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.
7,742
datasets available to search
ShareScore release 0.9.0
Dataset results
7,742 results for “individuals”
Distancia-Covid Individual Contact Estimates for Spain
<p>Individual estimates of age-specific contact patterns in Spain during the Covid-19 pandemic. This data was generated from the CSIC Distancia-Covid survey (https://distancia-covid.csic.es/). It includes estimated numbers of coresidents and non-coresident contacts for each individual represented in the Spanish Labor Force Survey during 2020 and 2021. These estimates do not relate to any identifiable person; rather they provide information about the overall distribution of contacts across the population. These individual estimates have been used to calculate the mean age-specific contacts provided in <a href="https://doi.org/10.5281/zenodo.5983902">https://doi.org/10.5281/zenodo.5983902</a>. </p> <p>This dataset contains the following files:</p> <ul> <li>distancia_covid_individual_contact_estimates_metadata_dictionary.csv: Variable definitions</li> <li>distancia_covid_individual_contact_estimates_spain_cores_wave1.csv.gz: Estimates of coresidents during wave 1 of the Distancia-Covid survey (14 May 2020 through 10 June 2020)</li> <li>distancia_covid_individual_contact_estimates_spain_cores_wave2.csv.gz: Estimates of coresidents during wave 2 of the Distancia-Covid survey (24 July 2020 through 31 August 2020)</li> <li>distancia_covid_individual_contact_estimates_spain_cores_wave3.csv.gz: Estimates of coresidents during wave 3 of the Distancia-Covid survey (14 December 2020 through 10 January 2021)</li> <li>distancia_covid_individual_contact_estimates_spain_noncores_wave1.csv.gz: Estimates of non-coresident contacts during wave 1 of the Distancia-Covid survey (14 May 2020 through 10 June 2020)</li> <li>distancia_covid_individual_contact_estimates_spain_noncores_wave2.csv.gz: Estimates of non-coresident contacts during wave 2 of the Distancia-Covid survey (24 July 2020 through 31 August 2020)</li> <li>distancia_covid_individual_contact_estimates_spain_noncores_wave3.csv.gz: Estimates of non-coresident contacts during wave 3 of the Distancia-Covid survey (14 December 2020 through 10 January 2021)</li> <li>CITATION.cff: Citation file.</li> </ul> <p> </p>
Distinct brain networks involved in placebo analgesia between individuals with or without prior experience with opioids
<p><strong>ABSTACT</strong></p> <p>Placebo analgesia is defined as a psychobiological phenomenon triggered by the information surrounding an antalgic drug instead of its inherent pharmacological properties. Placebo analgesia is hypothesized to be formed through either verbal suggestions or conditioning. The present study aims at disentangling the neural correlates of expectations effects with or without conditioning through prior experience using the model of placebo analgesia.</p> <p>We will address this question by recruiting two groups of individuals holding comparable verbally-induced expectations regarding morphine analgesia but either (i) with or (ii) without prior experience with opioids. We will then contrast the two groups’ neurocognitive response to acute heat-pain induction following the injection of sham morphine using electroencephalography (EEG). Topographic ERP analyses of the N2 and P2 pain evoked potential components will allow to test the hypothesis that placebo analgesia involves distinct neural networks when induced by expectations with or without prior experience.</p>
Individual tree aboveground biomass of four Pinaceae species in boreal forests in Yakutia in 2018
