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3,684 results for “extract”
Raw data of compounds extracted by GC-MS from each population replicate's of I. uriae ticks from Iceland.
<p>Raw data representing all the compounds extracted by GC-MS from each population replicate’s of <em>I. uriae</em> ticks from three sites in Iceland. Each replicate contain a pool of 10 living flat female ticks.</p> <p>Site: name of the site where ticks were collected.</p> <p>Host: name of the host bird.</p> <p>Replicate: number of the replicate (1 to 4).</p> <p>Peak: number of the detected peaks correponding to extracted compounds.</p> <p>Retention Time: time elapsed between sample introduction and the maximum signal of the given compound.</p> <p>Area: area under the curves of each detected coumpounds on the chromatogram.</p>
Source data and code for: Existing fossil fuel extraction would warm the world beyond 1.5°C
<p>Source data and code for the study, "Existing fossil fuel extraction would warm the world beyond 1.5°C." Datasets 1-4 include mine-level data collected for China (Dataset 1), India (Dataset 2), and five other countries (Dataset 3) that are among the world's top nine coal producers - the United States, Indonesia, Australia, South Africa, and Poland. Dataset 4 includes global and country-level output data from the 1,000-run Monte Carlo simulation. <Committed_Reserves_Monte_Carlo_Input_Data.zip> includes data and code to replicate the Monte Carlo simulation.</p>
Machine Learning Assisted SSH Keys Extraction From The Heap Dump
<p>This dataset contains heap dump of OpenSSH that contains session keys.</p> <p>On the performance test data, we also include the PCAP file that contains the encrypted SSH network traffic. With the correct session keys, it can be decrypted.</p>
Extract from the PEAPL framework : Modelling "Writing sentences" competency (French as the schooling language)
<p>Competences, skills and knowledges that make up "writing sentences" competency, based on the linguistic praxeological organization of French as the schooling language (https://doi.org/10.5281/zenodo.4001381). This is an extract of the general framework, some of the visible objects are linked to other objects in other main competences. Orange links show how pedagogic ressources (game levels) are linked to framework objects.</p> <p>This framework is used for the PEAPL (peapl.eu) project to link activities in the GamesHub platform (for example activities using "L'Orthodyssée des Gram" grammar online game, available at https://www.lafamillegram.ch/#)</p> <p> </p> <p> </p> <p> </p>
Acknowledged scholars extracted from open access journals
<p><strong>Published paper</strong><br> For details of the data development: </p> <p>Kusumegi, K., Sano, Y. Dataset of identified scholars mentioned in acknowledgement statements. Sci Data 9, 461 (2022). https://doi.org/10.1038/s41597-022-01585-y</p> <p>To use the data, please cite the above mentioned paper .</p> <p> </p> <p><strong>Data on scholars mentioned in acknowledgments extracted from open access journals.</strong></p> <p>This data firstly updated the repository at University of Tsukuba (Division of Policy and Planning Sciences Commons).</p> <p>https://commons.sk.tsukuba.ac.jp/data_en</p> <pre> <strong>Each csv correspond to:</strong> biology.csv as PLOS Biology compbiology.csv as PLOS Computational Biology genetics.csv as PLOS Genetics medicine.csv as PLOS Medicine ntds.csv as PLOS Neglected Tropical Diseases pathogenes.csv as PLOS Pathogens plosone.csv as PLOS ONE srep.csv as Scientific Reports Data details: Doi: DOI of the paper PaperId: Paper ID in MAG AcknowledgedId: Acknowledged scholar's ID in MAG CollaborationApproach: Acknowledged scholar is identified by collaboration relationship CitationApproach: Acknowledged scholar is identified by citation relationship </pre> <p> </p>
