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.
9,113
datasets available to search
ShareScore release 0.9.0
Dataset results
9,113 results for “Sampling”
Biogenic silicate concentration in sea water samples, collected from the CTD in the Southern Ocean during the austral summer of 2016/2017, on board the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>Biogenic Silicate (Bsi) concentration (µmol/L) in seawater data. Water samples were collected from CTD rosette deployments, filtered on board and then analysed by flow injection following appropriate digestion.</p> <p>This data supports chemical and biological oceanography studies conducted during the Antarctic Circumnavigation Expedition.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_biogenic_silicate_concentration_in_seawater_ctd.csv, data file, comma-separated values</li> <li>ace_biogenic_silicate_concentration_in_seawater_ctd_visual_summary.png, metadata, portable network graphics</li> <li>data_file_header.txt, metadata, text format</li> <li>README.md, metadata, text format</li> </ul> <p>All missing values where no data point exists from lack of sample, have been set to NaN.</p> <p><strong>Dataset license</strong></p> <p>This biogenic silica concentration dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Table S27: Target and identified unknown organic micropollutants detected in surface water samples taken during heavy rain events
<p>In the following table, peak intensities of detected organic micropollutants in water samples are displayed.</p> <p>This data table is part of the appendix of Chapter 4 of the PhD thesis “Novel approaches to identify drivers of chemical stress in small rivers” by Liza-Marie Beckers prepared at RWTH Aachen University and at the Helmholtz Centre for Environmental Research-UFZ. In Chapter 4, precipitation-related pollutant patterns and indicator compounds during heavy rain events were identified in the Holtemme River by nontarget screening and cluster analysis. The table contains peak heights of organic micropollutants detected in water samples taken during heavy rain events in the Holtemme River (Saxony – Anhalt, Germany). The table is structured into the following columns: Compound name, use class of compound (e.g., pharmaceutical or pesticide), distinction between target or identified unknown compounds, mass-to-charge ratio (m/z), retention time (RT), assignment to a pattern identified by cluster analysis (i.e., “Base” or “Quick”), the probability of belonging to the assigned pattern as number between 0 and 1 as well as the peak height of the compound in each sample. The samples are indicated by "B" for "bottle" and a number from 1-16. The use class “NA” indicates that now major use class for this compound could be identified.</p> <p>The sampling was triggered by combined sewer overflow at a wastewater treatment plant upstream of the sampling point. Samples were taken by an automated sampler in 30-min composite samples for 8 hours resulting in 16 samples per rain event. In total, 6 heavy rain events from May to September 2016 were sampled during this study. The table is divided into 6 subtables (i.e., Table S27 A-F). Each subtable displays compounds and their peak heights detected in samples from one heavy rain event. The different rain events are abbreviated by the sampling date:</p> <p>Table S27A displays results from the rain event samples May 29<sup>th</sup> 2016 : E2905</p> <p>Table S27B displays results from the rain event samples June 01<sup>st</sup> 2016 : E0106</p> <p>Table S27C displays results from the rain event samples June 24<sup>th</sup> 2016 : E1306</p> <p>Table S27D displays results from the rain event samples June 13<sup>th</sup> 2016 : E2406</p> <p>Table S27E displays results from the rain event samples July 13<sup>th</sup> 2016 : E1307</p> <p>Table S27F displays results from the rain event samples September 17<sup>th</sup> 2016 : E1709</p> <p>Chemical analysis of the water samples was performed by liquid chromatography (UltiMate 3000 LC system (Thermo Scientific)) coupled to high resolution mass spectrometry (Q Exactive Plus, Thermo Scientific) with a heated electrospray ionization (HESI) source. Nontarget screening was performed as it allows for a comprehensive characterization of the chemical exposure during heavy rain events. However, only annotated target compounds and unknown compounds identified by structure elucidation are presented in the table. Details on data evaluation methods are described in Chapter 4 of the PhD thesis.</p> <p>Beckers, L.M. (2019): Novel approaches to identify drivers of chemical stress in small rivers. RWTH Aachen University, Aachen.</p>