<p>Samples to estimate aboveground tree biomass for four boreal forest species (<em>Larix gmelinii</em>, <em>Picea obovata</em>, <em>Pinus sylvestris</em>, <em>Pinus sibirica</em>) were collected during fieldwork in Yakutia in 2018 by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany, The Institute for Biological problems of the Cryolithozone, Russian Academy of Sciences, Siberian branch, and The Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia (Kruse et al., 2019). From each of the visited site, three living trees (a small, a medium-sized and the talles tree) per each site were cut down after estimating the quantity of the different types to be sampled, namely branches, needles, cones, making up the tree. Further, to estimate the stem weight, tree discs were taken. The discs were taken at the base of a tree (0 cm, disc A), breast height (130 cm, disc B) and top/close to the top of a tree (260 cm, disc C). If the tree was small with <1.3 m, its stem is included as woody biomass in the branch sample. To estimate each tree's stem biomass, the stem was assumed to have a cone shape. Dead trees were also sampled, if present. All harvested samples were weighed fresh in the field and subsampled. The dry weight of all subsamples was recorded after oven drying (60 °C, 48 h for needle and branch samples, up to one week for tree stem discs). A detailed protocol for total tree and shrub AGB estimation can be found in Shevtsova, et al. (2020).</p> <p><strong>Data format</strong><br> The data consists of one table for each of the four species. The columns (N=13) contain the follwoing information:<br> 1. TreeDataBaseID -> unique Tree Data Base identifier of the individual<br> 2. Site -> Sampling site name<br> 3. SampleID -> Field name given to the individual<br> 4. Species -> Species name<br> 5. Height_cm -> Height of the tree individual in cm<br> 6. Vitality -> Estimate of the vitality state in 6 levels, ++ very good, + good, 0 mediocre, - bad, -- very bad, dead<br> 7. NeedleWeight_g -> Dry weight of needles in g<br> 8. StemWeight_g -> Dry weight of the stem in g<br> 9. BiomassBranchStatus -> 1 if branches are present and included in the biomass estimate or not<br> 10. TotalWeightNonStem_g -> Dry weight of all parts but the stem, which are needles, branches and cones in g<br> 11. DiameterBasal_cm -> Stem diameter at tree stem base (0 cm above ground) in cm<br> 12. DiameterBreast_cm -> Stem diameter at breast height (130 cm above ground) in cm<br> 13. CrownDiameter_cm -> Mean crown diameter in cm</p> <p><strong>Additional information</strong><br> This data is linked to further information about individual trees and their sites as published in: van Geffen, Femke; Schulte, Luise; Geng, Rongwei; Heim, Birgit; Pestryakova, Luidmila A; Herzschuh, Ulrike; Kruse, Stefan (2021): Tree height and crown diameter during fieldwork expeditions that took place in 2018 in Central Yakutia and Chukotka, Siberia. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.932817<br> Information about the expedition in 2018 in: Kruse, Stefan; Bolshiyanov, Dimitry Yu; Grigoriev, Mikhail N; Morgenstern, Anne; Pestryakova, Ludmila A; Tsibizov, Leonid; Udke, Annegret (2019): Russian-German Cooperation: Expeditions to Siberia in 2018. Berichte zur Polar- und Meeresforschung = Reports on Polar and Marine Research, 734, 257 pp, https://doi.org/10.2312/BzPM_0734_2019<br> Aboveground estimation protocol and further data in: Shevtsova, Iuliia; Kruse, Stefan; Herzschuh, Ulrike; Brieger, Frederic; Schulte, Luise; Stuenzi, Simone Maria; Pestryakova, Ludmila A; Zakharov, Evgenii S (2020): Total above-ground biomass of 39 vegetation sites of central Chukotka from 2018. PANGAEA, https://doi.org/10.1594/PANGAEA.923719</p>
Raw data of individuals with Down syndromre, individuals with Williams syndrome, healthy children and adults in a visual learning task, a conditional learning task and a transitive inference task.
