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.
7,195
datasets available to search
ShareScore release 0.9.0
Dataset results
7,195 results for “SET”
Data set supporting journal article: Markwitz, C., Knohl, A. and Siebicke, L.: "Evapotranspiration over agroforestry sites in Germany", Biogeosciences, 2020
<p>This data set contains all necessary data needed to replicate figures and analysis presented in the research article: Markwitz, C., Knohl, A. and Siebicke, L.: "Evapotranspiration over agroforestry sites in Germany", Biogeosciences, 2020.</p> <p>In detail, this data set contains 1) meteorological data and half-hourly evapotranspiration rates obtained by a conventional eddy covariance set-up, a low-cost eddy covariance set-up and an energy balance eddy covariance set-up for measurement campaigns of approximately four weeks duration (*_Fluxes_Campaigns_*); 2) raw data to recalculate flux footprints for the campaigns of approximately four weeks duration (*_Campaign_Footprints_*) and for the whole year (*_Annual_Footprints_*); 3) half-hourly evapotranspiration rates obtained by a low-cost eddy covariance set-up and an energy balance eddy covariance set-up gap-filled and corrected for energy balance closure (*_Fluxes_Annual_*). The data were collected at five agroforestry systems and five monoculture agriculture systems without trees across Northern Germany. </p>
Supplementary material (aggregated data set): Egeler, G.-A. & Baur, P. (2020). Menüwahl in der Hochschulmensa: Fleisch oder Vegi? Ergebnisse eines 12-wöchigen Feldexperiments (NOVANIMAL Working Paper No. 5). ZHAW. https://doi.org/10.21256/zhaw-1405
<p><strong>Meal choice at two university canteens in a field experiment during 12 weeks: aggregated menu sales data</strong></p> <p>How do canteen visitors respond to a revised offer of meat-based and plant-based meals? Selected innovations were simultaneously implemented and tested in a trans­disciplinary field experiment in two university canteens over a 12-week period in the autumn semester 2017. Throughout this time, the meat dishes and ‘veg-meals’ (ovo-lacto-vegetarian and vegan meals) were randomly distributed among the three menu lines, the veg-meals were not marketed and advertised as such and the previous vegetarian menu line was abolished. Weeks where the usual number of meat dishes were on offer (the ‘base weeks’) alternated with weeks where the share of veg-meals was increased (the ‘intervention weeks’). <br> The field experiment did not have a negative impact on the number of meals sold or the turnover compared to the two previous years. Women choose meat dishes less often than men. This connection applies in the base weeks and intervention weeks, in all age groups, among both students and among staff. Remarkably, the share of (non-labelled) vegan dishes is comparable for women and men over all age groups, independent of university affiliation (student, staff). Authentic vegan dishes were particularly welcome. Veg-meals could also be sold on the more expensive menu line. There was a better correlation between meal choice, eating habits and attitudes (health, environment, animal welfare, social aspects) than expected. <br> One quarter of canteen visitors show ‘veg-oriented’ eating habits and three quarters thereof ‘meat-oriented’ eating habits. Only a minority of potential visitors eat regularly at the canteen, and those who do exhibit meat-oriented eating habits more often. We conclude, therefore, that the canteen’s usual menu offer is primarily aimed at visitors with meat-oriented eating habits at lunchtime. The most typical visitors to the canteen are male students who select meat dishes.<br> It has been shown, therefore, that the simultaneous changes in supply have worked. Veg-meals are preferred, particularly by women and those prone to flexitarian eating habits; however, also the canteen visitors with meat-oriented eating habits chose veg-meals during the intervention weeks. Catering in canteens has the great potential to expand the range of veg-meals at the expense of meat dishes, provided that the culinary quality is of a high enough standard and meals are not offered as vegetarian or vegan. The question arises as to whether canteens are not missing an economic opportunity if they only offer traditional meat dishes? Canteens are perfectly suited as real-world laboratories in which innovations for sustainable catering can be tried out. The field experiment in the two university canteens is a start; further experiments are needed.</p> <p><strong>The data set contains more than <em>26'000</em> aggregated menu sales. The analyses and results are summarized in the working paper No. 5 <a href="https://doi.org/10.21256/zhaw-1405">https://doi.org/10.21256/zhaw-1405</a></strong></p> <p>The corresponding scripts are: </p> <p>- <a href="http://doi.org/10.5281/zenodo.4034686">10.5281/zenodo.4034686</a></p> <p>- <a href="http://doi.org/10.5281/zenodo.4034698">10.5281/zenodo.4034698</a></p> <p>- <a href="http://doi.org/10.5281/zenodo.4244258">10.5281/zenodo.4244258</a> (newer Version)</p> <p>For more information visit the <a href="http://novanimal.ch">novanimal.ch</a> website.</p>
