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654 results for “Exhaustion”
Data associated to "The Direct Cost of Contaminated Brownfield Sites on Real Estate in France: A Quasi-Exhaustive Hedonic Price Analysis"
<p>Data for replication of main results in "The Direct Cost of Contaminated Brownfield Sites on Real Estate in France: A Quasi-Exhaustive Hedonic Price Analysis". The folder "data_estim" contains all necessary data to replicate all estimations in the article (see the R code "codes_cbs-cost") with three .csv files: dvf_estim.csv, dvfbasol_estim.csv and cell200_simulation.csv. The variable names in these files are as follow:</p><p> </p><p>Identifier Variables:</p><p>- IDMUTATION: identifier for each transacted property</p><p>- comm_code: identifier for each commune defined in 2021</p><p>- admin_code: identifier for urban areas defined in 2021</p><p>- iris2014_code: identifier for each neighborhood defined in 2014</p><p>- cell200_code: identifier for each 200-meters gredded cells</p><p>- dvf_x: longitude of each transacted property (EPSG: 2154, Lambert-93, RGF93)</p><p>- dvf_y: latitude of each transacted property (EPSG: 2154, Lambert-93, RGF93)</p><p>- basol_code: identifier for each CBS (only reported in dvfbasol_estim.csv)</p><p>- anneemut: year of transaction for each property</p><p> </p><p>Dependent Variable:</p><p>- pm2: price in euro per square meter of transacted properties</p><p> </p><p>Interest Variables:</p><p>- areaha_basol250: area in hectare of CBS between 0 and 250 meters from transacted property</p><p>- areaha_basol500: area in hectare of CBS between 250 and 500 meters from transacted property</p><p>- areaha_basol1000: area in hectare of CBS between 500 and 1000 meters from transacted property</p><p>- areaha_basol2000: area in hectare of CBS between 1000 and 2000 meters from transacted property</p><p>- areaha_basol3000: area in hectare of CBS between 2000 and 3000 meters from transacted property</p><p>- area250_indpro: area in hectare of CBS with industrial manufacturing activities between 0 and 250 meters from transacted property</p><p>- area500_indpro: area in hectare of CBS with industrial manufacturing activities between 250 and 500 meters from transacted property</p><p>- area1000_indpro: area in hectare of CBS with industrial manufacturing activities between 500 and 1000 meters from transacted property</p><p>- area2000_indpro: area in hectare of CBS with industrial manufacturing activities between 1000 and 2000 meters from transacted property</p><p>- area3000_indpro: area in hectare of CBS with industrial manufacturing activities between 2000 and 3000 meters from transacted property</p><p>- area250_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 0 and 250 meters from transacted property</p><p>- area500_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 250 and 500 meters from transacted property</p><p>- area1000_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 500 and 1000 meters from transacted property</p><p>- area2000_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 1000 and 2000 meters from transacted property</p><p>- area3000_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 2000 and 3000 meters from transacted property</p><p>- area250_othact: area in hectare of CBS with other or unknown activities between 0 and 250 meters from transacted property</p><p>- area500_othact: area in hectare of CBS with other or unknown activities between 250 and 500 meters from transacted property</p><p>- area1000_othact: area in hectare of CBS with other or unknown activities between 500 and 1000 meters from transacted property</p><p>- area2000_othact: area in hectare of CBS with other or unknown activities between 1000 and 2000 meters from transacted property</p><p>- area3000_othact: area in hectare of CBS with other or unknown activities between 2000 and 3000 meters from transacted property</p><p>- areaha_specific250: area in hectare of CBS specific to a unique CBS between 0 and 250 meters from transacted property (only reported in dvfbasol_estim.csv)</p><p>- areaha_specific500: area in hectare of CBS specific to a unique CBS between 250 and 500 meters from transacted property (only reported in dvfbasol_estim.csv)</p><p>- areaha_specific1000: area in hectare of CBS specific to a unique CBS between 500 and 1000 meters from transacted property (only reported in dvfbasol_estim.csv)</p><p>- areaha_specific2000: area in hectare of CBS specific to a unique CBS between 1000 and 2000 meters from transacted property (only reported in dvfbasol_estim.csv)</p><p> </p><p>Robustness