Find research datasets worth reusing

opencc-by-4.0Jun 2024View details →

Virtual screening data sets for fragmented interaction fingerprint

Data sets containing docking poses, structure-activity relationship matrix (SARM) train-test splitting, and 2048 bits extended connectivity fingerprint (ECFP) descriptors for six biological targets. Poses were generated by employing molecular docking using Molecular Operating Environment (MOE) software. Details will be described in the original publication.

opencc-by-4.0Jun 2024View details →

Data set for the manuscript 'Leveraging pleiotropy identifies common-variant associations with selective IgA deficiency'

Open the record for dataset details and reuse information.

Weak Exchange Interactions in Multispin Systems: EPR Studies of Metalloporphyrins Decorated with {Cr7Ni} Rings. Open data set

Data supporting the original figures 2, 3, 4b, 5, and 6 of the related publication.

Data from: Lupus and inflammatory bowel disease share a common set of microbiome features distinct from other autoimmune disorders

Supplemental data for "Lupus and inflammatory bowel disease share a common set of microbiome features distinct from other autoimmune disorders"

opencc-by-4.0Feb 2023View details →

ITC data set of DNA binding by YdaT repressor from Escherichia coli O157:H7

Raw isothermal titration calorimetry data set from the published article Prolic-Kalinsek, M., Volkov, A. N., Hadzi, S., Van Dyck, J., Bervoets, I., Charlier, D. & Loris, R. Structural basis of DNA binding by YdaT, a functional equivalent of the CII repressor in the cryptic prophage CP-933P from Escherichia coli O157:H7. (2023). Acta Cryst. D79, 245-258. DOI: 10.1107/S2059798323001249. Concentrations in the files are expressed as monomer protein and duplex DNA. Titrations were measured at 25 °C. Buffer is 10 mM NaH2PO4, 10 mM Na2HPO4, 100 mM NaCl, 50 mM glutamic acid, 50 mM arginine pH 7.5.

Data sets and code for Sullivan and Koski "The role of photosynthetic response to environmental variation in shaping an elevational cline in leaf variegation"

Open the record for dataset details and reuse information.

opencc-by-4.0Sep 2021View details →

ITC data set of nanobody (Nb33) binding to PaaR2 repressor truncates from Escherichia coli O157:H7

Raw isothermal titration calorimetry data set from the published article De Bruyn, P., Prolič-Kalin&scaron;ek, M., Vandervelde, A., Malfait, M., Sterckx, Y. G. J., Sobott, F., Hadži, S., Pardon, E., Steyaert, J., & Loris, R. (2021). Nanobody-aided crystallization of the transcription regulator PaaR2 from Escherichia coli O157:H7. Acta crystallographica. Section F, Structural biology communications, 77(Pt 10), 374–384. https://doi.org/10.1107/S2053230X21009006. Titrations were measured at different temperatures (5-37 °C, indicated in the file name). Concentrations are listed in each itc data file. Buffer is 10 mM NaH2PO4, 10 mM Na2HPO4, 150 mM NaCl, 0.01% Triton X-100, pH 7.5.

Source data sets_Figure 1, 2, 6_Salmonella cancer therapy metabolically disrupts tumours at the collateral cost of T cell immunity

Flow cytometry data files associated with Copland et al., Salmonella cancer therapy metabolically disrupts tumours at the collateral cost of T cell immunity. Data associated to Figures 1, 2 and 6.