Supplementary material (ubp & gwp data set): Egeler, G.-A., von Rickenbach, F., & Baur, P. (2020). Menüwahl in der Hochschulmensa: Design & Durchführung Feldexperiment (NOVANIMAL Kurzbericht). ZHAW. https://doi.org/10.21256/zhaw-1408
<p>Calculation of the greenhouse warming potential (gwp) and environmental impact points (UBP, Umweltbelastungspunkte) of 93 meals served on the fieldexperiment in the NOVANIMAL Project. Results of the fieldexperiment see here:</p> <p><a href="https://zenodo.org/deposit/4115429">Egeler, G.-A. & Baur, P. (2020). Menüwahl in der Hochschulmensa: Fleisch oder Vegi? Ergebnisse eines 12-wöchigen Feldexperiments (NOVANIMAL Working Paper No. 5). ZHAW. https://doi.org/10.21256/zhaw-1405</a></p> <p> </p>
Supplementary material (ebp data set): Egeler, G.-A., von Rickenbach, F., & Baur, P. (2020). Menüwahl in der Hochschulmensa: Design & Durchführung Feldexperiment (NOVANIMAL Kurzbericht). ZHAW. https://doi.org/10.21256/zhaw-1408
<p>Calculation of the nutrient balance score of 93 meals served on the fieldexperiment in the NOVANIMAL Project: According two different methods: EBP and Teller Modell. Results of the fieldexperiment see here:</p> <p><a href="https://zenodo.org/deposit/4115429">Egeler, G.-A. & Baur, P. (2020). Menüwahl in der Hochschulmensa: Fleisch oder Vegi? Ergebnisse eines 12-wöchigen Feldexperiments (NOVANIMAL Working Paper No. 5). ZHAW. https://doi.org/10.21256/zhaw-1405</a></p>
Data Set htwddKogRob-TSDChangesSim for Localization and Lifelong Mapping
<p>This dataset provides log files recorded in a changed indoor environment with 18 dynamic obstacles. The changes from the original map to the simulated world are highlighted in the figure htwddKogRob-TSDChangesSim_changesHighlighted.png. The total distance travelled in this data set is 179.8 km. The prior knowledge map the robot got to localize and update is shown in htwddKogRob-TSDChangesSim_prior.png and the simulated now changed map is shown in htwddKogRob-TSDChangesSim_groundTruth.png (for both maps: 1px <span class="math-tex">\(\widehat{=}\)</span> 0.1m).<br> </p> <p>The work was first presented in:</p> <ul> <li>A Fuzzy-based Adaptive Environment Model for Indoor Robot Localization</li> <li>Authors: Frank Bahrmann, Sven Hellbach, Hans-Joachim Böhme</li> <li>Date of Publication: 2016/10/6</li> <li>Conference: Telehealth and Assistive Technology / 847: Intelligent Systems and Robotics</li> <li>Publisher: ACTA Press</li> </ul> <p>Additionally, we present a video with the proposed algorithm and an insight of this dataset under:</p> <ul> <li>youtube.com/AugustDerSmarte</li> <li>https://www.youtube.com/watch?v=26NBFN_XeQg</li> </ul> <p><strong>Instructions for use</strong></p> <p>The zip archives contain ascii files, which hold the log files of the robot observations and robot poses. Since this data set was recorded in a simulated environment, the logfiles include both a changed starting position and a ground-truth pose. For further information, please refer to the header of the logfile. To simplify the parsing of the files, you can use these two Java snippets:</p> <p><strong>Laser Range Measurements:</strong></p> <pre><code class="language-java"> List<Double> ranges = new ArrayList<>(numOfLaserRays); List<Error> errors = new ArrayList<>(numOfLaserRays); String s = line.substring(4); String delimiter = "()"; StringTokenizer tokenizer = new StringTokenizer(s, delimiter); while(tokenizer.hasMoreElements()){ String[] arr = tokenizer.nextToken().split(";"); boolean usable = (arr[0].equals("0")?false:true); double range = Double.parseDouble(arr[1]); ranges.add(range); errors.add(usable?Error.OKAY:Error.INVALID_MEASUREMENT); }</code></pre> <p><strong>Poses:</strong></p> <pre><code class="language-java"> String poseString = line.split(":")[2]; String[] elements = poseString.substring(1, poseString.length()-1).split(";"); double x = Double.parseDouble(elements[0]); double y = Double.parseDouble(elements[1]); double phi = Double.parseDouble(elements[2]);</code></pre> <p> </p>
Data Set htwddKogRob-TSDReal for Localization and Lifelong Mapping