All raw data for Fukuzawa, T et al. "Environmental DNA extraction method for a high and stable DNA yield"
<p>The all raw data of quantitative PCR for environmental DNA in Fukuzawa, T et al. "Environmental DNA extraction method for a high and stable DNA yield".</p> <p> </p>
Monthly averaged lightning and LCC lightning data extracted from present-day (2009-2011) and projected (2090-2095) EMAC simulations (T42L90MA resolution)
<pre>About Dataset Monthly averaged lightning and LCC lightning data extracted from present-day (2009-2011) and projected (2090-2095) EMAC simulations (T42L90MA resolution). Authors: Francisco J. Perez-Invernon, Francisco J. Gordillo-Vazquez, Patrick Joeckel and Heidi Huntrieser Description of the data PaR_T: Lightning parameterization based on cloud top height. PaR_L: Lightning parameterization based on cloud top height and modified over the oceans. Grewe: Lightning parameterization based on updraft velocity. AaP_P: Lightning parameterization based on convective precipitation. A AaP_M: Lightning parameterization based on Updraft strength at 440~hPa. PRaAP: Lightning parameterization based on cloud top height and updraft velocity. FinIF: Lightning parameterization based on updraft mass flux of ice at 440 hPa. extIF: Lightning parameterization based on updraft mass flux of ice at 440 hPa an isotherm. File format: netcdf Example: netcdf </pre> <p>2009_10h_______20090701_0000_mmlb_PRaAP.nc<br> netcdf \2009_10h_______20090701_0000_mmlb_PRaAP {<br> dimensions:<br> time = UNLIMITED ; // (1 currently)<br> lon = 128 ;<br> lat = 64 ;<br> tbnds = 2 ;<br> variables:<br> double time(time) ;<br> time:long_name = "time" ;<br> time:bounds = "time_bnds" ;<br> time:units = "day since 2009-01-01 00:00:00" ;<br> time:calendar = "gregorian" ;<br> double YYYYMMDD(time) ;<br> YYYYMMDD:long_name = "time" ;<br> YYYYMMDD:units = "days as %Y%m%d.%f" ;<br> YYYYMMDD:calendar = "gregorian" ;<br> double dt(time) ;<br> dt:long_name = "delta_time" ;<br> dt:units = "s" ;<br> double nstep(time) ;<br> nstep:long_name = "current time step" ;<br> float lon(lon) ;<br> lon:long_name = "longitude" ;<br> lon:units = "degrees_east" ;<br> float lat(lat) ;<br> lat:long_name = "latitude" ;<br> lat:units = "degrees_north" ;<br> float aps(time, lat, lon) ;<br> aps:long_name = "surface pressure" ;<br> aps:units = "Pa" ;<br> aps:representation = "GP_2D_HORIZONTAL" ;<br> aps:grid_type = "gaussian" ;<br> aps:table = 128 ;<br> aps:code = 134 ;<br> aps:REFERENCE_TO = "g3b: aps" ;<br> aps:coordinates = "lon lat" ;<br> aps:cell_methods = "time: point" ;<br> float aps_ave(time, lat, lon) ;<br> aps_ave:long_name = "surface pressure" ;<br> aps_ave:units = "Pa" ;<br> aps_ave:representation = "GP_2D_HORIZONTAL" ;<br> aps_ave:grid_type = "gaussian" ;<br> aps_ave:table = 128 ;<br> aps_ave:code = 134 ;<br> aps_ave:REFERENCE_TO = "g3b: aps" ;<br> aps_ave:coordinates = "lon lat" ;<br> aps_ave:cell_methods = "time: mean" ;<br> float fpscg(time, lat, lon) ;<br> fpscg:long_name = "CG flash frequency" ;<br> fpscg:units = "1/s" ;<br> fpscg:REFERENCE_TO = "lnox_PRaAP_gp: fpscg" ;<br> fpscg:coordinates = "lon lat" ;<br> fpscg:cell_methods = "time: point" ;<br> float fpscg_ave(time, lat, lon) ;<br> fpscg_ave:long_name = "CG flash frequency" ;<br> fpscg_ave:units = "1/s" ;<br> fpscg_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpscg" ;<br> fpscg_ave:coordinates = "lon lat" ;<br> fpscg_ave:cell_methods = "time: mean" ;<br> float fpsic(time, lat, lon) ;<br> fpsic:long_name = "IC flash frequency" ;<br> fpsic:units = "1/s" ;<br> fpsic:REFERENCE_TO = "lnox_PRaAP_gp: fpsic" ;<br> fpsic:coordinates = "lon