Chemical composition, soil water content and 16S rRNA and ITS gene copy numbers of soil aggregates and bulk soil samples
<p>This repository contains all data to reproduce the analyses presented in "Distinct microbial communities are linked to organic matter properties in millimetre-sized soil aggregates", Simon et al 2024, <em>The ISME Journal </em>(DOI: 10.1093/ismejo/wrae156).</p>
Microplastics' Characteristics for Water Samples
<p>The characteristics of microplastics by the abundance, color, and size are shown for water samples collected in plastic bottles by VLPF, PONIKVE, THAMES 21, UNIPER, and DOW COMPANY Partners, on June 2022, as part of In-No-Plastic Project. Three replicates were collected at each sampling site. Water samples were filtrated by stacked stainless steel sieves with 4 mm and 32 μm mesh sizes. After the organic matter was destroyed, the microplastics were observed by using an optical microscope.</p>
Values, beliefs, norms, and circular citizenship behaviours in a student sample
<p>Data set of 229 students from an online survey, run between March 7th 2024 and April 2nd 2024.</p> <p><strong>Gender</strong>: 43 men (18.8%), 181 women (79.0%), 3 non-binary persons (1.2%), 2 participants (0.9%) preferred not to indicate their gender.</p> <p><strong>Age</strong>: The age ranged from 17 to 29 years (M = 19.7, SD = 1.83).</p> <p><strong>Variables</strong>: Biospheric, altruistic, hedonic, and egoistic values, problem awareness, ascription of causal responsibility, self-efficacy, outcome efficacy, personal norms, circular citizenship behaviours</p> <p><strong>Data analysis </strong>by creating scales for the variables and running regressions/GLM.</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>
Data for the publication: Recombinant silk protein condensates show widely different properties depending on the sample background
<p>This entry includes raw data for the publication "Recombinant silk protein condensates show widely different properties depending on the sample background". The original publication was published in: Journal of Materials Chemistry B, DOI: 10.1039/d4tb01422g</p> <p>The folder "Videos_Micropipette_Aspiration_Zenodo.zip" contains 9 TIF files, labeled Number1 - Number9. The numbering corresponds to the numbering of IMAC condensates studied with micropipette aspiration in the publication. Each TIF file is an image stack from a time series.</p> <p>The folders "Videos_IMAC_silk_with_BG_lysate_coalescence.zip", "Videos_HT_silk_coalescence.zip", and "Videos_IMAC_silk_coalescence.zip" all contain subfolders labeled with the purification method, the framerate of the videos and then consecutive numbering. Each of these folders contains the frames of the video as single TIF files.</p> <p>Please find more information in the read_me file uploaded.</p>
LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe – files
<p><strong>Version 1.0 - This version is the final revised one.</strong></p> <p>This is the LamaH-CE dataset accompanying the paper: Klingler et al., LamaH-CE | LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, published at Earth System Science Data (ESSD), 2021 (<a href="https://doi.org/10.5194/essd-13-4529-2021">https://doi.org/10.5194/essd-13-4529-2021</a>).</p> <p>LamaH-CE contains a collection of runoff and meteorological time series as well as various (catchment) attributes for 859 gauged basins. The hydrometeorological time series are provided with daily and hourly time resolution including quality flags. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses with high temporal resolution.<br> LamaH is in its basics quite sililar to the well-known CAMELS datasets for the contiguous United States (<a href="https://doi.org/10.5194/hess-21-5293-2017">https://doi.org/10.5194/hess-21-5293-2017</a>), Chile (<a href="https://doi.org/10.5194/hess-22-5817-2018">https://doi.org/10.5194/hess-22-5817-2018</a>), Brazil (<a href="https://doi.org/10.5194/essd-12-2075-2020">https://doi.org/10.5194/essd-12-2075-2020</a>), Great Britain (<a href="https://doi.org/10.5194/essd-12-2459-2020">https://doi.org/10.5194/essd-12-2459-2020</a>) and Australia (<a href="https://doi.org/10.5194/essd-13-3847-2021">https://doi.org/10.5194/essd-13-3847-2021</a>), but new features like additional basin delineations (intermediate catchments) and attributes allow to consider the hydrological network and river topology in further applications.