Find research datasets worth reusing

Data sets for heat generation and associated contact temperature during an oblique impact of a deformable particle and a rigid substrate

This dataset contains essential data from the Finite Element Method model predicting heat generation due to friction and plastic deformation during the oblique impact of a deformable particle and a rigid substrate. Part of this data was processed and published in a journal article (<a href="https://doi.org/10.1016/j.powtec.2023.118481">https://doi.org/10.1016/j.powtec.2023.118481</a>). The following is the description of the data files and the associated Figure in the original paper. ‘Heat_Elast_Vt.xlsx’ and ‘Temp_Elast_Vt.xlsx’ data for the evolution of heat and nodal contact temperature, respectively, for varying tangential velocity. Data was used in Figs. 7a and 7b in the associated paper ‘Heat_Vt.xlsx’ and Heat_Vn.xlsx’ data for the evolution of heat for various tangential velocities and normal velocities, respectively. Data was used in Figs. 8a and 8b in the associated paper. ‘Heat_YM.xlsx’ and ‘Temp_YM.xlsx’ data for the evolution of heat and nodal contact temperatures, respectively, for varying Young’s moduli. Data was used in Figs. 9 and 10 in the associated paper. ‘Heat_YS.xlsx’ and ‘Temp_YS.xlsx’ data for the evolution of heat and nodal contact temperatures for varying yield strengths. Data was used in Figs. 11 and 12 in the associated paper. ‘Heat_Den.xlsx’ and ‘Temp_Den.xlsx’ data for heat and nodal contact temperature evolution, respectively, for varying yield strengths. Data was used in Figs. 13 and 14 in the associated paper.  ‘Temp_TC.xlsx’ data for the evolution of nodal contact temperatures for varying thermal conductivities. Data was used in Fig. 15 in the associated paper. ‘Temp_HC.xlsx’ data for the evolution of nodal contact temperatures for varying specific heat capacities. Data was used in Fig. 16 in the associated paper.

CUT WP3 Data set

-Resonator frequency versus gate voltage. -Time-resolved spectrum of EF-Trons embedded in a resonator.

Data set to support the DSML measure

Two datasets for the factor analysis which has been conducted to develop the Diversity of Strategies for Motivation in Learning (DSML), as it has been published in the Behavioral Sciences Journal by Caroline Hands and Maria Limniou (2013).

Data Set For Revels-MD tutorials

This small data set contains the trajectory files necessary to run the tutorials for the revelsmd (<a href="https://github.com/user200000/revelsmd">https://github.com/user200000/revelsmd</a>) the trajectories were generated using lammps (<a href="https://www.lammps.org/#gsc.tab=0">https://www.lammps.org/#gsc.tab=0</a>) and gromacs (<a href="https://www.gromacs.org/">https://www.gromacs.org/</a>).  <ul> <li>Lennard jones sphere radial distribution functions (as in <a href="https://doi.org/10.1063/5.0053737">https://doi.org/10.1063/5.0053737</a>) (number 1)</li> <li>Solvation of an immobilised Lennard jones sphere in a solvent of identicle Lennard Jones spheres.(number 2)</li> <li>Solvation of a static water molecule (as in <a href="https://aip.scitation.org/doi/abs/10.1063/1.5111697">https://aip.scitation.org/doi/abs/10.1063/1.5111697</a>) (number 4)</li> </ul> A fourth tutorial is in development

opencc-by-4.0Feb 2021View details →

Pyrolysis Model Data Set Contribution for the MaCFP Workshop April 2021 - Dataset