lat" ;<br> fpsic:cell_methods = "time: point" ;<br> float fpsic_ave(time, lat, lon) ;<br> fpsic_ave:long_name = "IC flash frequency" ;<br> fpsic_ave:units = "1/s" ;<br> fpsic_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsic" ;<br> fpsic_ave:coordinates = "lon lat" ;<br> fpsic_ave:cell_methods = "time: mean" ;<br> float fpsm2cg(time, lat, lon) ;<br> fpsm2cg:long_name = "CG flash density" ;<br> fpsm2cg:units = "1/s/m2" ;<br> fpsm2cg:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2cg" ;<br> fpsm2cg:coordinates = "lon lat" ;<br> fpsm2cg:cell_methods = "time: point" ;<br> float fpsm2cg_ave(time, lat, lon) ;<br> fpsm2cg_ave:long_name = "CG flash density" ;<br> fpsm2cg_ave:units = "1/s/m2" ;<br> fpsm2cg_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2cg" ;<br> fpsm2cg_ave:coordinates = "lon lat" ;<br> fpsm2cg_ave:cell_methods = "time: mean" ;<br> float fpsm2ic(time, lat, lon) ;<br> fpsm2ic:long_name = "IC flash density" ;<br> fpsm2ic:units = "1/s/m2" ;<br> fpsm2ic:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2ic" ;<br> fpsm2ic:coordinates = "lon lat" ;<br> fpsm2ic:cell_methods = "time: point" ;<br> float fpsm2ic_ave(time, lat, lon) ;<br> fpsm2ic_ave:long_name = "IC flash density" ;<br> fpsm2ic_ave:units = "1/s/m2" ;<br> fpsm2ic_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2ic" ;<br> fpsm2ic_ave:coordinates = "lon lat" ;<br> fpsm2ic_ave:cell_methods = "time: mean" ;<br> float fpslcc10(time, lat, lon) ;<br> fpslcc10:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10:units = "1/s" ;<br> fpslcc10:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc10" ;<br> fpslcc10:coordinates = "lon lat" ;<br> fpslcc10:cell_methods = "time: point" ;<br> float fpslcc10_ave(time, lat, lon) ;<br> fpslcc10_ave:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10_ave:units = "1/s" ;<br> fpslcc10_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc10" ;<br> fpslcc10_ave:coordinates = "lon lat" ;<br> fpslcc10_ave:cell_methods = "time: mean" ;<br> float fpslcc20(time, lat, lon) ;<br> fpslcc20:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20:units = "1/s" ;<br> fpslcc20:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc20" ;<br> fpslcc20:coordinates = "lon lat" ;<br> fpslcc20:cell_methods = "time: point" ;<br> float fpslcc20_ave(time, lat, lon) ;<br> fpslcc20_ave:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20_ave:units = "1/s" ;<br> fpslcc20_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc20" ;<br> fpslcc20_ave:coordinates = "lon lat" ;<br> fpslcc20_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc10(time, lat, lon) ;<br> fpsm2lcc10:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10:units = "1/s/m2" ;<br> fpsm2lcc10:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc10" ;<br> fpsm2lcc10:coordinates = "lon lat" ;<br> fpsm2lcc10:cell_methods = "time: point" ;<br> float fpsm2lcc10_ave(time, lat, lon) ;<br> fpsm2lcc10_ave:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10_ave:units = "1/s/m2" ;<br> fpsm2lcc10_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc10" ;<br> fpsm2lcc10_ave:coordinates = "lon lat" ;<br> fpsm2lcc10_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc20(time, lat, lon) ;<br> fpsm2lcc20:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20:units = "1/s/m2" ;<br> fpsm2lcc20:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc20" ;<br> fpsm2lcc20:coordinates = "lon lat" ;<br> fpsm2lcc20:cell_methods = "time: point" ;<br> float fpsm2lcc20_ave(time, lat, lon) ;<br> fpsm2lcc20_ave:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20_ave:units = "1/s/m2" ;<br> fpsm2lcc20_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc20" ;<br> fpsm2lcc20_ave:coordinates = "lon lat" ;<br> fpsm2lcc20_ave:cell_methods = "time: mean" ;<br> float fpssprite(time, lat, lon) ;<br> fpssprite:long_name = "Sprites flash frequency" ;<br> fpssprite:units = "1/s" ;<br> fpssprite:REFERENCE_TO = "lnox_PRaAP_gp: fpssprite" ;<br> fpssprite:coordinates = "lon lat" ;<br> fpssprite:cell_methods = "time: point" ;<br> float fpssprite_ave(time, lat, lon) ;<br> fpssprite_ave:long_name = "Sprites flash frequency" ;<br> fpssprite_ave:units = "1/s" ;<br> fpssprite_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpssprite" ;<br> fpssprite_ave:coordinates = "lon lat" ;<br> fpssprite_ave:cell_methods = "time: mean" ;<br> float fpsm2sprite(time, lat, lon) ;<br> fpsm2sprite:long_name = "Sprites flash density" ;<br> fpsm2sprite:units = "1/s/m2" ;<br> fpsm2sprite:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2sprite" ;<br> fpsm2sprite:coordinates = "lon lat" ;<br> fpsm2sprite:cell_methods = "time: point" ;<br> float fpsm2sprite_ave(time, lat, lon) ;<br> fpsm2sprite_ave:long_name = "Sprites flash density" ;<br> fpsm2sprite_ave:units = "1/s/m2" ;<br> fpsm2sprite_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2sprite" ;<br> fpsm2sprite_ave:coordinates = "lon lat" ;<br> fpsm2sprite_ave:cell_methods = "time: mean" ;<br> float bps(time, lat, lon) ;<br> bps:long_name = "BJ flash frequency" ;<br> bps:units = "1/s" ;<br> bps:REFERENCE_TO = "bluejetbPRaAP_gp: bps" ;<br> bps:coordinates = "lon lat" ;<br> bps:cell_methods = "time: point" ;<br> float bps_ave(time, lat, lon) ;<br> bps_ave:long_name = "BJ flash frequency" ;<br> bps_ave:units = "1/s" ;<br> bps_ave:REFERENCE_TO = "bluejetbPRaAP_gp: bps" ;<br> bps_ave:coordinates = "lon lat" ;<br> bps_ave:cell_methods = "time: mean" ;<br> float bpsm2(time, lat, lon) ;<br> bpsm2:long_name = "BJ flash density" ;<br> bpsm2:units = "1/s/m2" ;<br> bpsm2:REFERENCE_TO = "bluejetbPRaAP_gp: bpsm2" ;<br> bpsm2:coordinates = "lon lat" ;<br> bpsm2:cell_methods = "time: point" ;<br> float bpsm2_ave(time, lat, lon) ;<br> bpsm2_ave:long_name = "BJ flash density" ;<br> bpsm2_ave:units = "1/s/m2" ;<br> bpsm2_ave:REFERENCE_TO = "bluejetbPRaAP_gp: bpsm2" ;<br> bpsm2_ave:coordinates = "lon lat" ;<br> bpsm2_ave:cell_methods = "time: mean" ;<br> double time_bnds(time, tbnds) ;<br> time_bnds:long_name = "time bounds" ;<br> time_bnds:units = "days since 2009-01-01T00:00:00Z" ;<br> time_bnds:cell_methods = "time: point" ;</p> <p>// global attributes:<br> :MESSy = "MESSy version d2.55.1-62-g7ea171fc6-dirty_7ea171fc682744f70976123b863cca166dd2023b_2021-05-19T16:21:08+00:00_2021-06-04T13:28:34+0200, http://www.messy-interface.org" ;<br> :MESSy_switch = "version 1.0" ;<br> :MESSy_channel = "version 2.4.3" ;<br> :MESSy_tracer = "version 2.6" ;<br> :MESSy_timer = "version 0.1" ;<br> :MESSy_qtimer = "version 3.0" ;<br> :MESSy_import = "version 1.0" ;<br> :MESSy_grid = "version v1.5" ;<br> :MESSy_rnd = "version 1.1" ;<br> :MESSy_aeropt = "version 2.0.2" ;<br> :MESSy_cloud = "version 2.2" ;<br> :MESSy_cloudopt = "version 2.1b" ;<br> :MESSy_convect = "version 2.0" ;<br> :MESSy_gwave = "version 1.0" ;<br> :MESSy_lnox = "version 3.0" ;<br> :MESSy_orbit = "version 0.9" ;<br> :MESSy_orogw = "version 1.1" ;<br> :MESSy_rad = "version 2.2" ;<br> :MESSy_e5vdiff = "version 1.2" ;<br> :MESSy_surface = "version 1.2" ;<br> :MESSy_tropop = "version 2.1" ;<br> :MESSy_viso = "version 2.3" ;<br> :MESSy_experiment = "2009_10h" ;<br> :EXEC_CHECKSUM = "45e5ddd17ae5992f931a9ba4bab4a921 bin/echam5.exe (md5sum)" ;<br> :GCM = "ECHAM5 version 5.3.02, Max-Planck Institute for Meteorology, Hamburg" ;<br> :GCM_spherical_trunc_n = 42 ;<br> :GCM_spherical_trunc_m = 42 ;<br> :GCM_spherical_trunc_k = 42 ;<br> :GCM_vertical_mode = "middle atmosphere (MA)" ;<br> :GCM_horizontal_mode = "global" ;<br> :GCM_advection = "Lin&Rood" ;<br> :GCM_start_date_time = "20090101 000000" ;<br> :GCM_timestep = 900.f ;<br> :F95_COMPILER_VERSION = "ifort (IFORT) 17.0.2 20170213" ;<br> :F95_COMPILER_CALL = "/opt/mpi/bullxmpi_mlx/1.2.9.2/bin/mpif90" ;<br> :F95_COMPILER_FLAGS = "-sox -fpp -g -O2 -xCORE-AVX2 -fp-model strict -align all -save-temps -DBULL -I/sw/rhel6-x64/sys/bullxlib-1.0.0/include -L/sw/rhel6-x64/sys/bullxlib-1.0.0/lib -Wl,-rpath,/sw/rhel6-x64/sys/bullxlib-1.0.0/lib -lbullxMATH -no-wrap-margin" ;<br> :F95_PREPROC_DEFINITIONS = "-DMESSY -DLITTLE_ENDIAN -D_LINUX64 -DHAVE_PNETCDF -DPNCREGRID -DMPIOM_13B -D_VCSREV_=\'d2.55.1-62-g7ea171fc6-dirty_7ea171fc682744f70976123b863cca166dd2023b_2021-05-19T16:21:08+00:00_2021-06-04T13:28:34+0200\'" ;<br> :F95_COMPILER_INCLUDES = "-I/sw/rhel6-x64/netcdf/netcdf_fortran-4.4.2-intel14/include -I/sw/rhel6-x64/netcdf/netcdf_fortran-4.4.2-intel14/include -I/sw/rhel6-x64/netcdf/parallel_netcdf-1.6.0-bullxmpi-intel14/include" ;<br> :operating_date_time = "20210615 081833" ;<br> :operating_system = "Linux 2.6.32-754.33.1.el6.x86_64 on x86_64" ;<br> :operating_host = "mlogin102" ;<br> :operating_user = "Francisco-Javier Perez-Invernon (b309171)" ;<br> :channel_io_pe = 172 ;<br> :channel_time_slo = 5182200.f ;<br> :channel_name = "mmlb_PRaAP" ;<br> :channel_file_type = "output" ;<br> :channel_file_name = "2009_10h_______20090701_0000_mmlb_PRaAP.nc" ;<br> :channel_netcdf_lib = "4.3.2 of May 5 2015 13:21:25 $" ;</p>
VAMDC extraction with identifier = 579afd3a-9506-447c-9d76-f294c5b9bd38
<div>This is a dataset extracted from http://tipbase.obspm.fr/12.07/vamdc/tap/ VAMDC node.</div><div>Query originating this dataset: query=select * where ( atomsymbol = 'ne' and ioncharge <= 6 and ioncharge >= 4 ); </div><div> Data source version: 2017-01-30</div><div>Data format: XSAMS 12.07</div><div>Query uuid in VAMDC query store: 579afd3a-9506-447c-9d76-f294c5b9bd38</div>
VAMDC extraction with identifier = 579afd3a-9506-447c-9d76-f294c5b9bd38
<div>This is a dataset extracted from http://tipbase.obspm.fr/12.07/vamdc/tap/ VAMDC node.</div><div>Query originating this dataset: query=select * where ( atomsymbol = 'ne' and ioncharge <= 6 and ioncharge >= 4 ); </div><div> Data source version: 2017-01-30</div><div>Data format: XSAMS 12.07</div><div>Query uuid in VAMDC query store: 579afd3a-9506-447c-9d76-f294c5b9bd38</div>
Extracted opinions from the French National Great Debate (Grand Débat National) about transport and socio-economic description of municipalities participating to the debate
<p>This data set contains :</p> <p>- motifs_with_geom.json : extracted propositions from the answers to the online public consultation related to the French National Great Debate (Grand Débat National) which took place in 2019. The propositions consist of automatically extracted patterns. The propositions are related to transport. Extracted propositions are also georeferenced by postal codes and associated coordinates (WGS 84).</p> <p>- communes_with_all_data.geojson : socio-economic features which describes municipalities in France (mainland + overseas territories). These data are also georeferenced by postal codes and associated coordinates (WGS 84). data_geo.zip are the source files to build this file. The features are described in data_cadrage_meta.csv</p> <p>- motifsxcommunes_without_geom.csv : joined file of extracted propositions and socio-economic features of municipalities, without coordinates. These data are used to performed statistic analysis.</p> <p>- transportonto4.owl : ontology describing transport in French, used to extract propositions related to transport.</p>
Intermediate data files from the compilation of Economy-wide Material Flow Accounts for the Domestic Extraction of abiotic materials