Data Set for Probabilistic Indoor Temperature Forecasting

<h1>1. Dataset Manifest</h1> This text provides a description of the dataset used for model training and evaluation in our study "A Tutorial on Deep Learning for Probabilistic Indoor Temperature Forecasting". The dataset consists of various simulated thermal and environmental parameters for different room configurations. Below, you will find a table detailing each column in the dataset along with its description and unit of measurement.  <h2>1.1. Columns Description</h2> <table> <tbody> <tr> <th>Column Name</th> <th>Description</th> <th>Unit</th> </tr> <tr> <td><code>time</code></td> <td>Time stamp of the measurement</td> <td>-</td> </tr> <tr> <td><code>ZweiPersonenBuero.TAir</code></td> <td>Air temperature inside a two-person office</td> <td>°C</td> </tr> <tr> <td><code>heatStat.Heat.Q_flow</code></td> <td>Heating rate in the room</td> <td>W</td> </tr> <tr> <td><code>weaDat.AirPressure</code></td> <td>Atmospheric pressure</td> <td>Pa</td> </tr> <tr> <td><code>weaDat.AirTemp</code></td> <td>Outside air temperature</td> <td>°C</td> </tr> <tr> <td><code>weaDat.SkyRadiation</code></td> <td>Longwave sky radiation</td> <td>W/m²</td> </tr> <tr> <td><code>weaDat.TerrestrialRadiation</code></td> <td>Terrestrial radiation</td> <td>W/m²</td> </tr> <tr> <td><code>weaDat.WaterInAir</code></td> <td>Absolute humidity</td> <td>g/kg</td> </tr> <tr> <td><code>VAir</code></td> <td>Air volume in the room</td> <td>m³</td> </tr> <tr> <td><code>AExt0</code></td> <td>Exterior wall area facing the south</td> <td>m²</td> </tr> <tr> <td><code>AExt1</code></td> <td>Exterior wall area facing the north</td> <td>m²</td> </tr> <tr> <td><code>AInt</code></td> <td>Total interior wall area</td> <td>m²</td> </tr> <tr> <td><code>AFloor</code></td> <td>Floor area of the room</td> <td>m²</td> </tr> <tr> <td><code>AWin0</code></td> <td>Window area facing the south</td> <td>m²</td> </tr> <tr> <td><code>AWin1</code></td> <td>Window area facing the north</td> <td>m²</td> </tr> <tr> <td><code>azi0</code></td> <td>Azimuth (direction) of the first exterior wall</td> <td>rad</td> </tr> <tr> <td><code>azi1</code></td> <td>Azimuth (direction) of the second exterior wall</td> <td>rad</td> </tr> <tr> <td><code>id</code></td> <td>Unique identifier for the room configuration</td> <td>-</td> </tr> <tr> <td><code>is_holiday</code></td> <td>Indicator whether the day is a holiday (1 for yes, 0 for no)</td> <td>-</td> </tr> </tbody> </table> <h2>1.2. Note on Multi-Value Columns</h2> <div> For rooms with multiple exterior walls (rooms 15-30): <ul> <li>AExt: {Exterior wall 1 area, Exterior wall 2 area}</li> <li>AWin: {Window area on exterior wall 1, Window area on exterior wall 2}</li> <li>azi: {Azimuth of exterior wall 1, Azimuth of exterior wall 2}</li> </ul> Example: <ul> <li>AExt = {10, 15}</li> <li>AWin = {2, 0}</li> <li>azi = {0, 3.1415}</li> </ul> This indicates two exterior walls with areas of 10 m² and 15 m² facing south (0 rad) and north (3.1415 rad), respectively. The south-facing wall has a window of 2 m², while the north-facing wall has no window. <h2>1.3. Data Sources</h2> <div> <ul> <li>Room Model: Simulated using the reduced-order package of the Modelica Buildings Library.</li> <li>Weather Data: Provided by the German Meteorological Service (DWD) in Test Reference Year (TRY) format.</li> </ul> This comprehensive dataset provides crucial parameters required to train and evaluate thermal models for different room configurations. The simulation data ensures a diverse range of environmental and occupancy conditions, enhancing the robustness of the models. <h2>1.4. Data scaling</h2> The data set contains the raw data as well as the scaled data used for training and testing the model. The scaling was carried out using the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html" target="_blank" rel="noopener">StandardScaler</a> package. <h2>1.5. Weather data license</h2> This data set contains weather data recorded by the DWD under license &bdquo;Datenlizenz Deutschland – Namensnennung – Version 2.0" (<a href="https://www.govdata.de/dl-de/by-2-0" target="_blank" rel="noopener">URL</a>). The data is provided by "<a href="https://www.bbsr.bund.de" target="_blank" rel="noopener">Bundesinstitut für Bau-, Stadt- und Raumforschung</a>". The data can be downloaded from <a href="https://www.bbsr.bund.de/BBSR/DE/forschung/programme/zb/Auftragsforschung/5EnergieKlimaBauen/2013/testreferenzjahre/01-start.html?pos=2" target="_blank" rel="noopener">here</a>. We use data from the year 2015 from Heilbronn. We have added the weather data to the data set unchanged. </div> </div>