This is a contribution of material parameters for pyolysis modelling, to the <a href="https://iafss.org/macfp/">MaCFP Workshop in April 2021</a>.   This repository contains the input files for the inverse modelling process (IMP), the data bases with the IMP results, the analysis scripts and the simulation data of the requested model predictions. Different approaches were followed and all their data is stored here. Only two of them were presented to the MaCFP Workshop and were labelled "Approach A" and "Approach B" in the submitted report. Note however, that the labelling of the conducted IMP's is different. <ul> <li>Approach A: R2_CAPAII_UMD_60_from_TGA_LCPP (Case A2)</li> <li>Approach B: R2_CAPAII_UMD_25_60_TGA_LCPP_2_5_15_20_range_rep_03 (Case B2)</li> </ul>   An intermediate naming convention was introduced as follows: <ul> <li>Case A1: TGA: R2_CAPAII_UMD_25_60_from_TGA_LCPP, CAPA II: R2_CAPAII_UMD_25_60_from_TGA_LCPP</li> <li>Case A2: TGA: R2_CAPAII_UMD_25_60_from_TGA_LCPP, CAPA II: R2_CAPAII_UMD_60_from_TGA_LCPP</li> <li>Case A3: TGA: R2_TGA_UMET_1_10_50_Methane_Scen_00_neu, CAPA II: R2_CAPAII_UMD_25_60_from_TGA_UMET</li> <li>Case A4: TGA: R2_TGA_UMET_1_10_50_Methane_Scen_00_neu, CAPA II: R2_CAPAII_UMD_60_from_TGA_UMET</li> <li>Case B1: TGA and CAPA II: R2_CAPAII_UMD_25_60_TGA_LCPP_2_5_15_20_range_rep_02</li> <li>Case B2: TGA and CAPA II: R2_CAPAII_UMD_25_60_TGA_LCPP_2_5_15_20_range_rep_03</li> <li>Case B3: TGA and CAPA II: R2_CAPAII_UMD_25_60_TGA_LCPP_2_5_15_20_rep_02</li> </ul> Note: The two archives mimic the directory structure of the project, where both are at the same lavel next to each other. If you would want to run the analysis scripts the relative file paths should work out of the box.   Version 1.1: Added description and README.   Version 1.2: Added archive containing the FDS input and output of approaches A and B directly (one does not need to download the full IMP_Runs21.rar), as well as the files contributed to the MaCFP-2 workshop.

Data set for the manuscript "Development of Hydropower and the Environmental Impacts of Hydroelectric Dam Construction: A Case Study of the Three Gorges Dam"

This document provides the data set supplementary to the manuscript "Development of Hydropower and the Environmental Impacts of Hydroelectric Dam Construction: A Case Study of the Three Gorges Dam" Data content: Tables 1-3 <ul> <li>Table 1. The global annual data of GDP, surface temperature anomalies, carbon dioxide emissions and different kinds of renewable energy during 2000-2020. The renewable energy includes hydropower, wind, solar, geothermal, biomass and others (unit: TWh).</li> <li>Table 2.  The annual mean temperature, CO2 emissions, and renewable energy data in China from 2000 to 2020. The renewable energy data include hydro, solar, and wind electricity generation (unit: kWh).</li> <li>Table 3. Annual variation of precipitation and biodiversity in the TGD area. Annual mean precipitation data over China and the TGD are provided. Biodiversity data include the number of Yangtze Finless Porpoise, Carp Egg and Larvae, and the number of spawners in spawning ground for Acipenser_sinensis.</li> </ul>