<p>This dataset represents a 4.7 km long tour (odometry path shown in htwddKogRob-TSDReal_path.png) in an environment whose representation (see map htwddKogRob-TSDReal.png | 1px <span class="math-tex">\(\widehat{=}\)</span> 0.1m) is now obsolete. Several static objects have been moved or removed, and there are varying numbers of dynamic obstacles (people walking around).</p> <p>The work was first presented in:</p> <ul> <li>A Fuzzy-based Adaptive Environment Model for Indoor Robot Localization</li> <li>Authors: Frank Bahrmann, Sven Hellbach, Hans-Joachim Böhme</li> <li>Date of Publication: 2016/10/6</li> <li>Conference: Telehealth and Assistive Technology / 847: Intelligent Systems and Robotics</li> <li>Publisher: ACTA Press</li> </ul> <p>Additionally, we present a video with the proposed algorithm and an insight of this dataset under:</p> <ul> <li>youtube.com/AugustDerSmarte</li> <li>https://www.youtube.com/watch?v=26NBFN_XeQg</li> </ul> <p><strong>Instructions for use</strong></p> <p>The zip archive contains ascii files, which hold the log files of the robot observations and robot poses. Since this data set was recorded in a real environment, the logfiles hold only the odometry based robot poses. For further information, please refer to the header of the logfiles. To simplify the parsing of the files, you can use these two Java snippets:</p> <p><strong>Laser Range Measurements:</strong></p> <pre><code class="language-java"> List<Double> ranges = new ArrayList<>(numOfLaserRays); List<Error> errors = new ArrayList<>(numOfLaserRays); String s = line.substring(4); String delimiter = "()"; StringTokenizer tokenizer = new StringTokenizer(s, delimiter); while(tokenizer.hasMoreElements()){ String[] arr = tokenizer.nextToken().split(";"); boolean usable = (arr[0].equals("0")?false:true); double range = Double.parseDouble(arr[1]); ranges.add(range); errors.add(usable?Error.OKAY:Error.INVALID_MEASUREMENT); }</code></pre> <p><strong>Poses:</strong></p> <pre><code class="language-java"> String poseString = line.split(":")[2]; String[] elements = poseString.substring(1, poseString.length()-1).split(";"); double x = Double.parseDouble(elements[0]); double y = Double.parseDouble(elements[1]); double phi = Double.parseDouble(elements[2]);</code></pre> <p> </p>
Data Set htwddKogRob-InfReal for Localization and Lifelong Mapping
<p>This small dataset contains real world log files from a 2.2 km long patrol between two points of a previously known map (see htwddKogRob-InfReal.png | 1px <span class="math-tex">\(\widehat{=} \)</span> 0.1m). The environment changes slightly and there are some dynamic obstacles. The figure (see htwddKogRob-InfReal_path.jpg) shows the path driven by the robots according to the real kilometers driven and the patrol points.</p> <p>The work was first presented in:</p> <ul> <li>A Fuzzy-based Adaptive Environment Model for Indoor Robot Localization</li> <li>Authors: Frank Bahrmann, Sven Hellbach, Hans-Joachim Böhme</li> <li>Date of Publication: 2016/10/6</li> <li>Conference: Telehealth and Assistive Technology / 847: Intelligent Systems and Robotics</li> <li>Publisher: ACTA Press</li> </ul> <p>Additionally, we present a video with the proposed algorithm and an insight of this dataset under:</p> <ul> <li>youtube.com/AugustDerSmarte</li> <li>https://www.youtube.com/watch?v=26NBFN_XeQg</li> </ul> <p><strong>Instructions for use</strong></p> <p>The zip archive contains ascii files, which contain the log files of the robot observations and robot poses. Since this data set was recorded in a real environment, the logfile provides only the odometry based robot poses. For further information, please refer to the header of the logfile. To simplify the parsing of the files, you can use these two Java snippets:</p> <p><strong>Laser Range Measurements:</strong></p> <pre><code class="language-java"> List<Double> ranges = new ArrayList<>(numOfLaserRays); List<Error> errors = new ArrayList<>(numOfLaserRays); String s = line.substring(4); String delimiter = "()"; StringTokenizer tokenizer = new StringTokenizer(s, delimiter); while(tokenizer.hasMoreElements()){ String[] arr = tokenizer.nextToken().split(";"); boolean usable = (arr[0].equals("0")?false:true); double range = Double.parseDouble(arr[1]); ranges.add(range); errors.add(usable?Error.OKAY:Error.INVALID_MEASUREMENT); }</code></pre> <p><strong>Poses:</strong></p> <pre><code class="language-java"> String poseString = line.split(":")[2]; String[] elements = poseString.substring(1, poseString.length()-1).split(";"); double x = Double.parseDouble(elements[0]); double y = Double.parseDouble(elements[1]); double phi = Double.parseDouble(elements[2]);</code></pre> <p> </p> <p> </p>
Phenology data set of plants and birds and other taxonomic groups, as well as agrarian activities and abiotic phenomena from Latvia, 1970-2018