Variables:</p><p>- pm2mean_iris: average transaction price per square meter of neighborhood IRIS</p><p>- shpoorhouse: share in percentage of poor households </p><p>- dvfschool_nb250: number of schools within 250 meters of property</p><p>- dvfschool_nb500: number of schools within 500 meters of property</p><p>- dvfschool_nb1000: number of schools within 1000 meters of property</p><p>- dvfschool_nb2000: number of schools within 2000 meters of property</p><p>- dvfschool_nb3000: number of schools within 3000 meters of property</p><p>- dvfroad_nb250: number of road connections within 250 meters of property</p><p>- dvfroad_nb500: number of road connections within 500 meters of property</p><p>- dvfroad_nb1000: number of road connections within 1000 meters of property</p><p>- dvfroad_nb2000: number of road connections within 2000 meters of property</p><p>- dvfroad_nb3000: number of road connections within 30000 meters of property</p><p>- dvfrail_nb250: number of railway stations within 250 meters of property</p><p>- dvfrail_nb500: number of railway stations within 500 meters of property</p><p>- dvfrail_nb1000: number of railway stations within 1000 meters of property</p><p>- dvfrail_nb2000: number of railway stations within 2000 meters of property</p><p>- dvfrail_nb3000: number of railway stations within 3000 meters of property</p><p> </p><p>Control Variables:</p><p>- center_dist: distance in kilometers of transacted property from urban area center</p><p>- sterr: surface area in square meter of parcel of each property</p><p>- sbati: surface area in square meter of building surfaces</p><p>- vente_cla: transaction through a classical process (binary variable)</p><p>- vente_adj: transaction through adjudicated process (binary variable)</p><p>- vente_ech: transaction through special exchange process (binary variable)</p><p>- vente_exp: transaction through expropriation process (binary variable)</p><p>- vente_efa: transaction before completion (binary variable)</p><p>- nblocmai: number of houses in each transaction</p><p>- nblocapt: number of apartments in each transaction</p><p>- nblocdep: number of building dependencies in each transaction</p><p>- nblocact: number of properties for commercial purpose in each transaction</p><p>- nbapt1pp: number of apartment with 1 room in each transaction</p><p>- nbapt2pp: number of apartment with 2 rooms in each transaction</p><p>- nbapt3pp: number of apartment with 3 rooms in each transaction</p><p>- nbapt4pp: number of apartment with 4 rooms in each transaction</p><p>- nbapt5pp: number of apartment with 5 and more rooms in each transaction</p><p>- nbmai1pp: number of house with 1 room in each transaction</p><p>- nbmai2pp: number of house with 2 rooms in each transaction</p><p>- nbmai3pp: number of house with 3 rooms in each transaction</p><p>- nbmai4pp: number of house with 4 rooms in each transaction</p><p>- nbmai5pp: number of house with 5 and more rooms in each transaction</p><p>- pm2mean_comm: average transaction price in euro per square meter of commune</p><p>- dvfmonument_nb500: number of historical monuments between 0 and 500 meters from transacted property</p><p>- dvfmonument_nb1000: number of historical monuments between 500 and 1000 meters from transacted property</p><p>- dvfmonument_nb2000: number of historical monuments between 1000 and 2000 meters from transacted property</p><p>- dvfindus_nb500: number of active industrial sites between 0 and 500 meters from transacted property</p><p>- dvfindus_nb1000: number of active industrial sites between 500 and 1000 meters from transacted property</p><p>- dvfindus_nb2000: number of active industrial sites between 1000 and 2000 meters from transacted property</p><p>- sh_apt: share of apartments in neighborhood IRIS</p><p>- sh_1945: share in percentage of properties with a building age before 1945</p><p>- sh_1970: share in percentage of properties with a building age before 1970</p><p>- sh_1990: share in percentage of properties with a building age before 1990</p><p>- sh_ap90: share in percentage of properties with a building age between 1990 and 2015</p><p>- sh_2015: share in percentage of properties with a building age after 2015</p><p>- clc1000_urbanhousing: share in percentage of land within 1000 meters of transacted properties with housing</p><p>- clc1000_urbanpark: share in percentage of land within 1000 meters of transacted properties with urban parks</p><p>- clc1000_recreation: share in percentage of land within 1000 meters of transacted properties with recreative activities</p><p>- clc1000_industrial: share in percentage of land within 1000 meters of transacted properties with industrial activities</p><p>- clc1000_transport: share in percentage of land within 1000 meters of transacted properties with transport infrastructures</p><p>- clc1000_nature: share in percentage of land within 1000 meters of transacted properties with natural land use</p><p>- clc1000_agr: share in percentage of land within 1000 meters of transacted properties with agricultural land use</p><p>- clc1000_forest: share in percentage of land within 1000 meters of transacted properties with forest</p><p>- clc1000_water: share in percentage of land within 1000 meters of transacted properties with water</p><p> </p><p> </p>