Indoor UWB CIR Data Set for Material Prediction

ABOUT This data set contains spatially distributed CIR of multipath components in indoor environment acquired with ultra wideband (UWB) radio technology in microwave frequency range. The data is labeled with the materials of the surfaces bounding the space (floor, ceiling, walls). The data was collected for training and evaluating machine learning models for CIR-based indoor material prediction, but it may be also used for other studies based on indoor radio propagation data.    AUTHORS Teodora Kocevska, Andrej Hrovat, Tomaž Javornik Department of Communication Systems Jožef Stefan Institute, SI-1000 Ljubljana, Slovenia   DATA COLLECTION The synthetic data is obtained using <a href="https://www.remcom.com/wireless-insite-em-propagation-software">Remcom Wireless InSite</a> v.3.3.3. The CIR is estimated in 16,875 rooms in total.  These rooms belong to 5,625 distinct room types and each room type is considered in three sizes.  The number of distinct room types comes from the materials used for the floor, ceiling, and walls, having nine floor-ceiling material combinations and 625 wall-material combinations.   <table align="center"> <caption>Room Sizes</caption> <thead> <tr> <th scope="col"> ROOM SIZE</th> <th scope="col">FLOOR/CEILING DIMENSIONS </th> <th scope="col">WALL DIMENSIONS</th> </tr> </thead> <tbody> <tr> <td>S</td> <td>3 m x 3 m</td> <td>3 m x 3 m  </td> </tr> <tr> <td>M</td> <td>5 m x 5 m</td> <td>5 m x 3 m </td> </tr> <tr> <td>L</td> <td>7 m x 7 m </td> <td>7 m x 3 m </td> </tr> </tbody> </table>   MATERIALS <ul> <li> floor: concrete, wood, floorboard</li> <li> ceiling: concrete, plaster, wood</li> <li> walls: brick, concrete, glass, plaster, wood</li> </ul>   <table align="center"> <caption>Electrical properties of materials*</caption> <thead> <tr> <th scope="col">MATERIAL</th> <th scope="col">RELATIVE PERMITTIVITY</th> <th scope="col">CONDUCTIVITY</th> </tr> </thead> <tbody> <tr> <td>brick</td> <td>3.75</td> <td>0.038</td> </tr> <tr> <td>concrete</td> <td>5.31</td> <td>0.120</td> </tr> <tr> <td>glass</td> <td>6.27</td> <td>0.029</td> </tr> <tr> <td>plaster</td> <td>2.94</td> <td>0.036</td> </tr> <tr> <td>wood</td> <td>1.99</td> <td>0.026</td> </tr> <tr> <td>floorboard</td> <td>3.66</td> <td>0.039</td> </tr> </tbody> </table>   COMMUNICATION SYSTEM CONFIGURATION Ultra wideband (UWB) radio technology is considered. The parameters of the communication system are set according to 802.15.4-2011** standard. The configuration of system parameters is summarized as follows: <table align="center"> <caption>System configuration</caption> <thead> <tr> <th scope="col"> PARAMETER</th> <th scope="col">CONFIGURATION</th> </tr> </thead> <tbody> <tr> <td>frequency</td> <td>3494.4 MHz</td> </tr> <tr> <td>bandwidth</td> <td>466.2 MHz</td> </tr> <tr> <td>Tx/Rx height</td> <td>1.5 m</td> </tr> <tr> <td>antenna type</td> <td>omni</td> </tr> <tr> <td>polarization</td> <td>vertical</td> </tr> </tbody> </table>   RADIO NODE POSITIONS The data is collected using three acquisition layouts as follows: 1. Layout 1    Tx in the center of the room and Rx moved over uniform grid covering the room. 2. Layout 2 Tx in eight positions following circular pattern around the center of the room and Rx moved over uniform grid covering the room.  The distance from the center of the room to the circumference of the circle is 0.5 m, and the spacing between the radio nodes is pi/4 rad. 3. Layout 3 Tx in four positions near the corners of the room (0.375 m from the walls) and Rx moved over uniform grid covering the room. The corners of the grid are 0.25 m apart from the walls and the distance between the nodes is also 0.25 m. Since the grid size is defined relatively to the room size, the total number of grid node positions is different in rooms with different sizes. The total number of grid node positions is 121, 361, and 729 in S, M, and L rooms, respectively.   DATA ORGANIZATION Data is saved in .csv files. Each file starts with a header line specifying the column names.  