<p>A data set of phenological observations of plants, birds, as well as agrarian activities and abiotic phenomena from Latvia, 1970-2018 is presented. The data include limited number of observations of insects, amphibians, mammals, mushrooms, mollusks and fishes as well. The data was collected by voluntary observers (citizen scientists) and published as paper based yearly bulletins. It includes almost 48 000 individual observations of 159 different phenological phases from 103 locations in Latvia. Each entry is comprised of following fields:</p> <ol> <li>Station: name of the observation station</li> <li>Year: year of observation</li> <li>Season: season of observation as indicated in the primary publication</li> <li>Species: English name of the species observed or description of phenomena observed in case of abiotic occurrences</li> <li>Species Latin: Latine name of the species observed</li> <li>Taxonomic_group: taxonomic group of the species observed or grouping of non-biological phases (“Abiotic” for meteorological phenomena and “Agrarian” for agrarian activities)</li> <li>Phenophase: description of phenological phase observed</li> <li>BBCH: attributed BBCH code for phenological phase observed, where applicable</li> <li>Date: date of the first observation of the phase</li> <li>DoY: day of the year of the first observation of the phase</li> <li>Implausible: flag indicating of the reported date of phenological phase is highly implausible (TRUE) or realistic (FALSE)</li> <li>Wrong_order: flag indicating if the order of the reported phases at a given station and year is not realistic (TRUE) or realistic (FALSE)</li> </ol>
Case study result data set for Energy Economics article "Demystifying market clearing and price setting effects in low-carbon energy systems"
<p>The data set contains country-specific power generation and consumption time series data for the European energy system, including both traditional and new market participants due to cross-sectoral integration.</p> <p>Country codes: ALPHA-3<br> Unit: Megawatt (electric) (interval average values, i.e. MWh/h)</p> <p><strong>Generation technology types</strong></p> <ul> <li>batteryStorage (Li-Ion)</li> <li>conventionalHydro (aggregated for different equivalent hydropower systems)</li> <li>natural_gas_CC_COND (Combined Cycle Gas Turbine)</li> <li>natural_gas_CC_EXCOND (Combined Cycle Gas Turbine as extraction condensing CHP plant for district heating)</li> <li>natural_gas_GT_COND (Open-Cycle Gas Turbine)</li> <li>natural_gas_GT_EXCOND (Open-Cycle Gas Turbine as extraction condensing CHP plant for industry)</li> <li>offshoreWind (aggregated for different LCOE and IEC wind turbine classes)</li> <li>offshoreWindExplicit (offshore wind generation considered for offshore grid investments in the North Seas area, aggregated for different LCOE classes)</li> <li>onshoreWind (solar PV, aggregated for different LCOE classes)</li> <li>other (geothermal, waste)</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> <li>solar (solar PV, aggregated for different LCOE classes)</li> <li>uran_ST_COND (steam turbine condensing power plant)</li> </ul> <p><strong>Consumption technology types</strong></p> <ul> <li>BEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>PHEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>airConditioning</li> <li>batteryStorage (Li-Ion)</li> <li>conventionalLoad</li> <li>heatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridHeatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridTruck (Hybrid Overhead-Line truck)</li> <li>largeScaleDirectResistiveHeating (Centralised CHP systems)</li> <li>natural_gas_CC_EXCOND_electrodeHeater</li> <li>natural_gas_CC_EXCOND_heatpumpHeater</li> <li>natural_gas_GT_EXCOND_electrodeHeater</li> <li>natural_gas_GT_EXCOND_heatpumpHeater</li> <li>powerToGas</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> </ul>
Hyperspectral X-ray CT data set of mineralised ore sample with Au and Pb deposits
<p><strong>General data description:</strong></p> <p>This is a hyperspectral (energy-resolved) X-ray CT projection data set of a mineralised ore sample with small gold and galena deposits. It was acquired in a laboratory micro-CT scanner with an energy-sensitive HEXITEC detector in the Henry Moseley X-ray Imaging Facility at The University of Manchester.</p> <p>The data included contains all the relevant files required for reconstruction, following a hyperspectral scan of a mineralised ore sample. The sample contains a number of mineral phases, of varying concentration, distributed throughout. Some phases (including gold, and lead-based Galena) produce unique absorption edges, which act as spectral identifiers that can be measured by an energy-sensitive detector.</p> <p><strong>File descriptions:</strong></p> <p>The data set consists of one .txt file and three .mat (MATLAB) data files.</p> <p>Au_rock_scan_geometry.txt gives a breakdown of the full sample and detector geometry used when acquiring the raw projections. The number of horizontal detector pixels accounts for the fact that a set of 5 tiled scans of the sample were collected and later stitched together.</p> <p>Au_rock_sinogram_full.mat contains the full 4D sinogram constructed following flat-field normalisation of the raw projection data. The data matrix contains the total number of energy channels acquired during scanning, as well as the conventional elements of vertical/horizontal detector pixel number and total projection angles.</p> <p>commonX.mat provides a direct conversion between the energy channels, and the energies (in keV) that they correspond to, following a calibration procedure prior to scanning.</p> <p>FF.mat contains the 4D flatfield data acquired when no sample was present. This data was used to normalise the projection datasets, as the sinogram was constructed.</p>