Parker Solar Probe Reconnection Exhaust Data Set for Close Encounters 4-11
<p>The data set contained in the ascii text files consist of the current sheet (CS) time intervals and the associated parameters of identified magnetic reconnection exhausts across CSs recorded by the Parker Solar Probe within R<0.26 AU from the Sun for close encounters 4-11. A detailed description is available in a manuscript submitted to The Astrophysical Journal by Eriksson et al. [2024] entitled "Parker Solar Probe Observations of Magnetic Reconnection Exhausts in Quiescent Plasmas Near the Sun".</p><p>The ascii data file zenodo_psp_ce_lmn_cs_output_sorted_tctr.txt contains a center time of the optimized Walen relation (tc, column 1) and columns 2-3 show the CS start (CS1) and end (CS2) times. This is followed by the time duration dtcs in seconds (column 4) and whether a SPAN-I instrument survey (s=sf00) or burst (a=af00) data product exist (column 5). The RTN components of the adjacent time averaged magnetic field before (B1_R, B1_T, B1_N) and after (B2_R, B2_T, B2_N) the CS then follows in columns 6-11 and a corresponding magnetic field rotation angle (Bshear1) at the CS (column 12). The CP dL, dM, dN columns 13-15 list the distances covered along the hybrid-LMN coordinate system based in a Cross-Product normal to the CS in terms of the average ion inertial length diavg (column 16), which is based on a non-corrected proton density (Np1+Np2)/2=Npavg (columns 17-19). The subsequent columns 20-28 are the adjacent VL,VM,VN on the two sides of the CS from the MVAB LMN system as well as their averages (VL1+VL2)=VLavg, (VM1+VM2)/2=VMavg and (VN1+VN2)/2=VNavg. This is followed (columns 29-37) by the corresponding VL,VM,VN and averages in the hybrid-LMN system using the cross-product (CP) normal. The adjacent average total magnetic field Btot1 and Btot2 (columns 38-39) and the LMN components of the magnetic field in the MVAB system [BL1, BL2], [BM1, BM2], [BN1, BN2] in columns 40-45 are followed by the corresponding magnetic fields in the CP hybrid-LMN system (columns 46-51). The ratio of the intermediate to minimum eigenvalue ratio is included (column 52) followed by the unit vectors of the MVAB system L=[L_R, L_T, L_N], M=[M_R, M_T, M_N], and N=[N_R, N_T, N_N] in columns 53-61. The times adjacent to the CS used to obtain this set of MVAB eigenvectors is found in columns 62-63 (MVAB UT1 and MVAB UT2). The magnetic fields (B1 and B2) used to obtain the local hybrid-LMN system relative these MVAB times relatively farther from the CS are associated with a rotation angle (Bshear2) in column 64. The local hybrid-LMN system from the CP normal is then listed in columns 65-73 LCP=[L_R, L_T, L_N], MCP=[M_R, M_T, M_N], NCP=[N_R, N_T, N_N]. The angle (degrees) between the MVAB N and NCP is listed in column 74 (Nmv*Ncp) followed by the last two columns 75-76 that contain the time interval (s) relative CS1 and CS2 to obtain average plasma data (dext) and magentic field data (bext).</p><p>A letter "m" right before column 1 flags the five events in this list of 236 events when the magnetic field did not change sign across the assumed CS. A letter "d" marks the 10 events associated with two opposite (double) exhausts.</p><p>The ascii data file zenodo_psp_ce_lmn_cs_output_sorted_qtn.txt contains the same current sheet (CS) start (CS1) and end (CS2) times (columns 1-2). This file also lists the adjacent average proton (non-corrected) density in columns 3-4 as in the first (tctr) file which is followed by the average proton temperatures (Tp1 and Tp2 in columns 5-6), solar wind speed (Vtot1 and Vtot2 in columns 7-8) and the hybrid system L-components of the velocity (VL1 and VL2 in columns 9-10). Columns 11-16 contain the minimum and maximum values within each CS of the non-correct proton density, proton temperature and VL component. Column 17 lists the daily median density ratio from the electron density (Ne) and the non-corrected proton density (Np) where Ne is obtained through quasi-thermal noise spectroscopy by Kruparova et al. (2023). Column 18 lists the average of this daily ratio. Column 19 lists a value fc. A value larger than 1.0 indicates that a CS event time period is corrected to a local Ne value using Npfc=(Ne/Np)*Np*fc, where Np is a non-corrected proton density, Ne/Np is the median of the daily Ne/Np ratio.