The column names included in the .csv files are: - Column 0: layout {center, circle, corners} - Column 1: tx_point_id {1} for Layout 1, {1-8} for Layout 2, and {1-4} for Layout 3 - Column 2: rx_point_id {1-121} in S-rooms, {1-361} in M-rooms, and {1-729} in L-rooms - Column 3: 1_phase_deg NUMERIC - Column 4: 1_toa_ns NUMERIC - Column 5: 1_power_dbm NUMERIC - Column 6: 1_power_nw NUMERIC - Column 7: 2_phase_deg NUMERIC - Column 8: 2_toa_ns NUMERIC - Column 9: 2_power_dbm NUMERIC - Column 10: 2_power_nw NUMERIC - Column 11: 3_phase_deg NUMERIC - Column 12: 3_toa_ns NUMERIC - Column 13: 3_power_dbm NUMERIC - Column 14: 3_power_nw NUMERIC - Column 15: 4_phase_deg NUMERIC - Column 16: 4_toa_ns NUMERIC - Column 17: 4_power_dbm NUMERIC - Column 18: 4_power_nw NUMERIC - Column 19: 5_phase_deg NUMERIC - Column 20: 5_toa_ns NUMERIC - Column 21: 5_power_dbm NUMERIC - Column 22: 5_power_nw NUMERIC - Column 23: 6_phase_deg NUMERIC - Column 24: 6_toa_ns NUMERIC - Column 25: 6_power_dbm NUMERIC - Column 26: 6_power_nw NUMERIC - Column 27: 7_phase_deg NUMERIC - Column 28: 7_toa_ns NUMERIC - Column 29: 7_power_dbm NUMERIC - Column 30: 7_power_nw NUMERIC - Column 31: 8_phase_deg NUMERIC - Column 32: 8_toa_ns NUMERIC - Column 33: 8_power_dbm NUMERIC - Column 34: 8_power_nw NUMERIC - Column 35: 9_phase_deg NUMERIC - Column 36: 9_toa_ns NUMERIC - Column 37: 9_power_dbm NUMERIC - Column 38: 9_power_nw NUMERIC - Column 39: 10_phase_deg NUMERIC - Column 40: 10_toa_ns NUMERIC - Column 41: 10_power_dbm NUMERIC - Column 42: 10_power_nw NUMERIC - Column 43: 11_phase_deg NUMERIC - Column 44: 11_toa_ns NUMERIC - Column 45: 11_power_dbm NUMERIC - Column 46: 11_power_nw NUMERIC - Column 47: 12_phase_deg NUMERIC - Column 48: 12_toa_ns NUMERIC - Column 49: 12_power_dbm NUMERIC - Column 50: 12_power_nw NUMERIC - Column 51: 13_phase_deg NUMERIC - Column 52: 13_toa_ns NUMERIC - Column 53: 13_power_dbm NUMERIC - Column 54: 13_power_nw NUMERIC - Column 55: 14_phase_deg NUMERIC - Column 56: 14_toa_ns NUMERIC - Column 57: 14_power_dbm NUMERIC - Column 58: 14_power_nw NUMERIC - Column 59: 15_phase_deg NUMERIC - Column 60: 15_toa_ns NUMERIC - Column 61: 15_power_dbm NUMERIC - Column 62: 15_power_nw NUMERIC - Column 63: room_size_surf_m2 {9} for S-rooms, {25} for M-rooms, and {49} for L-rooms - Column 64: room_size_name {S} for S-rooms, {M} for M-rooms, and {L} for L-rooms - Column 65: room_shape {square} - Column 66: floor_mat {concrete, wood, floorboard} - Column 67: ceiling_mat {concrete, plaster, wood} - Column 68: wall1_mat {brick, concrete, glass, plaster, wood} - Column 69: wall2_mat {brick, concrete, glass, plaster, wood} - Column 70: wall3_mat {brick, concrete, glass, plaster, wood} - Column 71: wall4_mat {brick, concrete, glass, plaster, wood} Each row corresponds to separate radio link defined with the Tx and Rx nodes.  It includes information about      (i)  the CIR-acquisition layout (position of the Txs and Rxs),      (ii)  the Tx and Rx,      (iii)  the CIR of 15 strongest multipath components,      (iv)  the room geometry, and      (v)  the materials of the surfaces bounding the space.  - Column 0 specifies the layout.      The following maping is used:          Layout 1 -> center,          Layout 2 -> circle, and          Layout 3 -> corners. - Column 1 specifies the Tx identifier. - Column 2 specifies the Rx identifier. - Columns 3-62 are the input attributes.      The input attributest represent the phase (in deg), ToA (in ns), received power (in dBm), and received power (in nW) for 15 strongest multipath components.      The column naming is X_Y_Z, where X is the multipath component identifier (1 to 15), Y is the propagation characteristic (phase, toa, or power), and Z is the unit (deg, ns, dbm, or nw). - Column 63 specifies the surface of the room in square meters. - Column 64 specifies the room size category (S, M, or L). - Column 65 specifies the room-base shape. - Columns 66-71 are the target attributes specifying the material of the floor, ceiling, wall 1, wall 2, wall 3, and wall 4, respectively.    FOLDER STRUCTURE The folder indoor_CIR_data contains:     - one subfolder named CIR_data         It contains three .csv files with CIR data named by the size of the rooms where the data is acquired.         For naming the .csv files the following mapping is considered:             - Small.csv  -> S-rooms             - Medium.csv -> M-rooms             - Large.csv  -> L-rooms     - one subfolder named CIR_acquisition_details         It contains .png file with schematic representation of the radio node positions considered for obtaining the data.     - README.txt file The folder structure is:     - CIR_data         - Small.csv         - Medium.csv         - Large.csv     - CIR_acquisition_details         - radio_node_positions.png     - README.txt   REFERENCES * R. sector of International Telecommunication Union (ITU-R), “Effects of building materials and structures on radio wave propagation above about 100 MHz,” International Telecommunication Union, ITU-R Recommendation P.2040-2, 2021. ** IEEE, “Standard for local and metropolitan area networks–Part 15.4: Low-rate wireless personal area networks (LR-WPANs),” IEEE, Standard IEEE 802.15.4-2011, 2011.