<p>Raw data of 17 individuals with Down syndrome (8 girls/women; average age: 17.8 years; range: 7.2-30.8 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p> <p>Raw data of 27 individuals with Williams syndrome (16 girls/women; average age: 23.7; range: 9.4-43.8 at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p> <p>Raw data of 71<strong> </strong>healthy children (31 girls; average age: 6.42 years; range: 2.95-11.64 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p> <p>Raw data of 22 healthy adults (11 femaleswomen; average age: 26.05 years; range: 20.32-29.76 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p>
Statistical analysis and dataset for: Invasive ants fed spinosad collectively recruit to known food faster yet individually abandon food earlier
<p>Linked to the journal article published in bioRxiv (https://doi.org/10.1101/2024.06.20.599949).</p> <p><em><strong>Abstract</strong></em></p> <p>Current management strategies applied to invasive ants rely on slow-acting insecticides which aim to delay the ant’s ability to detect the poison until its effects are noticeable. Despite this, most control efforts are unsuccessful, likely due to bait abandonment and insufficient sustained consumption. Conditioned taste aversion, a learned avoidance of a particular taste, is a crucial survival mechanism which prevents animals from repeatedly ingesting toxic substances. However, whether ants are capable of this delayed association between food taste and subsequent illness remains largely unexplored. Here, we exposed colonies of the highly invasive Argentine ant, <em>Linepithema humile</em>, to a sublethal dose of the slow-acting insecticide spinosad. We combined measurements of individual-level feeding patterns with quantification of collective preferences and foraging dynamics to investigate the potential effects of the toxicant on behaviour. Collectively, ants preferred an odour associated with a previously experienced food, even if this contained spinosad, over a novel one. However, at the individual-level, previous exposure to spinosad resulted in reduced food consumption, as a consequence of earlier food abandonment. Moreover, while control-treated colonies recruited slower to a food source which tasted like a previously experienced one, spinosad-exposed colonies recruited equally fast to both novel and familiar foods. Although it appears that ants are unable to develop a conditioned taste aversion to sublethal doses of spinosad, ingestion of even small amounts of the toxicant strongly influences foraging behaviour. Understanding the subtle effects of slow-acting pesticides on ant cognition and behaviour can ultimately inspire the development of more efficient control methodologies.</p>
IPTIS (individual pomelo tree image sample) datasets
<p>These datasets include 480 clip images from two study sites (i.e., Site A and B) totally. The origional UAV-based images were captured with DJI drones on four different dates, i.e., 3 Debruary 2021, 12 March 2021, 12 April 2021, and 16 January 2022. They were proceeded into four separate datasets according to the date and one in total. They were used for the study on Detecting and Mapping Individual Fruit Trees in Complex Natural Environments via UAV Remote Sensing and Optimized YOLOv5 by Y Xiong, X Zeng, W Lai, J Liao, Y Chen, M Zhu, and K Huang, which was published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 7554 - 7576, 2024(22 March 2024). https://doi.org/10.1109/JSTARS.2024.3379522.<br>They were named IPTIS (individual pomelo tree image sample) datasets for short.</p>
Seeing nanoscale electrocatalytic reactions at individual MoS2 particles under an optical microscope: probing sub-mM oxygen reduction reaction
<p><span>Data in this repository include raw iSCAT optical microscopy movies for the operando monitoring of oxygen reduction reaction at bare ITO and MoS2-coated ITO electrodes in KCl solution in the presence or absence of La<sup>3+</sup> with their respective electrochemical data (voltammograms). </span></p>
Individuals Who Never Used the Internet
<p>Percentage of individuals who never used the internet. Based on the Eurostat dataset Internet use by individuals[tin00028] % of individuals aged 16 to 74 with various approximation and forecasting applied.<br> <br> </p> <p> </p>
Global Carbon Budget 2022, surface ocean fugactiy of CO2 (fCO2) and air-sea CO2 flux of individual Global ocean biogechemical models and surface ocean fCO2-based data-products