</p> <p>We provide two different files to download: 1) Hydrometeorological time series with daily and hourly resolution, which requires decompressed about 70 GB of free disk space. 2) Hydrometeorological time series only with daily resolution, which requires 5 GB. Beyond the temporal resolution of the time series, there are no differences.</p> <p><strong>Note: </strong>It is recommended to read the supplementary info file before using the dataset. For example, it clarifies the time conventions and that <strong>NAs</strong> are indicated by the number<strong> -999</strong> in the <strong>runoff time series</strong>.</p> <p><strong>Disclaimer:</strong> We have created LamaH with care and checked the outputs for plausibility. By downloading the dataset, you agree that we nor the provider of the used source datasets (e.g. runoff time series) cannot be liable for the data provided. The runoff time series of the German federal states Bavaria and Baden-Württemberg are retrospective checked and updated by the hydrographic services. Therefore, it might be appropriate to obtain more up-to-date runoff data from Bavaria (<a href="https://www.gkd.bayern.de/en/rivers/discharge/tables">https://www.gkd.bayern.de/en/rivers/discharge/tables</a>) and Baden-Württemberg (<a href="https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer">https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer</a>). Runoff data from the Czech Republic may not be used to set up operational warning systems (<a href="https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf">https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf</a>).</p> <p><strong>License: </strong>This work is licensed with CC BY-SA 4.0 (<a href="https://creativecommons.org/licenses/by-sa/4.0/">https://creativecommons.org/licenses/by-sa/4.0/</a>). This means that you may freely use and modify the data (even for commercial purposes). But you have to give appropriate credit (associated ESSD paper, version of dataset and all sources which are declared in the folder "Info"), indicate if and what changes were made and distribute your work under the same public license as the original.</p> <p><strong>Additional references: </strong>We ask kindly for compliance in citing the following references when using LamaH, as an agreement to cite was usually a condition of sharing the data: BAFU (2020), CHMI (2020), GKD (2020), HZB (2020), LUBW (2020), BMLFUW (2013), Broxton et al. (2014), CORINE (2012), EEA (2019), ESDB (2004), Farr et al. (2007), Friedl and Sulla-Menashe (2019), Gleeson et al. (2014), HAO (2007), Hartmann and Moosdorf (2012), Hiederer (2013a, b), Linke et al. (2019), Muñoz Sabater et al. (2021), Muñoz Sabater (2019a), Myneni et al. (2015), Pelletier et al. (2016), Toth et al. (2017), Trabucco and Zomer (2019), and Vermote (2015). These references are listed in detail in the accompanying <a href="https://doi.org/10.5194/essd-13-4529-2021">paper</a>.</p> <p><strong>Supplements: </strong>We have created additional files after publication (therefore non peer-reviewed):<br> 1) Shapefiles for reservoirs (points) and cross-basin water transfers (lines) including several attributes as well as tables with information about the accumulated storage volume and effective catchment area (considerung artificial in- and outflows) for every runoff gauge.<br> 2) Water quality data (e.g. dissolved oxygen, water temperature, conductivity, NO3-N), which are suitable to the gauges. The data for water quality may not be used for commercial purposes.<br> If you are interessted, just send us an email with your name, affiliation and the intended purpose for the requested files to the address listed below. If you find any errors in the dataset, feel free to send us an email to: christoph.klingler@boku.ac.at</p>
Pitfalls in Sample Preparation of Metalloproteins for Low-Temperature EPR: The Example of Alkaline Myoglobin