Human CD34 bone marrow SCE data set to reproduce Totem protocols

The data set <code>human_cd34_bm_rep1.rds</code> was parsed with the R script <code>download_h5ad_to_SCE_rds_script.R</code> (see github repository <a href="https://github.com/elolab/Totem-protocol">elolab/Totem-protocol</a>). It is a parsed <code>SingleCellExperiment</code> <code>RDS</code> object corresponding to the anndata h5ad <code>human_cd34_bm_rep1.h5ad</code> available on <a href="https://github.com/elolab/Totem-protocol/blob/main">HCA Portal</a> and published by <a href="https://www.nature.com/articles/s41587-019-0068-4">Setty et al., 2019</a>.

Kinase ChemoGenomic Set (KCGS) v 2.0 data set

Here we briefly describe the latest iteration of our kinase chemogenomic set, progressing toward eventual total kinome coverage. This new edition is called KCGS2.0. Our kinase chemogenomic set (KCGS) comprises well-annotated inhibitors that target kinases with potent activity but have what we consider narrow-spectrum activity across the kinome. Our goal is to continue growing the set until we have one to three inhibitors for each human kinase. When we reach this point, the set can in principle, be used to determine the relevance and/or function of each kinase in the context of interest. Individually each inhibitor is not promiscuous, and each has defined activity on a narrow set of kinases. When the set is screened in disease-relevant phenotypic assays, one can infer kinase vulnerability based on the results and follow up with more detailed experiments on kinases of interest to confirm the hypothesized dependence. We have now added additional compounds to KCGS1.0 and created KCGS2.0 affording expanded breadth (more kinases covered) and depth (additional chemotypes for kinase) of coverage. The set is being distributed through cancertools.org, Cancer Research UK's research tools arm. Follow this link (https://www.cancertools.org/tools ) and search for KCGS at this tools page. Frequently Asked Questions In the summary spreadsheet, what does the S10 (1 mM) mean? S10 (1 mM) is a selectivity metric generated from Discoverx broad kinome screening data. It is the number of kinases with PoC<10 (equivalent to 90%I) divided by the number of wild type (non-mutant) kinases screened (generally 403 kinases here). In our case we screened inhibitors at a concentration of 1 micromolar, thus, the S10 (1 mM). Smaller S10 values represent a more selective compound. Of course, this is an imperfect selectivity measure. Do you have the same data on all the compounds? We don’t. Compounds that ended up in KCGS2.0 but started in PKIS may only have data from the Nanosyn panel of assays we ran at that time. In that PKIS experiment we screened compounds at 100 nM and 1 micromolar. In the spreadsheet of KCGS2.0 summary data, any reference to Nanosyn is talking about the data from the 1 micromolar screening at Nanosyn. Please check out the PKIS paper and supplemental information for more information on the Nanosyn data. Here is the pubmed link: <a href="https://pubmed.ncbi.nlm.nih.gov/26501955/">https://pubmed.ncbi.nlm.nih.gov/26501955/</a> Compounds from PKIS2 that ended up in KCGS2.0 have broad screening data from the Discoverx panel of assays. The work around KCGS is described here: <a href="https://pubmed.ncbi.nlm.nih.gov/33429995/">https://pubmed.ncbi.nlm.nih.gov/33429995/</a>. Please refer to this paper for more detail on the design of KCGS and the use of KCGS. These same guidelines were used in expanding to KCGS2.0; so many of your KCGS2.0 questions may be answered by reading through that paper. The brand-new compound additions that turn KCGS into KCGS2.0 comes with new, and for the most part unpublished, Discoverx kinome scan data. We have added compounds that cover new kinases (increased breadth of coverage) as well as adding new chemotypes for some kinases (increased depth of coverage). Are all the compounds exquisitely selective? Initially we strived for S10 (1 mM) < 0.03 or so. Many of the compounds only had Nanosyn data initially. We have now tested many of those in the kinomescan assay, and that is reflected in the screening column (KCGS2.0 data overview spreadsheet) if it says “Nanosyn, Discoverx”. These two assay panels are different (but with many overlapping kinases) and completely different assay formats. In some cases, testing in the Discoverx panel has surfaced additional kinase targets, meaning that compounds with less-than-ideal selectivity are in the set. This just means users of the set need to take this into account as hits from phenotypic screens are followed up. What are the references you provide in the “reference” column? When we started building kinase chemogenomic sets and designing new kinase inhibitors, our premise was that we could use kinase inhibitors made for one target as starting points for other kinase targets. The "reference" column provides the original med chem references that report a number of these compounds. If one of these compounds hits in your assay, I encourage you to check out the original paper in case it offers any additional insights. Apologies if we have missed some references. This exercise has demonstrated that useful inhibitors for "other", often unrelated, kinases can be identified by broad screening of compounds made in medicinal campaigns for another kinase. If I get a hit from compound do I know with certainty that the target is critical for my phenotype? Screening the set will generate hypotheses for you to explore. Any hit in a phenotypic assay needs to be followed up carefully. Of course, looking at the list of targets in row I (target data: generally, Kd<100 nM and/or %I>90 (screened at 1 mM)) is a great place to start. Remember there COULD be other targets. We have not screened all kinases, for example. In addition, S10 (1 mM) is an imperfect selectivity metric. We have highlighted targets with Kd or IC50 < 100 nM, or with >90%I at 1 uM. Targets just slightly weaker than this could also lead to (or contribute to) a phenoytpe. Of course, there may be a chance a compound binds to a nonkinase target. For hits of interest, ALL possibilities should be considered as you seek to link compound to target to mechanism and phenotype. ---------------------------------------------- For additional information and to leave feedback, click here: <a href="https://openlabnotebooks.org/release-of-the-kinase-chemogenomic-set-2-0-kcgs2-0/">openlabnotebooks</a> ----------------------------------------------