<p><strong>Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (data-products).</strong><br> There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs. </p> <p><strong>Note: </strong>These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. <strong>The best estimates of the annual global ocean carbon sink, based on the native grids of data-products and GOBMs and with the adjustments described in the Global Carbon Budget 2022 (https://doi.org/10.5194/essd-14-4811-2022, section C3), are available in the Global Carbon Budget 2022 spreadsheet.</strong></p> <p>The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 13 of the Global Carbon Budget 2022 paper (https://doi.org/10.5194/essd-14-4811-2022), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.17 GtC yr-1, Tropics: 0.16 GtC yr-1, South: 0.32 GtC yr-1, see GCB 2022 paper, section 2.4.1). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because adjustments were applied only for global fluxes.</p> <p><strong>What is in the files?</strong></p> <p>(1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):</p> <p><br> fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: north, tropics, south<br> fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br> sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br> area: Area per pixel, dimensions: latitude, longitude<br> area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south</p> <p>(2) The files for the GOBMs contain the following fields, for simulation A (‘contemporary simulation’, including effects of rising CO2, climate change and variability) and simulation B (‘control simulation’, constant CO2, no climate change and variability). Temporal resolution: monthly</p> <p>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br> sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br> area: Area per pixel, dimensions: latitude, longitude</p> <p><br> (3) One file ‘GCB-2022_OceanModel_RegionalBreakdown_1959-2021.nc’ with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Temporal resolution: annual.</p> <p><br> <strong>Fair data use statement:</strong><br> The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.<br> <strong>Citation:</strong> Please cite the Global Carbon Budget 2022 (Friedlingstein et al., 2022, ESSD, https://doi.org/10.5194/essd-14-4811-2022) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2022 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720).<br> <strong>Acknowledgement:</strong> Please add the following text in the acknowledgement of your paper: “We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output.”<br> <strong>Co-authorship: </strong>An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.</p> <p><br> Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional output</strong> from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudget.org/</p> <p> </p>
International benchmark for ALS individual tree segmentation
<p>This upload aims to provide an international benchmark dataset for airborne LiDAR-based individual tree segmentation algorithm comparison and development.</p> <p> </p> <p> </p>
MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall
<p><strong>Dataset overview</strong></p> <p>This dataset provides data and images of snowflakes in free fall collected with a <a href="https://amt.copernicus.org/articles/5/2625/2012/">Multi-Angle Snowflake Camera (MASC)</a> The dataset includes, for each recorded snowflakes:</p> <ol> <li>A triplet of gray-scale images corresponding to the three cameras of the MASC</li> <li>A large quantity of geometrical, textural descriptors and the pre-compiled output of published retrieval algorithms as well as basic environmental information at the location and time of each measurement.</li> </ol> <p>The pre-computed descriptors and retrievals are available either individually for each camera view or, some of them, available as descriptors of the triplet as a whole. A non exhaustive list of precomputed quantities includes for example:</p> <ul> <li>Textural and geometrical descriptors as in <a href="https://amt.copernicus.org/articles/10/1335/2017/"><em>Praz et al 2017</em></a></li> <li>Hydrometeor classification, riming degree estimation, melting identification, as in <a href="https://amt.copernicus.org/articles/10/1335/2017/"><em>Praz et al 2017</em></a></li> <li>Blowing snow identification, as in <a href="https://tc.copernicus.org/articles/14/367/2020/"><em>Schaer et al 2020 </em></a></li> <li>Mass, volume, gyration estimation<em>, as in <a href="https://amt.copernicus.org/preprints/amt-2021-176/">Leinonen et al 2021</a></em></li> </ul> <p><strong>Data format and structure</strong></p> <p>The dataset is divided into four <em>.parquet</em> file (for scalar descriptors) and a <em>Zarr</em> database (for the images). A detailed description of the data content and of the data records is available <a href="https://pymascdb.readthedocs.io/en/latest/data.html#data">here</a>.