<p><strong>Description of the dataset: </strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements (EPR and UV-vis), computer simulations and data analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DTA</strong>, <strong>m</strong>, <strong>mat</strong>, <strong>ods</strong>,<strong> tif</strong></li> <li>Information on <strong>origin of the data</strong>: <ul> <li>EPR spectroscopic measurements have filename extensions <strong>DSC</strong> and <strong>DTA</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong></li> <li>UV-vis spectroscopic measurements have filename extensions <strong>ods</strong></li> <li>Processed data ready for simulation/figure preparation have filename extension <strong>mat </strong>(both for EPR and UV-vis)</li> <li>High-quality figures published in main text and supplementary are provided as <strong>tif</strong> files</li> </ul> </li> <li>CW-EPR measurements were generated with a Bruker ELEXSYS E580 X-band spectrometer equipped either with an Oxford CF935 continuous flow cryostat and a Bruker ER4118 SPT-N1 resonator or with an Oxford ESR 900 continuous-flow cryostat and a Bruker ER 4122 SHQ resonator.</li> <li>Pulse EPR experiments were performed with an Oxford CF935 continuous flow cryostat and a Bruker ER4118 SPT-N1 resonator.</li> <li>Simulations of EPR spectra and fitting were performed with the Easyspin software (v. 6.0.0-dev.26) implemented in Matlab (MathWorks, R2020b)</li> <li><strong>The dataset</strong>Files with extension <strong>m</strong> normally recall files with extension <strong>mat</strong> which should be stored in the same working folder <ul> <li>Files in <strong>PARACAT_WP3_20210929_01_CW</strong> folder include subfolders organised by topic: <ul> <li>“Alternative cryoprotectants” contains: ready-to-plot / ready-to-simulate data in <strong>mat</strong> format; simulation scripts in <strong>m</strong> format.</li> <li>“Glycerol effects - different buffers” contains: ready-to-plot / ready-to-simulate data in <strong>mat</strong> format; simulation scripts in <strong>m</strong> format.</li> </ul> </li> <li>Files in <strong>PARACAT_WP3_20210929_02_PULSE</strong> folder include pulse EPR spectroscopic measurements; original data are in <strong>DSC</strong> and <strong>DTA</strong> formats; processed data and fitting are in <strong>m</strong> format. Files in <strong>m</strong> format recall original data files, therefore they should be stored in the same working folder.</li> <li>Files in <strong>PARACAT_WP3_20210929_03_UV-VIS</strong> folder include UV-vis spectroscopic measurements; original and basic processed data are in <strong>ods</strong> format; ready-to-plot data are in <strong>mat</strong> format; scripts for figure preparation are in <strong>m</strong> format.</li> <li>Files in <strong>PARACAT_WP3_20210929_04_FIGURES</strong> folder include high-quality figures published in main text and supplementary, provided as <strong>tif</strong> files</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Information on</strong>: <ul> <li><strong>Specialized abbreviations:</strong> <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>CW</strong> – Continuous Wave EPR, <strong><em>T<sub>m</sub></em></strong> or <strong><em>T<sub>2</sub></em></strong>– phase memory time, <strong>CAPS</strong> - N-cyclohexyl-3-aminopropanesulfonic acid, <strong>CHES</strong> - N-Cyclohexyl-2-aminoethanesulfonic acid</li> <li><strong>Definitions of variables:</strong> magnetic field (<strong>mT</strong> - milliTesla), pH (pH units), UV-vis absorbance intensity (<strong>A.U.</strong> – arbitrary units), EPR intensity (<strong>A.U.</strong> – arbitrary units), Hahn Echo Intensity (<strong>A.U.</strong> – arbitrary units), <em>g</em>-values (adimensional)</li> <li><strong>Units of measurements:</strong> <ul> <li>Concentration: <strong>mM</strong> (millimolar), <strong>µM</strong> (micromolar), <strong>% v/v</strong> (percentage volume/volume)</li> <li>Volume: <strong>mL</strong> (milliliters), <strong>µL</strong> (microliters)</li> <li>Wavelength: <strong>nm</strong> (nanometers)</li> <li>Temperature: <strong>°C</strong> (Celsius degrees), <strong>K</strong> (Kelvin degrees)</li> <li>Time: <strong>ns</strong> (nanoseconds)</li> <li>Frequency: <strong>GHz</strong> (gigahertz)</li> <li>Power: <strong>mW</strong> (milliwatt)</li> </ul> </li> </ul> </li> </ul>
S101 | MTMDUST | List of chemicals characterised in indoor dust samples