</p><p>The ascii data file zenodo_psp_ce_lmn_cs_output_sorted_positions.txt contains the Parker Solar Probe median of its radial position within each CS1-CS2 time period in both solar radius and astronomical unit.</p><p>The PDF figures psp_ceXX_apj_plots_qtn_final.pdf for XX=04,05,06,07,08,09,10 and 11 contain all CS events with a reconnection exhaust for each close encounter 04-11 on the basis of the agreement with a Walen prediction. Each plot shows the pitch-angle distribution (0-180 degrees) of the supra-thermal energy flux, proton temperature (MK), proton density (black) corrected to a daily median ratio Ne/Np as Npcorr=(Ne/Np)*Np, where Ne (red dots) is obtained from a QTN analysis by Kruparova et al. (2023), L-components of the magnetic field and proton velocity with a red trace showing a Walen prediction to this VL across the CS, the M and N components of the magnetic field with BM shifted by its time-period average, and the M and N components of the proton velocity with VM shifted by its time-period average. The LMN unit vectors of the employed hybrid LMN system are shown below each plot for reference.</p><p>Finally, the eight ~11-day overview plots for each close encounter 4-11 marks the center times (tc) of each confirmed exhaust interval in this study of 231 events (red vertical dotted line) with the five marginal events marked as a black vertical dotted line. The panels from the top show the pitch-angle distribution (0-180 degrees) of the supra-thermal energy flux, proton temperature (MK), magnetic field strength, R-components of B and V, N-components of B and V, and the radial postion of Parker Solar Probe in terms of the solar radius. Here, R and N are two of the three RTN system components of B and V.</p>
Global Biodiversity Information Facility (GBIF): an exhaustive list of gbif record ids, dataset keys, and their associated Occurrence IDs, Institution Code, Collection Codes and Catalog Numbers. hash://sha256/ea88f03a7bfd1ba853fdbea3203d54ab81ac3cdc8e8da7c96bbbba9c4b05d933 hash://md5/c49fe34785354847b37ea4509261e130
<p>The Global Biodiversity Information Facility (GBIF) indexes thousands of biodiversity datasets from Natural History Collections, citizen science initiatives (e.g., iNaturalist, eBird), and other sources. As part of the index process, GBIF associates at least two identifiers with indexed records: a record id (aka gbifID) and a dataset id (aka dataset key). These ids are central to do lookup, reference data, and package interpreted data products.</p> <p>This publication contains an exhaustive list of GBIF IDs and ids associated by their data providers as derived from:</p> <p>GBIF.org (01 March 2023) GBIF Occurrence Download https://doi.org/10.15468/dl.pk3trq</p> <p>The resource (size: ~260GB) provided by GBIF had content id hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97 and was used to generate the resource included in this publication using</p> <pre><code class="language-bash">preston cat 'zip:hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97!/0015281-230224095556074.csv'\ | cut -f 1,2,3,37,38,39\ | gzip\ > gbifid.tsv.gz </code></pre> <p>with the content id of gbifid.tsv.gz (size: ~35GB) being hash://sha256/a339e32e10edaad585f61f2ded06cbb23e0618c65a6360db18d7d729054940a8 .</p> <p>the first 10 lines of gbifid.tsv.gz as extracted via</p> <pre><code>preston cat --remote https://zenodo.org/record/7789866/files,https://linker.bio hash://sha256/a339e32e10edaad585f61f2ded06cbb23e0618c65a6360db18d7d729054940a8\ | gunzip\ | head</code></pre> <p>are:</p> <pre><code>gbifID datasetKey occurrenceID institutionCode collectionCode catalogNumber 2997162320 c71c8000-9fc7-422c-804a-ce6abe751771 3399442 CEPEC CEPEC CEPEC00109669 2997162309 c71c8000-9fc7-422c-804a-ce6abe751771 2733085 CEPEC CEPEC CEPEC00000818 2997162317 c71c8000-9fc7-422c-804a-ce6abe751771 2733086 CEPEC CEPEC CEPEC00000888 2997162313 c71c8000-9fc7-422c-804a-ce6abe751771 3399443 CEPEC CEPEC CEPEC00109744 2997162306 c71c8000-9fc7-422c-804a-ce6abe751771 2733087 CEPEC CEPEC CEPEC00000889 2997162316 c71c8000-9fc7-422c-804a-ce6abe751771 3399440 CEPEC CEPEC CEPEC00109605 2997162324 c71c8000-9fc7-422c-804a-ce6abe751771 2733088 CEPEC CEPEC CEPEC00000890 2997162308 c71c8000-9fc7-422c-804a-ce6abe751771 3399441 CEPEC CEPEC CEPEC00109615 2997162303 c71c8000-9fc7-422c-804a-ce6abe751771 2733089 CEPEC CEPEC CEPEC00000891</code></pre> <p>Note that at time of writing, the html resource associated with the occurrence id 2997162320, and data set key c71c8000-9fc7-422c-804a-ce6abe751771 (extracted from of the first data row example above) are available via:</p> <p>https://gbif.org/occurrence/2997162320</p> <p>and</p> <p>https://gbif.org/dataset/c71c8000-9fc7-422c-804a-ce6abe751771</p> <p>respectively.