<p>These files represent a selection of intermediary files from the compilation of material flow accounts on Domestic Extraction (DE) of abiotic materials. The main output of this compilation has been integrated in the UNEP IRP Global Material Flow Database (GMFD).</p> <p>These files include input files (e.g. IDs for data harmonization, or factors for data conversion), as well as output files (e.g. supplementary information, or detailed data accounts before aggregation and integration into the GMFD).</p> <p>Please note: These files are published exclusively for the purpose of making this information available to interested parties in a transparent and orderly fashion, in particular to other research projects who may have use for it. Therefore, these files are not associated with any publication and have not been adjusted or formatted with regard to any publications standards, i.e. they are uploaded exactly as they have been processed in the respective R Github repository of the underlying data compilation.</p> <p>The following description attempts to give a short overview of the respective types of files and their contents. For more detailed information on the data compilation, please refer to chapters 6, 8, and 10 in the <a href="https://resourcepanel.org/sites/default/files/irp_technical_annex_global_material_flows_database.pdf">technical report</a> of the GMFD.</p> <p> </p> <p><strong>Main data output (i.e. detailed material flow accounts)</strong></p> <p><em>DE_met_min_fos_CCC_2021-11-04.csv</em>: Data aggregated to the official categories (CCC/TCCC) used for integration into the GMFD. With IDs, without names (e.g. for materials and countries).</p> <p><em>DE_met_min_fos_CCC_with_names_2021-11-04.csv</em>: Same as above, but with names.</p> <p><em>DE_met_min_fos_Detailed_2021-11-04.csv</em>: Detailed accounts, as compiled, before final aggregation. With IDs, without names.</p> <p><em>DE_met_min_fos_Detailed_with_names_2021-11-04.csv</em>: Same as above, but with names.</p> <p> </p> <p><em>DE_met_min_fos_Detailed_2022-05-24.csv: </em>Slightly revised version from May 2022. But not included in current GMFD version.</p> <p> </p> <p><strong>ID and concordance tables</strong></p> <p><em>ccc_vs_mat_ids.csv:</em> Concordance table for allocation of detailed material accounts to aggregated CCC accounts.</p> <p><em>country_ids.csv:</em> General ID table for country IDs and names</p> <p><em>estimated_ids.csv:</em> Material IDs which are assigned during application of ore estimation factors.</p> <p><em>geo_exist.csv:</em> Table for consistent geographic adjustment of data for specific countries which have disintegrated over time (not including regions like Germany, Yemen, Ethiopia, Sudan, which all have been dealt with individually if necessary).</p> <p><em>material_ids.csv:</em> General ID table for material IDs and names.</p> <p><em>source_country_ids.csv:</em> Concordance table for country IDs (i.e. for allocation of harmonized IDs to source namings/IDs)</p> <p><em>source_material_ids.csv:</em> Concordance table for material IDs (i.e. for allocation of harmonized IDs to source namings/IDs)</p> <p><em>source_unit_ids.csv:</em> Concordance table for unit IDs (i.e. for allocation of harmonized IDs to source namings/IDs)</p> <p><em>unit_ids.csv:</em> General ID table for unit IDs and names.</p> <p> </p> <p><strong>Conversion factors</strong></p> <p><em>conversion_elements.csv:</em> Factors applied for conversion from metal compounds to elemental metals.</p> <p><em>conversion_factors_units.csv:</em> Factors applied for unit conversions.</p> <p> </p> <p><strong>Ore estimation factors</strong></p> <ul> <li>These factors represent content-to-ore ratios (i.e. "tonnes of extracted ore/mineral per ton of produced content”).