Anthropometric data set
<p>The data set describes anthropometric data in extreme positions: standard, relaxed posture, self-hug, leaning, tilting, arms raised, lingering. Data were analyzed in tight clothing. The protective clothing used during the Covid-19 pandemic was analyzed in two positions for the same test subject (relaxed and raised arms). The data show the fit of the suit to the body. And changes in the body's anthropometric data as a result of various movements.</p>
Data set to Conference Paper "The Effect of Queuing Technology on Customer Experience in Physical Retail Environments"
<p>Following an open data policy as supported by the European Union (https://www.openaire.eu/), this is the data set used for the following conference paper: Obermeier, G., Zimmermann, R., & Auinger, A. (2020, July). The Effect of Queuing Technology on Customer Experience in Physical Retail Environments. In <em>International Conference on Human-Computer Interaction</em> (pp. 141-157). Springer, Cham.</p> <p>The present work was conducted within the Innovative Training Network project PERFORM funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 765395. The EU Research Executive Agency is not responsible for any use that may be made of the information it contains.</p>
Supporting Data Sets for "New Constraints on the Lunar Optical Space Weathering Rate"
<p>Data Sets supporting "New Constraints on the Lunar Optical Space Weathering Rate" submitted to Geophysical Research Letter on 12/18/2020. See Supporting Information (link TBD).</p>
Bath Natural Environment HAR Data Set
<p>The data set contains recording from 5 9-axis IMU (MARG) sensors. Attached to ankles, hips and chest. The sensors were sampled at 100Hz. The experiment involved 22 subjects walking around natural environments wearing the five sensors. Though a BLE<br> connections the sensors streamed data to an app on an android phone. The subjects labeled data in real time using buttons in the app. The data was collected in an unsupervised manner and shared with the researchers anonymously.</p> <p>The following activities were recorded; Walking, Ramp Ascent, Ramp Descent, Stair Ascent, Stair Descent, Stopped</p> <p>Please Cite</p> <p>Sherratt, F.; Plummer, A.; Iravani, P. Understanding LSTM Network Behaviour of IMU-Based Locomotion Mode Recognition for Applications in Prostheses and Wearables. <em>Sensors</em> <strong>2021</strong>, <em>21</em>, 1264. https://doi.org/10.3390/s21041264</p>
Spectral Induced Polarization (SIP) field data sets for comparison between time and frequency domain (Martin et al. 2020)
<p>Data and scripts for the scientific journal article:</p> <p>Martin, T., Günther, T., Flores Orozco, A. & Dahlin, T. (2020): <br> Evaluation of spectral induced polarization field measurements in time and frequency domain, <br> J. Appl. Geophys., 180, 104141, doi:10.1016/j.jappgeo.2020.104141.</p> <p>The paper deals with the comparison of instruments for measuring induced polarization (IP)<br> data, both in frequency domain (FD) as well in time-domain. Four instruments have been used<br> to measure two profiles (IP1: a=1m, IP5: a=5m) over a known blackshale near the town Schleiz<br> in Thuringia, Germany.</p> <p>Preface: Our vision is not only to publish scientific results, but also openly demonstrate<br> how the results have been produced. This includes the raw data, processed data and inversion<br> results, but also all scripts that have been used for it in a reproducible way.<br> The scripts use the pyBERT (Python Boundless Electrical Resistivity Tomography) based on the<br> pyGIMLi (Python Geophysical Inversion and Modelling Library) computational framework.<br> Here, we used the pyBERT version 2.3.1 based on pygimli 1.1.1, but it should be reachable by<br> similar versions as well. All is based on the classes FDIPdata and TDIPdata.<br> For installing, we refer to the webpages www.pygimli.org and gitlab.com/resistivity-net/bert</p> <p>Folders:<br> figures figures used for production process, generated by the scripts in the folder<br> scripts Python scripts for generating the pdf files<br> IP1 data for the shallow profile using an electrode spacing of a=1m<br> IP5 data for the deeper profile using an electrode spacing of a=5m</p> <p>Each of the two data folders contains data and inversion results in separated folder named<br> according to the instrument names: <br> * SIP256C (Radic Research) - FDIP instrument with remote units (intelligent electrodes)<br> * DAS-1 (Multi-Phase Technology) - FDIP and TDIP instrument using multi-core cables<br> * Terrameter LS2 (GuidelineGeo ABEM) - TDIP instrument using multi-core cables<br> * Syscal Pro Switch72 (Iris instruments) - TDIP instrument using multi-core cables</p> <p>Please see readme files in the individual folders for specific information.