</p> <p>This resource was initially created to help integrate with Bionomia (https://bionomia.net) to help associate people identifiers provided by bionomia to their original records via their GBIF ids. Bionomia re-uses GBIF records ids as a way to define links between records and the people (e.g., curators, collectors, identifiers) that worked on them. </p> <p>In other words, this resource provides a versioned translation table from the GBIF data universe (as defined by GBIF record ids, and dataset keys) to the data collections that exist (and evolve) independent of it. </p> <p>Note that the resource identified by hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97 was not included in this publication it was too big (260GB) to fit. You may be able to retrieve the resource from its original location at https://api.gbif.org/v1/occurrence/download/request/0015281-230224095556074.zip .</p>
Imaging Mass Cytometry Dataset of exhausted and non-exhausted breast cancer microenvironments
<p>A cohort of human breast tumor samples were annotated as having an "exhausted" or "non-exhausted" immune environment based on CyTOF characterization of T cell phenotypes (see Wagner et al. 2019). 12 samples (6 exhausted, 6 non-exhausted) were then selected for further analysis by Imaging Mass Cytometry (IMC) with the goal to compare the two immune environment types and to comprehensively characterize exhaustion-associated spatial features of the tumor microenvironment. For IMC, two consecutive FFPE sections of each sample were stained with two different antibody panels (Protein Panel and RNAscope Panel), and 4-10 regions of interest (ROIs, 1mm x 1mm) were measured on each section. ROIs on consecutive sections were registered manually to be as spatially close as possible.</p>
A new ChEMBL dataset for FastTargetPred and target fishing for an exhaustive list of linear tetrapeptides
<p>A ChEMBL-v29 dataset was generated to be used with the ligand-based similarity search target prediction engine FastTargetPred (https://github.com/ludovicchaput/FastTargetPred). Using this new dataset, attempts to predict macromolecular targets for a published dataset of 160,000 tetrapeptides was performed.</p> <p>The dbchembl29 directory contains all the files for FastTargetPred. This command line tool compares using different types of fingerprints, a file containing small query molecules in SDF format (it can be 1 molecule or a collection) to molecules extracted from ChEMBL29. If a match is found, this suggests that your query molecule is similar to a ChEMBL compound and as the ChEMBL compound has bioactivity data against one or more macromolecular target, then this suggests that your query compound could bind to targets that interact with compounds that are similar to the query molecule. The so-called similarity principle in chemistry. Fingerprints are computed with http://www.mayachemtools.org/, a collection of Perl and Python scripts for Chemoinformatics and (Structural) Bioinformatics.</p> <p>To run FastTargetPred with the new ChEMBL 29 data, you just need to unzip the dbchembl29 directory into the FastTargetPred main directory.</p> <p>The default FastTargetPred commands (e.g., python3 FastTargetPred.py rivaroxaban.sdf, the default command uses ECFP4 fingerprints and a Tanimoto coef of 0.6, rivaroxaban here is the query compound, it is provided in the extra_data directory) will use the data present in the default db directory and thus a curated version of ChEMBL-25 release. It was the version of the ChEMBL database available when FastTargetPred was developed. Since then, many new molecules have been added and this is why we generated the ChEMBL-29 dataset (last ChEMBL version at the time of writing).