global.d.gpp.nc`: Daily GPP from simulation s1b.</li> <li>`s1b_fapar3g_v2_global.d.wcont.nc`: Daily soil moisture from simulation s1b (is identical in other simulations, therefore not provided.)</li> </ul> <p>Due to limited total file size allowed for uploads to Zenodo, only outputs from s1b are provided here. Other outputs may be obtained upon request addressed to benjamin.stocker@gmail.com. </p> <p><strong>References</strong></p> <p>Davis, T. W. et al. Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture. Geoscientific Model Development 10, 689–708 (2017).<br> Hufkens, K. khufkens/gee_subset: Google Earth Engine subset script & library. (2017). doi:10.5281/zenodo.833789Running, S. W. et al. A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production. Bioscience 54, 547–560 (2004).<br> Stocker, B. et al., Quantifying soil moisture impacts on light use efficiency across biomes, New Phytologist, doi: 10.1111/nph.15123 (2018a).<br> Stocker, B. et al., Satellite monitoring underestimates the impact of drought on terrestrial primary productivity, Nature Geoscience (2019).<br> Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat Plants 3, 734–741 (2017).<br> </p>
Non-destructive thickness mapping of wafer-scale hexagonal boron nitride down to a monolayer
<p>Raw data associated with the publication "Non-Destructive Thickness Mapping of Wafer-Scale Hexagonal Boron Nitride<br> Down to a Monolayer" published in ACS Applied Materials & Interfaces under the DOI: 10.1021/acsami.8b08609</p>
Database of Nightside, High-latitude Ionosphere Meso-scale Flow Characteristics
<p>This database is a compilation of nightside, high-latitude ionosphere meso-scale flow characteristics built on those used in Gabrielse et al. [2018] (<a href="https://doi.org/10.1029/2018JA025440">https://doi.org/10.1029/2018JA025440</a>). It is the most complete version. If you would like to use the database, please contact Christine Gabrielse (cgabrielse@ucla.edu, cgabrielse@gmail.com, and/or christine.gabrielse@aero.org). Depending on how the results are used, the main authors request co-authorship on publications that utilize this database. </p> <p>The methodology and selection criteria can be found in Gabrielse et al. [2018] (<a href="https://doi.org/10.1029/2018JA025440">https://doi.org/10.1029/2018JA025440</a>). </p> <p>The following list describes the columns in each data file labeled, ***_FLOW-DATA-PCvsAO_YYYY.txt <br> The first three letters (RNK or SAS) designate the station used (Rankin Inlet or Saskatoon).<br> Files named ***_FLOW-DATA-PCvsAO_poleward_YYYY.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> <br> AO=Auroral Oval for Rankin Inlet; equatorward of the auroral oval for Saskatoon (not used)<br> PC=Polar Cap for Rankin Inlet; Auroral Oval for Saskatoon</p> <p>(Note: the data files for RNK and SAS have the same format, so the PC designator means flows above the pertinent boundary (polar cap boundary for RNK, auroral oval equatorward boundary at SAS) and the AO designator means flows below the pertinent boundary.)</p> <p> time [YYYYMMDDhhmmss]<br> flagAO [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> flagPC [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> FWHMavg_AO [degrees]<br> FWHMkmavg_AO=[km]<br> longtestranges=[ignore]<br> Velmaxavg_AO=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_AO=[m/s, determined from the Gaussian fits]<br> FWHMavg_PC=[degrees]<br> FWHMkmavg_PC=[km]<br> Velmaxavg_PC=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_PC=[m/s, determined from the Gaussian fits]</p> <p>;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;<br> For the bearings/orientation, see the orientation text files. The following four variables were calculated in a first step but are not<br> those used in the paper. They were not found with the strict selection criteria. **Please do not use.**<br> mbearingAO=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> mbearingPC=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)] <br> gbearingAO=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> gbearingPC=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> ;;;;;;;;;;;;;;;<br> minlatAO=[degrees, min geographic latitude of the flow]<br> maxlatAO=[degrees, max geographic latitude of the flow]<br> minlatPC=[degrees, min geographic latitude of the flow]<br> maxlatPC=[degrees, max geographic latitude of the flow]<br> mltAO=[degrees (MLT)]<br> mltPC=[degrees (MLT)]<br> AE=[nT]<br> AL=[nT]<br> SYMH=[nT]<br> IMFBy=[nT]<br> IMFBz=[nT] <br> F107=[sfu]</p> <p>The following list describes the columns in each data file labeled, ***_orientation_YYYY.txt <br> Files named ***_orientation_poleward_YYYY.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> The orientation was determined when enough bearings between RGs were available. See Gabrielse et al. [2018] for description. <br> https://doi.org/10.1029/2018JA025440 <br> AO=auroral oval<br> PC=polar cap</p> <p> time [YYYYMMDDhhmmss]<br> mbearingAO [degrees clockwise from magnetic North]<br> gbearingAO [degrees clockwise from geographic North]<br> mbearingPC [degrees clockwise from magnetic North]<br> gbearingPC [degrees clockwise from geographic North]</p> <p>The following list describes the columns in each data file labeled, ***_SPEC_TEST_***_noRG1-2.txt</p> <p> time [YYYYMMDDhhmmss]<br> RG [the range gate number at which the polar cap boundary was determined at RNK, or the auroral oval's equatorial boundary at SAS]</p>