opencc-by-nc-4.0Sep 2024View details →

Mobility and persistence of pesticides and emerging contaminants in age-dated and redox-classified groundwater under a range of land use types [data set]

Data set covering measured pesticides and emerging contaminants, the tritium and noble gas data and metadata for which the results are discussed in the publication in Science of the Total Environment (2024); doi: 10.1016/j.scitotenv.2024.176344

Data Set - Seasonal variability in the global relevance of mountains to satisfy lowland water demand

GENERAL INFORMATION The data and scripts used for the analysis of the manuscript "Seasonal variability in the global relevance of mountains to satisfy lowland water demand" When using this dataset, please refer to the original publication in addition to this Zenodo repository. DATA & FILE OVERVIEW please have a look at readme.txt  Don't hesitate to contact us in case of any questions (sarah.hanus@geo.uzh.ch)

EEG Data from a Within-Subjects Study Comparing P300 Speller Performance with Different Electrode Sets

The dataset contains EEG data of 10 participants who completed a P300 speller task with different electrode sets. 4 different electrode sets were used: <ul> <li>4-electrode set: Pz, POz, P7 and P8</li> <li>6 -electrode set: Pz, POz, P3, P4, O1 and O2</li> <li>8-electrode set: Cz, Pz, POz, P3, P4, O1, O2 and Oz</li> <li>16-electrode set: FC1, FC2, C3, C4, Cz, CP1, CP2, Pz, POz, P3, P4, P7, P8, O1, O2 and Oz</li> </ul> Each participant completed 3 runs of the speller (i.e. copy-spelled 3 words) with each electrode set. 10 flashes per row and column were used for the first run of each set, 5 for the second run, and 3 for the third run. The order of the electrode sets was randomised. Subjects 1 to 5 completed the experiment in an office location with natural light, Subjects 6 to 10 completed the experiment in a different office location with artificial light. An LDA classifier was trained on 2 calibration runs for each electrode set. For the 8-electrode set, an xDAWN spatial filter was used to reduce the 8 channels to 2 xDAWN components. For the 16-electrode set, an xDAWN spatial filter was used to reduce the 16 channels to 3 xDAWN components. Each participant folder contains: <ul> <li>calibration-signal[1/2/s].ov - unprocessed EEG signals from P300 speller runs used to calibrate the BCI (i.e. train the LDA classifier and xDAWN spatial filter, where applicable), in Openvibe (.ov) file format, see details below</li> <li>run-[x]-raw-[y].[ov/mat] – unprocessed EEG signals from [y] electrodes for a P300 speller run with [x] flashes per row and column in Openvibe (.ov) and Matlab (.mat) file formats, see details of the runs below</li> <li>classifier[y].cfg - LDA classifier weights for [y] electrodes</li> <li>spatial-filter[y].cfg - xDAWN spatial filter weights for [y] electrodes (xDAWN spatial filter only used for the 8- and 16-electrode sets)</li> <li>log.txt - contains the performance in the P300 speller runs for all electrode sets</li> </ul> The .ov and .mat files contain data from the following runs: <table> <tbody> <tr> <th>Filename</th> <th>Word to be copy-spelled</th> <th>Number of flashes per row and column</th> <th>Number of electrodes used</th> <th>Feedback given to participant</th> </tr> <tr> <td>calibration-signal1</td> <td>THE</td> <td>12</td> <td>16</td> <td>no</td> </tr> <tr> <td>calibration-signal2</td> <td>QUICK</td> <td>12</td> <td>16</td> <td>no</td> </tr> <tr> <td>calibration-signals</td> <td>Concatenation of calibration-signal1 and calibration-signal2</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>run-[x]-raw-[y]</td> <td>DANCE</td> <td>x</td> <td>y</td> <td>yes</td> </tr> </tbody> </table>   This research is supported by the Irish Research Council under project ID GOIPG/2020/692 and Science Foundation Ireland under grant number 12/RC/2289_P2.