</p> <p><strong>Supporting code</strong></p> <p>A python-based API is available to manipulate, display and organize the data of our dataset. It can be found on <a href="https://github.com/ltelab/pymascdb">GitHub</a>. See also the code documentation on <a href="https://pymascdb.readthedocs.io/en/latest/index.html">ReadTheDocs</a>.</p> <p><strong>Download notes</strong></p> <ul> <li>All files available here for download should be stored in the same folder, if the python-based API is used</li> <li><em>MASCdb.zarr.zip</em> must be unzipped after download</li> </ul> <p><strong>Field campaigns</strong></p> <p>A list of campaigns included in the dataset, with a minimal description is given in the following table</p> <table> <tbody> <tr> <td><strong>Campaign_name</strong></td> <td><strong>Information</strong></td> <td> <p><strong>Shielded / Not shielded</strong></p> <p><em>DFIR = Double Fence Intercomparison Reference</em></p> </td> </tr> <tr> <td> <p><em>APRES3-2016 & APRES3-2017</em></p> </td> <td>Installed in Antarctica in the context of the APRES3 project. See for example <a href="https://essd.copernicus.org/articles/10/1605/2018/essd-10-1605-2018.html">Genthon et al, 2018</a> or <a href="https://tc.copernicus.org/articles/11/1797/2017/">Grazioli et al 2017</a></td> <td>Not shielded</td> </tr> <tr> <td><em>Davos-2015</em></td> <td>Installed in the Swiss Alps within the context of <a href="https://public.wmo.int/en/resources/meteoworld/spice-%E2%80%93-improving-snowfall-measurements">SPICE</a> (Solid Precipitation InterComparison Experiment)</td> <td>Shielded (DFIR)</td> </tr> <tr> <td><em>Davos-2019</em></td> <td>Installed in the Swiss Alps within the context of <a href="https://www.envidat.ch/group/about/raclets-field-campaign">RACLETS</a> (<em>Role of Aerosols and CLouds Enhanced by Topography on Snow</em>)</td> <td>Not shielded</td> </tr> <tr> <td><em>ICEGENESIS-2021</em></td> <td>Installed in the Swiss Jura in a MeteoSwiss ground measurement site, within the context of ICE-GENESIS. See for example <a href="https://doi.org/10.1175/BAMS-D-21-0184.1">Billault-Roux et al, 2023</a></td> <td>Not shielded</td> </tr> <tr> <td><em>ICEPOP-2018</em></td> <td>Installed in Korea, in the context of ICEPOP. See for example <a href="https://doi.org/10.5194/essd-13-417-2021">Gehring et al 2021</a>.</td> <td>Shielded (DFIR)</td> </tr> <tr> <td><em>Jura-2019 & Jura-2023</em></td> <td>Installed in the Swiss Jura within a MeteoSwiss measurement site</td> <td>Not shielded</td> </tr> <tr> <td><em>Norway-2016</em></td> <td>Installed in Norway during the High-Latitude Measurement of Snowfall (HiLaMS). See for example <a href="https://doi.org/10.1175/BAMS-D-21-0007.1">Cooper et al, 2022</a>.</td> <td>Not shielded</td> </tr> <tr> <td><em>PLATO-2019</em></td> <td>Installed in the "Davis" Antarctic base during the <a href="https://www.osti.gov/biblio/1524773">PLATO</a> field campaign</td> <td>Not shielded</td> </tr> <tr> <td><em>POPE-2020</em></td> <td>Installed in the "Princess Elizabeth Antarctica" base during the POPE campaign. See for example <a href="https://essd.copernicus.org/articles/15/1115/2023/essd-15-1115-2023.html">Ferrone et al, 2023</a>.</td> <td>Not shielded</td> </tr> <tr> <td><em>Remoray-2022</em></td> <td>Installed in the French Jura.</td> <td>Not shielded</td> </tr> <tr> <td><em>Valais-2016</em></td> <td>Installed in the Swiss Alps in a ski resort.</td> <td>Not shielded</td> </tr> <tr> <td>ISLAS-2022</td> <td>Installed in Norway during the <a href="https://www.uib.no/en/rg/meten/150202/islas2022-field-campaign">ISLAS campaign</a></td> <td>Not shielded</td> </tr> <tr> <td>Norway-2023</td> <td>Installed in Norway during the MC2-ICEPACKS campaign</td> <td>Not shielded</td> </tr> </tbody> </table> <p> </p> <p><strong>Version</strong></p> <p>1.1 - Two new campaigns ("ISLAS-2022", "Norway-2023") added.</p> <p>1.0 - Two new campaigns ("Jura-2023", "Norway-2016") added. Added references and list of campaigns.</p> <p>0.3 - a new campaign is added to the dataset ("Remoray-2022")</p> <p>0.2 - rename of variables. Variable precision (digits) standardized</p> <p>0.1 - first upload</p>
CBS03 Grasshopper sparrow surveys: densities, reproductive index, and locations of marked individuals on Konza Prairie
Data on the location, identity, and reproductive index (Vickery et al. 1992) of Grasshopper Sparrows prior to 2017, and after that, additionally many Dickcissels, Eastern Meadowlarks, Brown-headed Cowbirds and other songbirds within 10-ha plots on multiple watersheds units on Konza and on two adjoining units on the Rannells Preserve. Each plot was surveyed every ~7-10 days. These surveys documented individual sparrow, Dickcissel, and Eastern Meadowlark locations, and are used to calculate dispersal distances and territory densities and movements. Missing values in character fields denoted by NA, and in numeric fields, either -999 or -9.