Data set from "A free-space interferometer design for optical frequency dissemination and out-of-loop characterization below the 10^{-21}-level"

The data set contains the data underlying the improved out-of-loop interferometer layout performance evaluation published in Photonics Research (<a href="https://doi.org/10.1364/PRJ.485899">https://doi.org/10.1364/PRJ.485899</a>). The experimental setup and the methodology used is explained in that publication. The data is stored in the Matlab(R)-native file format. This proprietary file format is also readable by other numerical computing environments. The files 'data_i_*_S*.mat' contain the timeseries of the analysed continuous measurement runs in configuration S*. Each of these files includes the following variables: year, month, day, hour, minute, second: date at which the measurment point was acquired rem_float, rem: observed 1s Lambda-averaged out-of-loop frequency deviation in Hz as float and string, respectively p: measured air pressure in hPa T: measured laboratory temperature inside the cover close to the interferometer in °C pressure_phase_OOL: phase variations of the out-of-loop signal estimated from the measured pressure variations temp_phase_OOL: phase variations of the out-of-loop signal estimated from the measured temperature variations   Different barometers have been used for the pressure mesaurements. In the measurement runs for the S1 and S2PM configurations, a barometer placed in a neighboring laboratory in the same building at PTB was used to characterize pressure fluctuations. For the measurement in the S2 configuration, we used air pressure data from the climate station of the department of Hydrology and River Basin Management of the Technical University Braunschweig, which is ≈6km apart. At times of overlapping operation, we have observed matching pressure instabilities of both barometers for averaging times 𝜏>1000s, which shows that on these averaging times the exact placement of the barometer is of lesser importance.

Point clouds data "Waterfall height sets the mechanism and rate of upstream retreat"

Point clouds data on the erosion shape of waterfalls in our experiment in the paper "Waterfall height sets the mechanism and rate of upstream retreat".

Set of Data from "Selective targeting of striatal parvalbumin-expressing interneurons for transgene delivery" https://doi.org/10.1016/j.jneumeth.2021.109105

PVCre (+/+) mice were injected in the right striatum with AAV2/1-hsyn-FLEX-eGFP AAV2/9-CBA-FLEX-eGFP  or AAV2/9-hsyn-FLEX-eGFP vectors. Two weeks after, cells were manually counted by a blind observer on 2D maximal intensity projections of confocal images transformed into the tiff format using the Multi-point tool in ImageJ. The GraphPad .pzfx files show the quantitative anaylsis of the proportion of GFP+ cells which were PV+ (specificty) , the proportion of PV+ cells which were GFP+ (efficiency), the proportion of PV+ cells in the injected striatum relative to to the injected striatum of PVCre (+/+) mice (absence of vector toxicity) and the proportion of PV+ cells in the striatum  of PVCre (+/+) mice versus wt mice.

Hurricane Disturbance Vegetation Anomaly (HDVA) [Data set for Turner et al.]

The Hurricane Disturbance Vegetation Anomaly (HDVA) is a rapid assessment approach to understand the severity of ecological damage from a high intensity storm event on an otherwise healthy, mature mangrove forest. Data archived here focuses on Cuba, where Hurricane Irma (Category 5) hit the northern coast in September 2017 and caused wide-spread damage to mangroves and coastal forests. Local scientists were not able to assess the full extent or severity of damages through field work due to limited infrastructure and resources, so they turned to remote sensing analysis. We developed a multitemporal, multiresolution approach to assess the damage 1) extent and 2) relative severity using changes from the typical green-leaf phenology represented by the Enhanced Vegetation Index with MODIS and Sentinel-2 data. All data was processed in Google Earth Engine API to access and utilize large amounts of historical data, as well as compare sensors’ spatial resolution impacts on results. This data set includes the HDVA products and categorization of data by quartile of damage (catastrophic, severe, moderate, mild, no loss).