</p> <p>To use the new ChEMBL-29 data, you can run the following command:</p> <p>python3 FastTargetPred.py rivaroxaban.sdf -fp MACCS -tc 0.9 -db dbchembl29/chembl29_active</p> <p>This applies a similarity search for the query compound (here rivaroxaban, you can for instance move this SDF file in the directory containing the file FastTargetPred.py) using MACCS fingerprints, a Tanimoto coefficient threshold of 0.9 and the -db option forces the system to look at the ChEMBL29 curated data and not the default ChEMBL-25 data. This 0.9 value means to focus on molecules very similar to rivaroxaban present in the ChEMBL data. If one is looking for more distantly related compounds, then a value of 0.7 can be used. Users can try different values or try consensus scoring...See FastTargetPred: a program enabling the fast prediction of putative protein targets for input chemical databases. Chaput et al., Bioinformatics. 2020 Aug 15;36(14):4225-4226</p> <p>The chembl29 directory also contains 714,780 compounds (canonical SMILES strings) extracted from ChEMBL29 that have bioactivity data. Fingerprints could not be computed for 19 molecules that have unusual chemistry. We selected the following thresholds (eg, binding assays, activity against targets less than 20 micro-molar, ChEMBL confidence_score = 6 or above, maximum = 9).</p> <p>With this new dataset, we attempted to predict potential targets with FastTargetPred for 160,000 input query peptides (4 amino acids, combination should be 20 x 20 x 20 x 20) previously reported by Dewi Prasasty and Perdana Istyastono, Data in brief 27 (2019) 104607. The peptides for which a putative target was predicted are shown in two DataWarrior files with the amino acid sequence of the query peptide, the compounds found to be similar in the ChEMBL29 dataset (fingerprints = ECFP4, Tanimoto 0.6) and thus the compound IDs, the target ChEMBL IDs, mapping to the UniProt database when available, information about disease involvements, Reactome pathway database identifiers. These two DataWarrior files are searchable, can be sorted and hyperlinks to the ChEMBL, UniProt and Reactome databases have been inserted.</p> <p> </p>
Dataset for the publication: Impact of unintentionally formed CH2O in oxygenated fuel exhausts on DeNOx-SCR at different NO2/NOx ratios under close to real conditions
<p>The dataset covers the research data of the publication in Catalysis Science & Technology with the title "Impact of unintentionally formed CH2O in oxygenated fuel exhausts on DeNOx-SCR at different NO2/NOx ratios under close to real conditions" (DOI: 10.1039/d2cy01935c).</p>
Supporting data for: "Diaphysator: an online application for the exhaustive cartography and user-friendly statistical analysis of long bone diaphyses"
<p>Example of dataset to be used with the R-shiny application “Diaphysator”, composed of right tibiae and femora.</p> <p>These data file have been published in: Lacoste Jeanson, A., Santos, F., Villa, C., Banner, J., & Bruzek, J. (2018). Architecture of the femoral and tibial diaphyses in relation to body mass and composition: Research from whole-body CT. <em>American Journal of Physical Anthropology</em>, 167, 813– 826. doi: <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/ajpa.23713">10.1002/ajpa.23713</a></p> <p>This zip file contains:</p> <ul> <li>an “Information file” in CSV format</li> <li>various data files for human femora and tibiae in CSV format</li> </ul> <p>For all CSV files, the field separator is the comma “,” and the character used for decimal points is the dot “.”</p>
Data for publication "Switzerland's PM10 and PM2.5 environmental increments show the importance of non-exhaust emissions"
<p>Data for publication "Switzerland's PM10 and PM2.5 environmental increments show the importance of non-exhaust emissions". Please see README.md for information. </p>
Peripheral T-cell profiling reveals downregulated exhaustion marker and increased diversity in lymphedema post-lymphatic venous anastomosis
<p>In this study, we compared T cell receptor (TCR) repertoire diversity between patients with lymphedema, post-lymphatic venous anastomosis and healthy controls (HCs). TCR diversity was decreased in lymphedema compared with HCs; such TCR skewing was drastically improved in post-LVA.</p>
Metadata with submitted Emission Control Science and Technology Journal manuscript Traceable uncertainty of exhaust flow meters embedded in portable emission measurement systems
<p>Metadata with submitted Emission Control Science and Technology Journal <em>Traceable uncertainty of exhaust flow meters embedded in portable emission measurement systems</em></p> <p>Link to article: https://link.springer.com/article/10.1007/s40825-025-00260-z</p>
KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection (LCMV DSM scRNA data and ATAC-seq)
Open the record for dataset details and reuse information.
KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection (PerturbSeq Data)
Open the record for dataset details and reuse information.
Dataset: 2D particle-in-cell (PIC) simulation of the magnetic reconnection for the paper "Electron mixing and isotropization in the exhaust of asymmetric magnetic reconnection with a guide field"
<p>This repository contains pubilicly available numerical data of a 2D magnetic reconnection event, which includes the field data and plasma moment data. The simulation is performed with the VPIC code. The simulated data are used for the paper "Electron mixing and isotropization in the exhaust of asymmetric magnetic reconnection with a guide field". </p>
In the face of climate change and exhaustive exercise: The physiological response of an important recreational fish species
<p>Cobia (<i>Rachycentron canadum</i>) support recreational fisheries along the U.S. mid- and south-Atlantic states and have been recently subjected to increased fishing effort, primarily during their spawning season in coastal habitats where increasing temperatures and expanding hypoxic zones are occurring due to climate change. We therefore undertook a study to quantify the physiological abilities of cobia to withstand increases in temperature and hypoxia, including their ability to recover from exhaustive exercise. Respirometry was conducted on cobia from Chesapeake Bay to determine aerobic scope, critical oxygen saturation, ventilation volume, and the time to recover from exhaustive exercise under temperature and oxygen conditions projected to be more common in inshore areas by the middle and end of this century. Cobia physiologically tolerated predicted mid- and end-of-century temperatures (28-32°C) and oxygen concentrations as low as 1.7-2.4 mg l<sup>-1</sup> suggesting that they may be a climate change winner. Our results indicated cobia can withstand environmental fluctuations that occur in coastal habitats and the broad environmental conditions their prey items can tolerate. However, at these high temperatures some cobia did suffer post exercise mortality. It appears cobia will be able to withstand near future climate impacts in coastal habitats like Chesapeake Bay, but as conditions worsen, catch-and-release fishing may result in higher mortality than under present conditions.</p>
Supplementary material 3: List of tested and analyzed data sharing tools (non-exhaustive) from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
List of tested and analyzed data sharing tools (non-exhaustive)
DATA from: Pulsed plasma thruster exhaust reconstruction
<p><strong>Data from: Pulsed plasma thruster exhaust reconstruction</strong></p> <p>- Authors: Scherezade Barquero, Jaume Navarro-Cavallé, Mario Merino</p> <p>- Contact email: mbalsera@ing.uc3m.es</p> <p>- Date: 2024-03-30</p> <p>- Keywords: electric propulsion, plasma, pulsed plasma thruster, PPT, plume, time-of-flight</p> <p>- Version: 1.1</p> <p>- License: This dataset is made available under the <a href="http://opendatacommons.org/licenses/by/1.0">Open Data Commons Attribution License</a></p> <p><strong>Abstract</strong></p> <p>This dataset contains the data found in the plots of the accepted article in the Journal: Plasma Sources Science and Technology (PSST):</p> <p><a href="https://iopscience.iop.org/article/10.1088/1361-6595/ad35e5">"Pulsed plasma thruster exhaust reconstruction"</a></p> <p>The data in this repository are obtained from measurements performed on an PTFE (ablative) pulsed plasma thruster as described in the reference. For further information on the experimental setup please refer to the article.</p> <p><strong>Data Files</strong></p> <p>The datasets are uploaded in MATLAB MAT-file format, organized in MATLAB structures. </p> <p>The files are organized following the order of the figures in the article. Therefore each file varies in size and contains different variables. </p> <p><strong>Citation</strong></p> <p>Any works using this dataset or any part of it in any form shall cite it as follows:</p> <p>The preferred means of citation is to reference the publication associated with the article in the Journal Plasma Sources Science and Technology (PSST):</p> <p>"Pulsed plasma thruster exhaust reconstruction"</p> <p>The BibTex is also provided for the sake of convenience:</p> <p>@article{barq2024a,<br> author={Barquero, Scherezade and Navarro-Cavallé, Jaume and Merino, Mario},<br> title={Pulsed plasma thruster exhaust reconstruction.