</p> <p>Notice: In additon to the published results, we also added more data from the field <br> measurements which completes our results, e.g. <br> - TDIP data with 8s aquisition time<br> - all four instruments for the long profile IP5</p> <p>Complete full-waveform data for the Terrameter LS2 are available on request.</p>
Data set with length measurments of Machu Picchu
<p>The base of a niche was considered to be a construction level where in the past the architectural module could be applied. To check this possibility the width and the distances between niches were measured in the 3D point cloud for further cosine quantogram analysis. Thus, 11 data sets were created, each corresponding to a particular sector or distinguish part of it, from the area so-called <em>zona urbana</em> in Machu Picchu site. The size of each sample depends on the amount and size of buildings in a sector, so samples vary from 44 to 244 measurements, measured in centimetre [cm].</p>
Adsorption kinetics data sets, compiled from the literature. As used in the research article "A revised pseudo-second order kinetic model for adsorption, sensitive to changes in adsorbate and adsorbent concentrations"
<p>Data sets reporting experimental adsorption kinetics, compiled from the literature. These data sets were subjected to empirical analysis in the development of our revised pseudo-second order rate equation (the rPSO model) as discussed in the ChemRxiv pre-print "<a href="https://chemrxiv.org/articles/preprint/A_Revised_Pseudo-Second_Order_Kinetic_Model_for_Adsorption_Sensitive_to_Changes_in_Sorbate_and_Sorbent_Concentrations/12008799">A Revised Pseudo-Second Order Kinetic Model for Adsorption, Sensitive to Changes in Sorbate and Sorbent Concentrations</a>".</p>
Data set for "Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing"
<p>Data set for: Gasselin C, Hohl B, Vernet A, Crochet C, Petersen CCH (2021) Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing. Neuron doi: 10.1016/j.neuron.2020.12.018</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2021_Gasselin_Neuron.pdf" is the Open Access pdf of the online publication in Neuron.</p> <p>2. The file named "Gasselin_data_code.zip" (~9 GB) is a zipped version of a folder "Gasselin_data_code" (~13 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. To access the data and the code, first unzip the file. Then add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘Gasselin_data_code’). Each code computes and plots the results used in the corresponding figure. Figures and Tables are saved in the subfolder ‘Figures’.</p> <p>The subfolder ‘Functions’ contains functions called by the main codes.</p> <p>The main folder contains the following codes:</p> <p><em>Gasselin_Figure1: computes and plots the results for the panels D, E and F of figure 1.</em></p> <p><em>Gasselin_Figure2: computes and plots the results for the panels B and C of figure 2.</em></p> <p><em>Gasselin_Figure3: computes and plots the results for the panels B, C and D of figure 3.</em></p> <p><em>Gasselin_Figure4: computes and plots the results for the panels A, B and C of figure 4.</em></p> <p><em>Gasselin_FigureS1: computes and plots the results for the panels A, B and C of figure S1.</em></p> <p><em>Gasselin_FigureS2: computes and plots the results for the panels A and B of figure S2.</em></p> <p> </p> <p>The subfolder ‘Data’ contains the data structures used for the different figures:</p> <p><em>data_figure1.mat</em></p> <p><em>data_figure2.mat</em></p> <p><em>data_figure3.mat</em></p> <p><em>data_figure4_MECA.mat</em></p> <p><em>data_figure4_Activation.mat</em></p> <p><em>data_figure4_Inactivation.mat</em></p> <p><em>data_figureS2_Activation.mat</em></p> <p><em>data_figureS2_Inactivation.mat</em></p> <p><em>data_Axon.mat</em></p> <p> </p> <p>The data structures contain the following fields:</p> <p><em>Mouse_Name</em> : name of the mouse.</p> <p><em>Mouse_DateOfBirth</em>: date of birth of the mouse (YMD).</p> <p><em>Mouse_Sex</em>: sex of the mouse (F or M).</p> <p><em>Mouse_Genotype</em>: genotype of the mouse.</p> <p><em>Mouse_Drug</em>: experimental condition of the recording (control = ‘No Drug’; blockade of glutamatergic transmission = ‘CNQX_DAPV’; blockade of glutamatergic transmission and nicotinic receptors = ‘CNQX_DAPV_MECA’; blockade of nicotinic receptors only = ‘MECA’).</p> <p><em>Mouse_Virus</em>: virus injected if any.