<p>This is the collection associated with list S101 MTMDUST List of chemicals characterised in indoor dust samples on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>A list of 300 chemicals characterised with identification confidence level of ≥ 3 found in retrospective analysis of 30 dust samples from 4 different indoor settings (offices, households, preschools and various occupational settings) described in Dubocq et al. (2022) DOI: <a href="https://www.oaepublish.com/jeea/article/view/5192">10.20517/jeea.2022.23</a> which can help to better estimate the exposure risks of organic contaminants to humans in the indoor environment. The categories of the main detected chemical groups were plant natural products (n = 57), personal care products (n = 44), pharmaceuticals (n = 44), food additives (n = 43), plasticisers (n = 43), flame retardants (n = 43), colourants (n = 42) and pesticides (n = 31).</p> <p>Additional notes: For entries existing in multiple isomeric forms, an additional row has been added (ID347-352) with corresponding identifiers while the metadata for each isomer exist as per original entries<br> </p>
Dataset generated to evaluate in situ sampling strategies to reconstruct fine-scale ocean currents in the context of SWOT satellite mission (H2020 EuroSea project)
<p><strong>Dataset generated in Subtask 2.3.1 of the H2020 EuroSea project.</strong></p> <ul> <li> <p><em>H2020 EuroSea project:</em><br> The H2020 EuroSea project aims at improving and integrating the European Ocean Observing and Forecasting System (see official website: <a href="https://eurosea.eu/">https://eurosea.eu/</a>). It has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626).</p> </li> <li> <p><em>Task 2.3:</em><br> Task 2.3 has the objective to improve the design of multi-platform experiments aimed to validate the Surface Water and Ocean Topography (SWOT) satellite observations with the goal to optimize the utility of these observing platforms. Observing System Simulation Experiments (OSSEs) have been conducted to evaluate different configurations of the in situ observing system, including rosette and underway CTD, gliders, conventional satellite nadir altimetry and velocities from drifters. High-resolution models have been used to simulate the observations and to represent the “ocean truth”. Several methods of reconstruction have been tested: spatio-temporal optimal interpolation, machine-learning techniques, model data assimilation and the MIOST tool. The planned OSSEs are detailed in this public report <a href="https://doi.org/10.3289/eurosea_d2.1">Barceló-Llull et al. (2020)</a> and the complete analysis is available here <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>. Contributors to Task 2.3 are CSIC (Spain), CLS (France), SOCIB (Spain), IMT-Atlantique (France) and Ocean-Next (France).</p> </li> <li> <p><em>Subtask 2.3.1:</em><br> Subtask 2.3.1 aims to evaluate different in situ sampling strategies to reconstruct fine-scale ocean currents (~20 km) in the context of SWOT. An advanced version of the classic optimal interpolation used in field experiments, which considers the spatial and temporal variability of the observations, has been applied to reconstruct different configurations with the objective to evaluate the best sampling strategy to validate SWOT.</p> </li> <li> <p><em>Where?</em><br> The analysis focuses on two regions of interest: (i) the western Mediterranean Sea and (ii) the Subpolar North West Atlantic. In the western Mediterranean Sea, the target area is located within a swath of SWOT, while in the North West Atlantic the region of study includes a crossover of SWOT during the fast-sampling phase.</p> </li> </ul> <p><strong>Report with the full analysis</strong></p> <p>The complete analysis can be found in this report: <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>.</p> <p><strong>Codes for the analysis</strong></p> <p>The codes generated to develop Subtask 2.3.1 can be found on GitHub: <a href="https://github.com/bbarcelollull/EuroSea_subTask_2.3.1">https://github.com/bbarcelollull/EuroSea_subTask_2.3.1</a></p> <p><strong>The dataset</strong></p> <p>The dataset includes:</p> <p>1) Model outputs used to simulate the observations in different configurations in both regions of study. The folder "2D_model_outputs" contains 2D data used to simulate SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42). The folder "3D_model_outputs" contains 3D model outputs used to simulate observations of temperature and salinity. Note that eNATL60 outputs have been interpolated onto a new regular grid. </p> <p>2) Simulated configurations (or sampling strategies) in each region (PKL file format).