},<br> journal={Plasma Sources Science and Technology},<br> url={http://iopscience.iop.org/article/10.1088/1361-6595/ad35e5},<br> year={2024},</p> <p>month={March},</p> <p> doi = {<a href="https://iopscience.iop.org/article/10.1088/1361-6595/ad35e5">10.1088/1361-6595/ad35e5</a>},<br> publisher={IOP Publishing} <br>}<br><br></p> <p>Optionally the dataset can be cited by referencing the corresponding DOI:</p> <p>10.5281/zenodo.10908405</p> <p><strong>Acknowledgments</strong></p> <p>This work was been supported by the MARTINLARA project, funded by the Comunidad de Madrid, under Grant reference P2018/NMT-4333 MARTINLARA-CM. Additional support came from the ESPEOS Project, funded by the Agencia Estatal de Investigación (Spanish National Research Agency), Under Grant Number PID2019-108034RBQ5 I00/AEI/10.13039/501100011033.</p>
Exhaustive Symbolic Regression Function Sets
<p>ESR (Exhaustive Symbolic Regression) is a symbolic regression algorithm which efficiently and systematically finds all possible equations at fixed complexity (defined to be the number of nodes in its tree representation) given a set of basis functions. This is achieved by identifying the unique equations, so that one minimises the number of equations which one would have to fit to data.</p> <p>Here we provide the functions generated, the unique equations, and the mappings between all equations and unique ones using different sets of basis functions. These are:</p> <ul> <li>"core_maths": <span>\(\{x, a, {\rm inv}, +, -, \times, \div, {\rm pow} \}\)</span></li> <li>"ext_maths": <span>\(\{x, a, {\rm inv}, \sqrt{\cdot}, {\rm square}, \exp, +, -, \times, \div, {\rm pow} \}\)</span></li> <li><span>"base_e_maths": \(\{x, a, {\rm inv}, \exp, \log, \exp, +, -, \times, \div, {\rm pow} \}\)</span></li> </ul> <p>where <span>\(x\)</span> is the input variable and <span>\(a\)</span> denotes a constant.</p> <p>One can fit these functions to a data set of interest by using the <a href="https://esr.readthedocs.io">ESR package</a>.</p>
The effect of ethical leadership on organizational outcomes in the hospitality industry: the mediating role of trust and emotional exhaustion
<p>SPSS dataset for the paper: "The effect of ethical leadership on organizational outcomes in the hospitality industry: the mediating role of trust and emotional exhaustion".</p>
Impact of CD4 T cells on intratumoral CD8 T cell exhaustion and responsiveness to PD-1 blockade therapy in mouse brain tumors
<p>scRNA-seq data (Cellranger filtered feature-barcode matrices) and scVDJ-seq data (Cellranger filtered_contig_annotations.csv files) for publication listed above.</p>
A general swimming response in exhausted obligate swimming fish
<p><span>Marine organisms normally swim at elevated speeds relative to cruising speeds only during strenuous activity, such as predation or escape. We measured swimming speeds of 29 ram ventilating sharks from 10 species and of three Atlantic bluefin tunas immediately after exhaustive exercise (fighting a capture by hook-and-line), and unexpectedly found all individuals exhibited a uniform mechanical response, with swimming speed initially 2 times higher than the cruising speeds reached approximately 6 hours later. We hypothesised that elevated swimming behaviour is a means to increase energetic demand and drive the removal of lactate accumulated during capture via oxidation. To explore this hypothesis, we estimated the mechanical work that must have been spent by an animal to elevate its swim speed, and then showed that the amount of lactate that could have been oxidized to fuel it comprises a significant portion of the amount of lactate normally observed in fishes after exhaustive exercise. An estimate for the full energetic cost of the catch-and-release event ensued. </span></p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.