</p> <p><em>Cell_Counter</em>; cell recorded in a given mouse.</p> <p><em>Cell_Type</em>: type of the recorded cell based on 2P imaging. (EXC, VIP, PV, SST, 5HT3aR_non_VIP).</p> <p><em>Cell_Depth</em>: depth of the recorded cell relative to pia (µm).</p> <p><em>Cell_TargetedBrainArea</em>: cortical area targeted (C2 column of the barrel cortex = C2).</p> <p><em>Cell_Fluorescence</em>: expression of the genetically encoded fluorophore (FALSE or TRUE). A neuron recorded in a VIP_IRES_Cre x LSL_tdTomato (cf <em>Mouse_Genotype</em>) with <em>Cell_Fluorescence</em>=TRUE is considered as a VIP neuron (cf <em>Cell_Type</em>).</p> <p><em>Sweep_Counter</em>: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-60 s).</p> <p><em>Sweep_Type</em>: experimental condition during that sweep (Only spontaneous whisking onset = ‘Onset’; Whisking onset and whisker stimulus = ‘Onset_Whisker_Stim’ ; Optogenetic stimulation = ‘Opto_Stim’; Optogenetic activation = ‘Opto_Activation’; Optogenetic inactivation = ‘Opto_Inactivation’; ).</p> <p><em>Sweep_Start_Time</em>: time at the beginning of the sweep recording (YMDHms).</p> <p><em>Sweep_WhiskerAngle</em>: C2 whisker angular position extracted from simultaneous high-speed video filming (deg).</p> <p><em>Sweep_WhiskerAngle_SamplingRate</em>: sampling rate of the whisker angle trace.</p> <p><em>Sweep_WhiskingOnset_Time</em>: time of identified whisking onset - excluding any whisker stimulus shortly before or after (s).</p> <p><em>Sweep_MembranePotential</em>: membrane potential recording (mV) after cutting of the APs.</p> <p><em>Sweep_MembranePotential_SamplingRate</em>: sampling rate of the membrane potential signal (pt.s<sup>-1</sup>).</p> <p><em>Sweep_CurrentInjected</em>: current injected into the cell (pA).</p> <p><em>Sweep_CurrentInjected_SamplingRate</em>: sampling rate of current signal (pt.s<sup>-1</sup>).</p> <p><em>Sweep_WhiskerStim_Name</em>: whisker to which the magnetic stimulus was applied to (C2 or B2&C2).</p> <p><em>Sweep_WhiskerStim_Time</em>: onset times of the whisker stimulus (s).</p> <p><em>Sweep_OptoStim_Power</em>: light power applied for optogenetic manipulations (% of the max power).</p> <p><em>Sweep_OptoStim_Time</em>: onset times of the light pulses for optogenetic manipulations (s).</p> <p><em>Cell_ID</em>: unique cell identifier (= <em>Mouse_Name</em>+<em>Cell_Counter</em>).</p> <p><em>SpikeThreshold</em>: spike threshold used to detect APs (mV).</p> <p><em>Trial_WhiskingOnset</em>: data structure containing the cut signals used to compute averaged responses around whisking onset times.</p> <p><em>Trial_WhiskerStim</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times.</p> <p><em>Trial_WhiskerStim_QuietTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without whisker movements.</p> <p><em>Trial_WhiskerStim_WhiskingTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with whisker movements.</p> <p><em>Trial_Opto</em>: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times.</p> <p><em>Trial_OptoAndWhisker_QuietTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and no whisker movements.</p> <p><em>Trial_OptoAndWhisker_WhiskingTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and with whisker movements.</p> <p><em>Trial_OnlyWhisker_QuietTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and without whisker movements.</p> <p><em>Trial_OnlyWhisker_WhiskingTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and with whisker movements.</p> <p><em>Trial_OnlyOpto</em>: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times in trials without whisker stimulus.</p>
Spatial distribution data set of wetlands in Baiyangdian Basin
<p>As one of the wetland systems in the northern plain of China, Baiyangdian plays a key role in ensuring the water resources security and good ecological environment of Xiong'an New Area. Understanding the current situation of the wetland ecosystem in Baiyangdian basin is also of great significance for the construction of the New Area and future scientific planning. Based on the 10 meter spatial resolution sentinel-2B image provided by ESA in September 2017, combined with Google Earth high resolution satellite image (resolution 0.23m), the network distribution map and water system distribution map of Baiyangdian basin wetland ecosystem in 2017 were drawn by artificial visual interpretation and machine automatic classification It provides the basis for the study of the connectivity (including hydrological connectivity and landscape connectivity).