</p> <p>3) Observations simulated in each configuration in both regions of study. The observations simulated are temperature and salinity. ADCP horizontal velocities are also simulated, however for eNATL60 they will be corrected in the future to account for the rotated original axes. File format: region_configuration_period_model.nc. The folder "SSH" includes the simulated SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42).</p> <p>4) Reconstructed fields with the spatio-temporal optimal interpolation. File format: region_configuration_period_model_stOI_Lx_Lt_cd_YYYYMMDDhhmm_var.nc (stOI = spatio-temporal optimal interpolation, Lx = spatial correlation scale, Lt = temporal correlation scale, cd = map on the central date of the sampling, YYYYMMDDhhmm = date and time of the map, var = variable interpolated (temperature and salinity) or the derived variables (dynamic height, geostrophic velocities and the Rossby number)).</p> <p>5) Compared fields (ocean truth from model outputs vs. reconstructed fields) for each region and model (PKL file format).</p> <p> </p>
MCMC samples of the posterior distribution from the paper "TESS spots a mini-neptune interior to a hot saturn in the TOI-2000 system"
<p>This dataset contains the Hamiltonian Monte Carlo samples of the posterior distribution of the planetary and stellar parameters from the paper "TESS Spots a Mini-Neptune Interior to a Hot Saturn in the TOI-2000 System". The file format, NetCDF, is based on HDF5, and is meant to be read by the Python package <a href="https://python.arviz.org/en/latest/">ArviZ</a>.</p> <p>Hot jupiters (<em>P</em> < 10 d, <em>M</em> > 60 M<sub>⊕</sub>) are almost always found alone around their stars, but four out of hundreds known have inner companion planets. These rare companions allow us to constrain the hot jupiter's formation history by ruling out high-eccentricity tidal migration. Less is known about inner companions to hot Saturn-mass planets. We report here the discovery of the TOI-2000 system, which features a hot Saturn-mass planet with a smaller inner companion. The mini-neptune TOI-2000 b (2.70 ± 0.15 R<sub>⊕</sub>, 11.0 ± 2.4 M<sub>⊕</sub>) is in a 3.10-day orbit, and the hot saturn TOI-2000 c (<span class="math-tex">\(8.14^{+0.31}_{-0.30}\)</span> R<sub>⊕</sub>, <span class="math-tex">\(81.7^{+4.7}_{-4.6}\)</span> M<sub>⊕</sub>) is in a 9.13-day orbit. Both planets transit their host star TOI-2000 (TIC 371188886, <em>V</em> = 10.98, <em>TESS</em> magnitude = 10.36), a metal-rich ([Fe/H] = <span class="math-tex">\(0.439^{+0.041}_{-0.043}\)</span>) G dwarf 174 pc away. <em>TESS</em> observed the two planets in sectors 9–11 and 36–38, and we followed up with ground-based photometry, spectroscopy, and speckle imaging. Radial velocities from HARPS allowed us to confirm both planets by direct mass measurement. In addition, we demonstrate constraining planetary and stellar parameters with MIST stellar evolutionary tracks through Hamiltonian Monte Carlo under the PyMC framework, achieving higher sampling efficiency and shorter run time compared to traditional Markov chain Monte Carlo. Having the brightest host star in the <em>V</em> band among similar systems, TOI-2000 b and c are superb candidates for atmospheric characterization by the JWST, which can potentially distinguish whether they formed together or TOI-2000 c swept along material during migration to form TOI-2000 b.</p>
i-Dreams H2020 EU Project: Sample dataset
<p>The overall objective of the <em>i</em>-DREAMS project is to setup a framework for the definition, development, testing and validation of a context-aware safety envelope for driving (‘Safety Tolerance Zone’), within a smart Driver, Vehicle & Environment Assessment and Monitoring System (<em>i</em>-DREAMS). Taking into account driver background factors and real-time risk indicators associated with the driving performance as well as the driver state and driving task complexity indicators, a continuous real-time assessment is made to monitor and determine if a driver is within acceptable boundaries of safe operation. Moreover, safety-oriented interventions were developed to inform or warn the driver real-time in an effective way as well as on an aggregated level after driving through an app- and web-based