opencc-by-nc-4.0Jan 2023View details →

Data of "Pen mates' interactions, potential precursors of damaging behaviours, object manipulation, straw rooting, and primary activity: A detailed data set in undocked pigs under dietary protein restriction"

Damaging behaviours, such as tail biting, are common problems in pig production, compromising animal welfare and causing economic losses. Detailed studies are impeded by the difficulty of directly observing these behaviours. Tail biting is a broader phenomenon that begins long before lesions manifest, and behavioural problems caused by various stressors present themselves weeks before they escalate to damaging behaviour, resulting in serious injuries. Therefore, detailed data on behaviours, which can be considered precursors of tail biting, such as oral and nasal manipulation of conspecifics, should be collected. The present data were collected in the course of a large study on the genetic potential of protein efficiency, in which the crude protein content in the diet was reduced to 80% of the recommendations. Dietary protein reduction is a promising way to reduce nitrogen emissions in pig manure, but its implications for animal welfare are not yet clear. Pigs differ phenotypically and genetically in their ability to utilise dietary proteins; therefore, there might be individual differences in how they cope with the protein reduction. Here, we present detailed data of focal observations of 95 pigs at an experimental farm with undocked tails that were fed a protein-reduced diet. Pigs were observed directly in their home pens for 5 min each on four different days. All actions directed towards objects in the pen, interactions with and confrontations among pen mates, and straw rooting behaviour and general activity were recorded. After the behavioural observations, wounds on different parts of the body and the cleanliness of the pigs were noted. The protein efficiency of 94 pigs was obtained. The data set comprises six tables. The first table contains information on the animals, including the identities of their parents, farrowing group, sex, and protein efficiency. The other data tables contain four 5-min observations of each pig on 10 object-manipulation behaviours; 150 interaction behaviours, including reactions; 14 confrontation behaviours and their outcomes and reactions; 10 mounting behaviours, including reactions; two rooting behaviours; seven basic behaviours; and an index of general activity. The observations took place under comparatively good housing conditions. Pigs were not tail-docked and were given fresh straw daily, ad libitum access to feed, floor space above the legal requirements (only a partially slatted floor), and daily cleaning of pens, and they were closely monitored for signs of damaging behaviour; all of these are favourable conditions as they limit stress and the risk of damaging behaviour. These data can be used to further explore the relationships of specific behaviours and phenomena and their association with protein efficiency. The ethogram can be used as a template for further observations. Practitioners could use the data to support pigs’ need for occupation, such as by providing sufficient straw.

SECURES-Met - A European wide meteorological data set suitable for electricity modelling (supply and demand) for historical climate and climate change projections

For the modelling of electricity production and demand, meteorological conditions are becoming more relevant due to the increasing contribution from renewable electricity production. But the requirements on meteorological data sets for electricity modelling are quite high. One challenge is the high temporal resolution, since a typical time step for modelling electricity production and demand is one hour. On the other side the European electricity market is highly connected, so that a pure country based modelling does not make sense and at least the whole European Union area has to be considered. Additionally, the spatial resolution of the data set must be able to represent the thermal conditions, which requires high spatial resolution at least in mountainous regions. All these requirements lead to huge data amounts for historic observations and even more for climate change projections for the whole 21st century. Thus, we have developed an aggregated European wide data set that has a temporal resolution of one hour, covers the whole EU area, has a reasonable size but is considering the high spatial variability. This meteorological data set for Europe for the historical period and climate change projections fulfills all relevant criteria for energy modelling. It has a hourly temporal resolution, considers local effects up to a spatial resolution of 1 km and has a suitable size, as all variables are aggregated to NUTS regions. Additionally meteorological information from wind speed and river run-off is directly converted into power productions, using state of the art methods and the current information on the location of power plants. Within the research project SECURES (https://www.secures.at/) this data set has been widely used for energy modelling.   The SECURES-Met dataset provides variables visible in the table. <table> <tbody><tr> <th>Variable</th> <th>Short name</th> <th>Unit</th> <th>Aggregation methods</th> <th>Temporal resolution</th> </tr> </tbody><tbody> <tr> <th>Temperature (2m)</th> <td>T2M</td> <td> °C °C </td> <td> spatial mean population weighted mean (recommended) </td> <td>hourly</td> </tr> <tr> <th>Radiation</th> <td> GLO (mean global radiation) BNI (direct normal irradiation) </td> <td> Wm-2 Wm-2 </td> <td> spatial mean population weighted mean (recommended) </td> <td>hourly</td> </tr> <tr> <th>Potential Wind Power </th> <td>WP</td> <td>1</td> <td>normalized with potentially available area</td> <td>hourly</td> </tr> <tr> <th>Hydro Power Potential</th> <td> HYD-RES (reservoir) HYD-ROR (run-of-river) </td> <td> MW 1 </td> <td> summed power production summed power production normalized with average daily production </td> <td>daily</td> </tr> </tbody> </table>                           SECURES-Met is available in a tabular csv format for the historical period (1981-2020, Hydro only until 2010) created from ERA5 and ERA5-Land and two future emission scenarios (RCP 4.5 and RCP 8.5, both 1951-2100, wind power starting from 1981, hydro power from 1971) created from one CMIP5 EUROCORDEX model (GCM:  ICHEC-EC-EARTH, RCM: KNMI-RACMO22E, ensemble run: r12i1p1) on the spatial aggregation level <ul> <li>NUTS0 (country-wide),</li> <li>NUTS2 (province-wide),</li> <li>NUTS3 (Austria only),</li> <li>and EEZ (Exclusive Economic Zones, offshore only).</li> </ul> The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized,  and a folder (Meta.zip), which has information and shape files of the different NUTS levels. As population weighted temperature and radiation represent values in geographical areas more relevant for solar power, it is highly relevant to use population weighted files. Spatial mean should be used for reference only. The project SECURES, in which this dataset was produced, was funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.