</p> <p>The boundary of Baiyangdian basin in this data set is from the basic geographic information map of Baiyangdian basin provided by Zhou Wei and others. The DEM is the GDEM digital elevation data with 30m resolution. The original image data of wetland remote sensing classification comes from the sentinel-2b remote sensing image provided by ESA on September 20, 2017. This data set uses the second, third, fourth and eighth bands of 10 meter resolution in the image, carries out radiation calibration, mosaic, mosaic and other preprocessing operations in SNAP and ArcGIS 10.2 software, and carries out supervised classification in ENVI 5.3 software. The data used for river channel extraction is based on Google Earth high resolution satellite images.</p> <p>The research and development steps of this dataset include: preprocessing sentinel-2B image, establishing wetland classification system and selecting samples, mapping the latest wetland ecosystem network distribution map of Baiyangdian basin by support vector machine classification; obtaining river network of Baiyangdian basin by visual interpretation based on Google Earth high resolution satellite image (resolution 0.23m).</p> <p>The spatial distribution data set of Baiyangdian Wetland includes vector data and raster data: (1) Baiyangdian basin boundary data (. SHP); Baiyangdian basin river network data (. shp); (2) Baiyangdian basin land use / cover classification data (including the classification data of the study area and the river 3 km buffer) (. tif); Baiyangdian basin constructed wetland and natural wetland distribution map (. shp); Baiyangdian basin slope map (. tif).</p> <p>According to the river network map of Baiyangdian basin obtained by manual visual interpretation, the total length of the river in Baiyangdian basin is about 2440 km and the total area is 514 km2. Among them, there are 177 km2 river channels in mountainous area, 866 km in length, distributed in Northeast southwest direction, mostly at the junction of forest land and cultivated land; and 337 km2 river channels in plain area, 1574 km in length.</p> <p>Baiyangdian basin is divided into eight types of land use / cover: river, flood plain, lake, marsh, ditch, cultivated land, forest land and construction land. The remote sensing monitoring results show that the wetland area of Baiyangdian basin accounted for 13.90 % in 2017. Among all wetland types, the area of marsh is the largest, followed by the area of flood plain, ditch accounts for about 1%, and the proportion of lake and river is less than 0.5%. Combined with the land use / cover classification map and the distribution of slope and elevation, it can be seen that nearly 60% of the area of woodland is distributed in 10 ° to 30 ° mountain area, and the rest of the land use / cover types are mainly distributed in 0 ° to 2 ° area. The elevation statistics show that nearly 80% of the lakes and large reservoirs are distributed in the height of 100 m to 300 m, the distribution of marsh is relatively uniform, mainly in the high altitude area of 20 m to 300 m, the types of construction land, flood area and cultivated land are mainly concentrated in the area of 20 m to 100 m, and rivers and ditches are mainly concentrated in the area of 0 m to 100 m.</p> <p>Based on the classification results of land use / cover within the river, it can be found that the main land use type is wetland. Specifically, the types of swamp, flood area and lake are the most, while the types of ditch and river are less. With the increase of the buffer area, the proportion of non wetland type gradually increased, while the proportion of wetland type gradually decreased. The main wetland types in 1-3km buffer zone on both sides of the river are swamp and flood zone. It is worth noting that nearly one third of the River belongs to cultivated land, that is, the river occupation is serious. In terms of area, about 1 / 3 rivers and 3 / 4 lakes are distributed in the river course. Most of the water bodies in the river course are controlled by human beings, but the marsh area in the river course only accounts for about 3% of the marsh area in the whole river course.</p> <p>River occupation will not only directly reduce the connectivity of wetlands in the basin, but also cause some environmental and economic problems such as water pollution. However, if the connectivity of wetlands is reduced, the ecological and environmental functions of wetlands will be destroyed, which will pose a great threat to the water security of the basin. Taking Baiyangdian basin as a whole, improving the connectivity of wetlands and enhancing the ecological and environmental functions of wetlands in the basin will help to improve the water ecological and environmental security of xiong'an new area and Baiyangdian basin.</p>
A dissymmetric [Gd2] coordination molecular dimer hosting six addressable spin qubits. Open data sets
<p>Includes data relevant for publication with DOI <a href="https://doi.org/10.1038/s42004-020-00422-w">10.1038/s42004-020-00422-w</a> plus a table with information about how the data were obtained and processed.</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.