gamified coaching platform. The conceptual framework, which was tested in a simulator study and three stages of on-road trials in Belgium, Germany, Greece, Portugal and the United Kingdom on a total of 600 participants representing car, bus, and truck drivers, respectively. Specifically, the Safety Tolerance Zone (STZ) is subdivided into three phases, i.e. ‘Normal driving phase’, the ‘Danger phase’, and the ‘Avoidable accident phase’. For the real-time determination of this STZ, the monitoring module in the<em> i</em>-DREAMS platform continuously register and process data for all the variables related to the context and to the vehicle. Regarding the operator, however, continuous data registration and processing are limited to mental state and behavior. Finally, it is worth mentioning that data related to operator competence, personality, socio-demographic background, and health status, are collected via survey questionnaires. More information of the project can be seen from project website: https://idreamsproject.eu/wp/</p> <p>This dataset contains naturalistic driving data of various trips of participants recruited in i-Dreams project. Various different types of events are recorded for different intensity levels such as headway, speed, acceleration, braking, cornering, fatigue and illegal overtaking. Running headway, speed, distance, wipers use, handheld phone use, high beam use and other data is also recorded. Driver characteristics are also available but not part of this sample data. In the i-Dreams project, raw data for a particular trip was collected via CardioID gateway, Mobileye, wristband or CardioWheel. These trip data are fused using a feature-based data fusion technique, namely geolocation through synchronization and support vector machines. The system provided by CardioID integrates several data streams, generated by the different sensors that make up the inputs of the i-Dreams system. The sample dataset is fused, processed as well as aggregated to produce consistent time series data of trips for a particular time interval such as 30 secs/ 60 secs or 2- minutes intervals. More datasets can be acquired for analysis purposes by following the data acquisition process given in the data description file.</p>
Sample of 100 place names from 'special maps' by Charles Perthées (1:225,000, 1783-1804)
<p>The dataset contains a sample of 100 place names from ‘special maps’ by Charles Perthées (1:225,000, 1783-1804). Place names are supplemented with possible matches from the “National Register of Geographical Names” (NRGM) or the "Historical Atlas of Poland (HAP). There are 19 266 matches for 100 place names. 85 of them have 'certain' or 'uncertain' matches and 15 remained unmatched and, therefore, not geocoded.</p>
Sampled and simulated benthic invertebrate body-mass data from the Porcupine Abyssal Plain Sustained Observatory (4850 m, 48.83° N 16.50 °W, NE Atlantic)
<p>A dataset of sampled and simulated benthic invertebrate body-mass data from the Porcupine Abyssal Plain Sustained Observatory (4850 m, 48.83° N 16.50 °W, NE Atlantic) has been produced. It includes data on macro- and megabenthos derived from a randomly sampled power law distribution, seabed core samples, large-scale seabed photography, and seabed trawls.</p>
Datasets of fitted toy Monte Carlo samples for "Gaussian Process-based calculation of look-elsewhere trials factor"
<p>For the paper "Gaussian Process-based calculation of look-elsewhere trials factor", we generated and fitted a number of toy Monte Carlo samples which can be used to reproduce figures from the paper or do tests of hypotheses inspired by the method suggested in the article.</p> <p>Together with the hdf5 files containing the samples, we also provide a Jupyter notebook which was used to produce the figures.</p>
Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation at Lake Garda, GAIT site (Italy)