Ferns surveys of individuals of terrestrial ferns in Canopy Trimming Experiment (CTE) plots document changes in species richness and abundance over time in response to canopy opening and/or debris deposition
Whole plot surveys of Canopy Triming Experiment (CTE) plots were done to detect changes in the number of terrestrial fern species and individuals in response to canopy opening and debris deposition. Surveys were conducted annually prior to and after treatments. A count of all terrestrial ferns, identified to species on the CTE plots was recorded for each subplot in January during CTE1 (2002-2010) and in the fall during CTE2 (2014-present). The surveys document losses of individuals of shade tolerant fern species and the appearance of open canopy ferns such as the tree fern Cyathea arborea. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
The human Voice Areas: spatial organisation and inter-individual variability in temporal and extra-temporal cortices
Open the record for dataset details and reuse information.
Internet use: participating in social networks [percentage of individuals] processed Eurostat data [CEEMID indicator]
<p>The indicator '<strong>Internet use: participating in social networks (creating user profile, posting messages or other contributions to facebook, twitter, etc.) [percentage of individuals]</strong>' from the Eurostat statistical product <em>Individuals who used the internet, frequency of use and activities.</em></p> <p>- NUTS2013 regional codes are recoded to NUTS2016<br> - missing data is handled with last observation carry forward, next observation carry back, linear interpolation<br> -NUTS2 areas are imputed when only NUTS1 level data is available. <br> <br> The original dataset is available here:<br> <a href="https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_r_iuse_i&lang=en">https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_r_iuse_i&lang=en</a></p> <p>More about CEEMID: <a href="http://ceemid.eu">www.ceemid.eu</a><br> Get in touch: <a href="http://danielantal.eu/#contact">danielantal.eu/#contact</a></p>
The Hearpiece database of individual transfer functions of an openly available in-the-ear earpiece for hearing device research
<p>We present a database of acoustic transfer functions of the Hearpiece, an openly available multi-microphone multi-driver in-the-ear earpiece for hearing device research. The database includes HRTFs for 87 incidence directions as well as responses of the drivers, all measured at the four microphones of the Hearpiece as well as the eardrum in the occluded and open ear. The transfer functions were measured in both ears of 25 human subjects and a KEMAR with anthropometric ears for five reinsertions of the device. We describe the measurements of the database and analyse derived acoustic parameters of the device. All regarded transfer functions are subject to differences between subjects as well as variations due to reinsertion into the same ear. Also, the results show that KEMAR measurements represent a median human ear well for all assessed transfer functions. The database is a rich basis for development, evaluation and robustness analysis of multiple hearing device algorithms and applications.</p>
Data for investigating structural complexity of individual Scots pine trees
<p>Tree functional traits together with processes such as forest regeneration, growth, and mortality affect forest and tree structure. Forest management inherently impacts these processes. Moreover, forest structure, biodiversity, resilience, and carbon uptake can be sustained and enhanced with forest management activities. To assess structural complexity of individual trees, comprehensive and quantitative measures are needed, and they are often lacking for current forest management practices. Fractal analysis and a single scale, independent metric called box dimension offer means for assessing structural complexity of individual trees. Terrestrial laser scanning (TLS) point clouds provide three-dimensional (3D) information on trees that can be utilized in generating the box dimension metric. This data set includes information needed for generating the box dimension from 741 individual Scots pine (<em>Pinus sylvestris</em> L.) trees from 9 sample plots with different thinning treatments located in southern boreal forests. The thinning treatments include two intensities of thinning and control treatment (i.e., no thinning treatment since the establishment). The data set can be used in characterizing structural complexity of individual Scots pine trees of various size as well as assessing effects of various thinning treatments on it.</p> <p>Please see the data descriptor for more information on the data structure and its possibilities.</p> <p>Please keep the designated corresponding author informed of any plans to use the data. Consultation or collaboration with the original investigators is strongly encouraged. Publications and data products that make use of the data must include proper acknowledgement.</p>