</li> </ul> <p><em>all_integrated_est_fac_2021-11-04.csv:</em> Factors for estimation of metal and mineral ores (all which have been compiled/integrated)</p> <p><em>applied_est_fac_2021-11-04.csv:</em> Factors for estimation of metal and mineral ores (which have actually been applied)</p> <p><em>average_metal_prices_1990-2020.csv:</em> Metal prices applied in the compilation of estimation ratios.</p> <p><em>raw_metal_to_ore_ratios_fineprint.csv:</em> Raw metal-to-ore ratios derived from FINEPRINT mining data.</p> <p><em>raw_metal_to_ore_ratios_snl.csv:</em> Raw metal-to-ore ratios derived from SNL mining data.</p> <p> </p> <p><strong>Intermediate files from data processing</strong></p> <p><em>all_interm_conv_integr_2021-12-13.csv:</em> Harmonized, converted (units & elemental metals), integrated (i.e. without double counting). Before any estimations (ores & construction minerals) and before final cleaning/formatting.</p> <p><em>wmd_bgs_usgs_interm_harmonized_2021-12-13.csv:</em> All harmonized raw data from WMD/BGS/USGS, before any further processing (i.e. with double counting).</p> <p> </p> <p><strong>Comparison data (for mining accounts from FINEPRINT project)</strong></p> <p><em>data_for_comparison_2022-05-24.csv:</em> Data set specifically compiled for comparison with accounts on production of mines from the FINEPRINT project. Has been applied for verification of data published in <em>"Jasansky et al. (2022) An open database on global coal and metal mining"</em>.</p> <ul> <li>Includes all available types of materials which have been reported (e.g. ores and metals and metal compounds) <ul> <li>meaning: also materials which are associated with each other, for example iron ore and iron (and would therefore have been selectively integrated/excluded in the GMFD compilation).</li> </ul> </li> <li>Excludes any double counting for the exact same material from different data sources</li> <li>Harmonized IDs, converted units</li> <li>Includes metal compounds approximated/converted from reported elemental metals (where possible)</li> </ul> <p> </p> <p><strong>materialflows.net</strong></p> <p><em>data_sunburst_material_profiles_20220607.csv:</em> Full detail of data underlying the Sunburst visualization "Global Domestic Extraction in 2019, by material group" in section "Raw Material Profiles" on materialsflow.net. However, for all available years. Includes data on Domestic Extraction of biomass. Includes detail for CCC, MFA13+, MFA4+</p> <p> </p> <p><strong>Outliers</strong></p> <p><em>overview_adjusted_outliers_2021-12-22.csv:</em> Overview of outliers which were adjusted during final formatting.</p> <p> </p> <p><strong>Other</strong></p> <p><em>approximation_tailings_detailed_2021-09-26.csv:</em> Estimation of tailings based on reported amounts of ores/minerals and their respective contents.</p> <p> </p>
Feature Extraction Using Hidden Markov Model for a Phonetic Process
<p>Speech is one of the primary forms of communication among humans. In real life, a dictionary is used to seek the pronunciation of a complex word; but, for computers, this look-up table is called a phonetic dictionary. A speech recognition process tags a word-utterance to its phoneme structure, thereby returning the grapheme representation. However, the speech recognition process is challenging because of the contextual relationship between words and sentences, dependent on speakers’ intentions. Further, factors influencing time, accents, noisy environment, and data security impose accuracy threats. The present research study proposes a new hybrid speech recognition model by considering three significant aspects: sound generation through phonetic representation, sound acoustics for