Dataset _ Influence of liquid-to-biogas ratio and alkalinity on the biogas upgrading performance in a demo scale algal-bacterial photobioreactor
<p>This is the dataset used for the publication of the journal article title<em> “</em><strong>Influence of liquid-to-biogas ratio and alkalinity on the biogas upgrading performance in a demo scale algal-bacterial photobioreactor</strong><strong>”. </strong>In this dataset there is all the information collected in the experimentation process.</p>
Testing Smart City environmental monitoring technology using small scale temporary cities
<p>This is the data used for:</p> <blockquote> <p>S. J. Johnston <em>et al</em>., "Testing Smart City environmental monitoring technology using small scale temporary cities," <em>2019 IEEE 5th World Forum on Internet of Things (WF-IoT)</em>, 2019, pp. 578-583, doi: 10.1109/WF-IoT.2019.8767274.</p> </blockquote> <p> </p> <p><em><strong>Abstract:</strong></em></p> <p>Exposure to particulate matter has been identified as a major health problem worldwide. Established measurement<br> techniques require equipment costing many thousands of dollars and specialist expertise to maintain. Ongoing research<br> is investigating the use of low cost <$300 sensors to enable greater temporal-spatial density of readings to be taken. There<br> are questions about the suitability and reliability of these low-cost sensors, queries which can be addressed by deploying<br> and evaluating the sensors in a real world application. We propose festival site as small scale cities to enable a short term<br> deployments and evaluation of sensors. We present data from these devices and experiences gained from using a festival site as a substitute for a city.</p> <p><em><strong>Files:</strong></em></p> <p>1 - timeLapse: mp4 file presenting a time lapse of the measurements realised during the festival<br> 2 - sensor_data: csv file containing the 5 min averaged data from all the sensors deployed and their coordinates used to generate the graph and the maps in the paper</p> <p>3 - workshop.pdf: instructions to run the workshop and code used for the workshop</p>
Large scale and information effects on cooperation in public good games
<pre>This dataset accompanies the paper "Large scale and information effects on cooperation in public good games", https://doi.org/10.1038/s41598-019-50964-w It records participant decisions in a set of Public Goods Games (see details in the paper). Explanation of the data fields: #participant_id: identification number for each participant in each treatment #player_alive: (for each round) 1=participant is still playing; 0=participant has been banned for forgetting three decisions or did not show up the first day. #group_avg_contribution: Average contribution #player_contribution: participant individual contribution to the PGG #round_number: round number #gender #age #treatment: see the publication for details regarding the different treatments # IBSEN metadata # 2017A2ECOCOOPANSA01ONL0000ESPMAD </pre>
Rethinking the fundamental unit of ecological remote sensing: Estimating individual level plant traits at scale
<p>derived data of leaf and plant structural traits for two National Ecological Observatory Network (NEON) Airborne Observatory Platform (AOP) sites. Dataset contains spatial explicit information for 4.5 million trees, and include: Nitrogen (%mass), Phosphorus (%mass), Leaf mass per area (g m<sup>-2</sup>), diameter at breast height (cm), crown area (m2), tree height (m) and other physical topographic variables (Albedo, Elevation, Slope, Aspect). data are associated to the </p>
Images of apples for the use of the Viola-Jones method. Data set no. 2 - grey scale.
<p>The database contains pictures of apples made at different angles, from different sides and containing different varieties. In this way, two bases of apple images were created (each database contains 1,100 images). This set is data set no. 2 - grey scale: processed images in shades of gray. The photos were prepared for the best possible detection process in the Viola-Jones method. These photo bases with apples can be used to teach machines to recognize specific varieties and count apples.</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.