<p>The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site in Lake Garda in Italy (GAIT). It is a subset of the complete data record which consists of the best quality GAIT measurements which could be used for satellite validation. </p><p>The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances, with and without NIR Similarity Correction (see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as:</p><p>\(\rho_wnosc=\pi (Lu-\rho_FLd)/E_d\)</p><p>where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance).</p><p>For the GAIT site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided:</p><p>\(\rho_w=\pi (Lu-\rho_FLd)/E_d-\epsilon\)</p><p>These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p><p>The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included.</p><p>To obtain this dataset, we start from the full GAIT data record and omit all the data that do not pass all the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation:</p><p>1. The coefficient of variation in water reflectance is below 10% in the 580-600 nm range</p><p>2. The water reflectance (after correction for the NIR similarity) at 500 nm is below 0.1</p><p> </p>
Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation from the LPAR site (Argentina)
<p>The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site in Rio de La Plata, LPAR, in Argentina. It is a subset of the complete data record which consists of the best quality LPAR measurements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances (without NIR Similarity Correction, see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance (referred to as reflectance_nosc in the file) defined as:</p> <p><span class="math-tex"><em>ρ</em><em>w</em><em>n</em><em>o</em><em>s</em><em>c</em>=<em>π</em>(<em>L</em><em>u</em>−<em>ρ</em><em>F</em><em>L</em><em>d</em>)/<em>E</em><em>d</em></span></p> <p>where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance).</p> <p>These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included.</p> <p>To obtain this dataset, we start from the full LPAR data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation:</p> <p>1. The coefficient of variation in water reflectance is below 10% in the 400-900 nm range</p> <p>2. The water reflectance at 500 nm is below 0.1</p>
Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation from the VEIT site (Italy)
<p>The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site at Aqua Alta, Venice in Italy (VEIT). It is a subset of the complete data record which consists of the best quality VEIT measurements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances, with and without NIR Similarity Correction (see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as:</p> <p><span class="math-tex">\(\rho_wnosc=\pi (Lu-\rho_FLd)/E_d\)</span></p> <p>where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance).</p> <p>For the VEIT site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided:</p> <p><span class="math-tex">\(\rho_w=\pi (Lu-\rho_FLd)/E_d-\epsilon\)</span></p> <p>These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included.</p> <p>To obtain this dataset, we start from the full VEIT data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation:</p> <p>1. The coefficient of variation in water reflectance is below 10% in the 580-600 nm range</p> <p>2. The water reflectance (after correction for the NIR similarity) above 800 nm is below 0.01</p> <p> </p>
Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation at Berre coastal lagoon, BEFR site (France)
<p>The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site at Etang de Berre in France (BEFR). It is a subset of the complete data record which consists of the best quality BEFR measurements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances, with and without NIR Similarity Correction (see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as:</p> <p><span class="math-tex"><em>ρ</em><em>w</em><em>n</em><em>o</em><em>s</em><em>c</em>=<em>π</em>(<em>L</em><em>u</em>−<em>ρ</em><em>F</em><em>L</em><em>d</em>)/<em>E</em><em>d</em></span></p> <p> </p> <p>where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance).</p> <p>For the BEFR site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided:</p> <p><span class="math-tex"><em>ρ</em><em>w</em>=<em>π</em>(<em>L</em><em>u</em>−<em>ρ</em><em>F</em><em>L</em><em>d</em>)/<em>E</em><em>d</em>−<em>ϵ</em></span></p> <p> </p> <p>These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included.</p> <p>To obtain this dataset, we start from the full BEFR data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation:</p> <p>1. The coefficient of variation in water reflectance is below 10% in the 500-600 nm range</p> <p>2. The water reflectance (after correction for the NIR similarity) between 700-900 nm is below 0.01</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.