Many-Body Models for Chirality-Induced Spin Selectivity in Electron Transfer. Open data set

Data supporting the original figures 1, 2, 3 and 4 of the related publication.

opencc-by-4.0Oct 2024View details →

Age of stratospheric air: observational data sets (v2)

This dataset of mean stratospheric age of air is a compilation of data calculated from trace gas observations of CO2 and SF6 on Satellite, Aircraft and Balloon platforms.It provides 1) zonal mean monthly mean values from satellite data, 2) climatological data for different latitude bands from aircraft and balloon data, and 3) the time-series of mean age of air in the northern mid-latitude mid-stratosphere (updating Engel et al, 2009, 2017). Moreover, input data for the calculation of mean age of air from trace gases is provided (reference time series and ratio of moments).  New in Version 2: addition of climatology from aircraft data at 20 km, binned in 5° latitude bins. This data set is a supplement to Garny et al, 2024: "Age of stratospheric air: Progress on processes, observations and long-term trends" (Review of Geophysics, accepted). See Readme for more information.

opencc-by-4.0Jan 2024View details →

EPA and NR-DBIND data sets from the publication Sellami et al., 2024

<h3>The directory is protected by a password until the associated publication is accepted. The directory will be available as open access once the publication is accepted.</h3>

opencc-by-4.0Oct 2024View details →

Initial tractions data set for simulations presented in Figure 6

Initial tractions data set for simulations presented in Figure 6 in the paper with the title "3D Dynamic Rupture Simulations for the Expected Main Marmara Fault Earthquake in the Sea of Marmara Based on the Inter-seismic Strain Accumulation"

Coadsorption of ZnTPP and 2HMCTPP on Rutile TiO2(110) Data Set

Raw data and evaluation files to the publication

opencc-by-4.0Nov 2024View details →

Data set: Modeling of Magnesium Intercalation into Chevrel Phase Mo6S8: Report on improved cell design.

Dataset of the continuum simulations generated and used within the paper "Modeling of Magnesium Intercalation into Chevrel Phase Mo6S8: Report on improved cell design", published in Batteries & Supercaps  (2023, 6 (5), e202200562, DOI: 10.1002/batt.202200562). A good understanding of the limiting processes in rechargeable magnesium batteries is key to develop novel highcapacity/high-voltage cathode materials. Thereby, the performance of magnesiumion batteries can strongly depend on the morphology of the intercalation cathode. Moreover, high mass loadings are essential for commercialization. In this work the influence of different mass loadings are studied in addition to the impact of the particle size distribution of the active material. Therefore, a detailed continuum model is developed, which is able to describe the complex intercalation of magnesium into a Chevrel phase (CP) cathode. The model considers the thermodynamics, kinetics and interplay of the two energetically different intercalation sites of Mo6S8, which results from its unique crystal structure, as well as the impact of the desolvation on the electrochemical reactions and possible ion agglomeration. Ideal combinations of mass loading and electrolyte concentration as well as the desired CP particle size are determined for the state-of-the-art magnesium tetrakis(hexafluoroisopropyloxy)borate Mg[B(hfip)4]2 electrolyte.

opencc-by-4.0Dec 2023View details →

Data set and simulation code for "Static workspace computation for underactuated cable-driven parallel robots"

See the attached readme file