Data sets for temperature rise due to frictional heat generation during a sliding contact between an elastic particle and a rigid substrate

This dataset contains essential data from the Finite Element Method model predicting heat generation due to friction during the sliding contact between an elastic particle and a rigid substrate. Part of this data was processed and presented in an article under review for journal publication. The following is the description of the data files and the associated Figure in the original paper. 'Temp_CoeffFric_01_055.xlsx' data for temperature evolution for the nodes in the contacts and along the particle radius for various friction coefficient values (0.1-0.55). 'Temp_Load_001_01.xlsx' data for temperature evolution for the nodes in the contacts and along the particle radius for various normal load values (0.01-0.1 N). 'Temp_Vel_02_1.xlsx' data for temperature evolution for the nodes in the contacts and along the particle radius for various sliding velocity values (0.2-1 m/s). 'Temp_TC_5_100.xlsx' data for temperature evolution for the nodes in the contacts and along the particle radius for various thermal conductivity values. (i.e. 5-100 W/m K). 'Temp_HC_100_1600.xlsx' data for temperature evolution for the nodes in the contacts and along the particle radius for various thermal conductivity values. (i.e. 100-1600 J/kg K).

Understanding unconventional magnetic order in a candidate axion insulator by resonant elastic x-ray scattering - Data set

This data set includes the resonant elastic xray scattering (REXS) data collected at the I16 (Diamond Light Source, United Kingdom) and P09 (DESY, Germany) beamline, along with the magnetization data. I16 Data - Temperature dependence of the L=15 reflection - Temperature dependence of the L=14.333 reflection - 00L dependence (6K - 20K) - Azimuthal dependence of L=15 reflection - Azimuthal dependence of L=13.667 reflection P09 Data - Field dependence of L=15 reflection (pi-pi channel) - Field dependence of L=15 reflection (pi-sigma channel) - Field dependence of L=14.333 reflection (pi-pi channel) - Field dependence of L=14.333 reflection (pi-sigma channel) Magnetization Data - Field Dependence of the Magnetization

Neural net training and test data set

In this zip file you will find several folders as well as a readme file explaining the dataset. The neural net file can be directly used in cellpose to segment widefield 20x objective imaging data of neutrophils. The neural net could be used for other types of data, but might perform below expectations.

Short communication: Synchrotron-based elemental mapping of single grains to investigate variable infrared-radiofluorescence emissions [Data set]

This dataset accompanies a research paper in the journal Geochronology (<a href="https://doi.org/10.5194/gchron-6-77-2024">https://doi.org/10.5194/gchron-6-77-2024</a>). It contains the raw output of micro-XRF measurements carried out at the 5-ID SRX beamline at the National Synchrotron Light Source II (NSLS-II) at Brookhaven National Laboratory, USA, on coarse K-feldspar grains. Additionally, the XRF intensity attributed to each element after spectral fitting is provided for elemental mapping. The dataset also includes the output from scanning electron microscope energy-dispersive X-ray spectroscopy (SEM-EDS) measurements on coarse K-feldspar grains of two samples taken at Archéosciences Bordeaux, France.