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VICINITY IoT Use Case Data Sets
<p>This repository contains Internet-of-Things (IoT) use case data sets from the VICINITY project. The use cases are described under https://vicinity2020.eu, where also the contributing companies are described in more detail. </p> <p>The use cases include: </p> <ul> <li>eHealth use case data by GNOMON/CERTH (anonymized)</li> <li>Smart parking, building control use case data by HITS, TINYM</li> <li>Energy management data set by ENERC</li> <li>Several other, smaller use cases with less data from open call winners. </li> </ul> <p>The data sets have also been the basis for the publications available for download under the same URL; the folder AAU gives additionally the data from AAU's publications. </p> <p> </p>
A Data Set of 255,000 Randomly Selected and Manually Classified Extracted Ion Chromatograms for Evaluation of Peak Detection Methods
<p>Non-targeted mass spectrometry (MS) has become an important method over the last years in the fields of metabolomics and environmental research. While more and more algorithms and workflows become available to process a large number of data sets nontargeted, there still exist few manually evaluated universal test data sets for refining and evaluating these methods. The first step of non-targeted screening, peak detection (and refinement of it) is arguably the most important step for non-targeted screening. However, the absence of a model data set makes it harder for researchers to evaluate peak detection methods. In this Data Descriptor, we provide a manually checked data set consisting of 255,000 EICs (5000 peaks randomly sampled from across 51 samples) for the evaluation on peak detection and gap filling algorithms. The data set was created from a previous real-world study, of which a subset was used to extract and manually classify ion chromatograms by three mass spectrometry experts. The data set consists of:</p> <ul> <li>51 converted mass spectral files in mzML format</li> <li>An .RData-file containing the extracted ion chromtograms (EICs)</li> <li>The randomly selected subset and the original output table of MZmine in .csv-format</li> <li>Example .xlsx files for the classification</li> <li>2 central classification tables</li> <li>Several tables with additional information about the sampling, chemical analysis and expert jugdement on EICs</li> </ul> <p>For a full description of the experiment and the data set, please read the related Data Descriptor with the title "A data set of 255000 randomly selected and manually classified extracted ion chromatograms for evaluation of peak detection methods" in Metabolites (https://www.mdpi.com/journal/metabolites; DOI: https://doi.org/10.3390/metabo10040162).</p>
Data set: Can ocean community production and respiration be determined by measuring high-frequency oxygen profiles from autonomous floats?
<p>Relevant autonomous float data for <a href="https://doi.org/10.5194/bg-17-4119-2020">Gordon et al. (2020)</a>. Following a similar structure to the Argo network's "synthetic" profile files, one file per float is produced with all relevant variables (temperature, salinity, chlorophyll, backscatter, dissolved oxygen) on a common depth and time grid. The Electro-Magnetic Autonomous Profiling Explorer (EM-APEX) floats were deployed in the northern Gulf of Mexico in May 2017 - see <a href="https://doi.org/10.1109/CWTM43797.2019.8955168">Shay et al. (2019)</a> for more information. </p> <p>The data published here contains a timestamp for each data point. Another version of this data which contains some additional variables is hosted on <a href="https://data.gulfresearchinitiative.org/data/R5.x275.281:0001">GRIIDC</a>, but does not contain a timestamp for each data point, but rather for each profile. </p>
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>
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.