Supplementary Material: "A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data"
<p><strong>Supplementary Material</strong></p> <p>This material regards the paper entitled "<em>A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data</em>".</p> <p>The Readme.txt file explains all the contents of the data package, which consists of the data supporting the paper and the MATLAB script for the Individual Tree Detection and Measurement (ITDM).</p> <p>Please cite the related article if using the data or the script.</p> <p>Latella, M., Sola, F., & Camporeale, C. (2021). A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data. Remote Sensing, 13(2), 322.</p>
Terrestrial laser scanning - RIEGL VZ-1000, individual tree point clouds and cylinder models, Belgian hedgerows and tree rows
<p>Terrestrial laser scans were acquired for 69 trees (<em>Quercus robur</em>: 39 trees; <em>Alnus glutinosa</em>: 19 trees; <em>Betula pendula: </em>11 trees) in hedgerows and tree rows in agricultural lands in Flanders, Belgium. We used a RIEGL VZ-1000 terrestrial laser scanner (RIEGL Laser Measurement Systems GmbH, Austria) with a beam divergence of 0.35 mrad operating in the infrared (wavelength 1550 nm) with a range up to 1000 m. We scanned leaf-off and all recorded variables are valid for overbark measurements. Individual trees were manually extracted from the co-registered point cloud in RiSCAN PRO software (provided by RIEGL). To the extracted trees, quantitative structure models (QSM) were fitted. We used the QSMs to derive branch length (m), total wood volume (m³) and merchantable wood volume (m³, using only cylinders with diameter > 7 cm). From the point clouds, we extracted the tree structural features such as crown projection (m²), maximum crown diameter (m) and tree height (m). Biomass expansion factors (BEF) were calculated by dividing total tree volume to merchantable tree volume. We expressed the age dependency of the BEF values via non-linear regression models. See Van Den Berge et al. (2021) for further information (DOI: 10.1007/s12155-021-10250-y).</p>
Data from: Advancement in long-distance bird migration through individual plasticity in departure
<p>Research summary: Globally, bird migration is occurring earlier, consistent with climate-related changes in breeding resources. Although often attributed to phenotypic plasticity, there is no clear demonstration of long-term population advancement in avian migration through individual plasticity. Using direct observations of bar-tailed godwits (<em>Limosa lapponica</em>) departing New Zealand on a 16,000-km journey to Alaska, we show that migration advanced by six days during 2008–2020, and that within-individual advancement was sufficient to explain this population-level change. However, in individuals tracked for the entire migration, earlier departure did not lead to earlier arrival or breeding in Alaska, due to prolonged stopovers in Asia. Moreover, changes in breeding-site phenology varied across Alaska, but were not reflected in within-population differences in advancement of migratory departure. We demonstrate that plastic responses can drive population-level changes in timing of long-distance migration, but also that behavioral and environmental constraints <em>en route</em> may yet limit adaptive responses to global change.</p> <p>The collection of long-term departure data was supported by Chris & Neville Hopkins, David & Lucile Packard Foundation, Dobberke Foundation for Comparative Psychology, Manawatu Estuary Trust, Marsden Fund (Royal Society of New Zealand), Massey University Doctoral Scholarship, New Zealand Department of Conservation, Ornithological Society of New Zealand, Pacific Shorebird Migration Project, Pūkorokoro Miranda Naturalist’s Trust, and Royal Netherlands Academy of Arts & Sciences.</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.