transmission, and sound reception on how the sound is received. These steps are achieved through a speech-to-text model divided into various stages such as noise removal, speech-pause detection, feature extraction through framing, and windowing by adopting Hidden Markov Model (HMM). The implementation is performed on a phonetic tool, Praat. The robustness of the model is estimated using evaluation metrics such as f-measure and accuracy, resulting in 98% and 99% scores, respectively. Thus, the proposed approach efficiently transforms the spoken words into their corresponding text.</p>
Extraction and Analysis of Fictional Character Networks: A Survey
<p><strong>Description. </strong>Resources used in our survey of character network extraction and analysis methods. This version of the resources does not correspond to the published paper, but to a later update used in the (longer) arXiv/HAL version of the paper. The resources used in the original published paper are provided in v1.0.0.</p> <p><strong>Updates. </strong>The latest version of the data (especially the bibliographic table) is available on a GitHub page, which is easier to update: <a href="https://compnet.github.io/CharNetReview/">https://compnet.github.io/CharNetReview/</a>. This Zenodo repository is no longer updated.</p> <p><strong>Citation. </strong>If you use these data or figures, please cite the following paper:</p> <ul> <li>V. Labatut and X. Bost, “<em>Extraction and Analysis of Fictional Character Networks: A Survey</em>,” ACM Computing Surveys 52(5):89, 2019. ⟨<a href="https://hal.archives-ouvertes.fr/hal-02173918">hal-02173918</a>⟩ DOI: <a href="http://doi.org/10.1145/3344548">10.1145/3344548</a></li> </ul> <p><br><code>@Article{Labatut2019,</code><br><code> author = {Labatut, Vincent and Bost, Xavier},</code><br><code> title = {Extraction and Analysis of Fictional Character Networks: A Survey},</code><br><code> journal = {ACM Computing Surveys},</code><br><code> year = {2019},</code><br><code> volume = {52},</code><br><code> number = {5},</code><br><code> pages = {89},</code><br><code> doi = {10.1145/3344548},</code><br><code>}</code></p>
Extracted and Anonymised Qualitative Data on Students' Acceptance of an Early Warning System
<p>The data published in this record was adopted in the following study: </p> <p><em><strong>Exploring Higher Education students' experience with AI-powered educational tools: The case of an Early Warning System </strong></em></p> <p>The study analyses the students' experience of an early warning system developed at a fully online university. The study is based on 21 semi-structured interviews that yielded a corpus of 21,761 words, for which a mixed inductive and deductive codification approach was applied after thematic analysis. We focused on 11 themes, 52 subthemes, and 396 coded segments to perform content analysis. Our findings revealed that the students, primarily senior workers with a high-level academic self-efficacy, had little experience with this type of system and low expectations about it. However, a usage experience triggered interest and meaningful reflections on the mentioned tool. Nevertheless, a comparative analysis between disciplines related to Computer Science and Economics showed higher confidence and expectation about the system and artificial intelligence overall by the first group. These results highlight the relevance of supporting students' further experiences and understanding of artificial intelligence systems in education to accept them and mainly to participate in iterative development processes of such tools to achieve quality, relevance, and fairness.</p> <p>The three records attached as part of the dataset include:</p> <p>1- The General CodeTree with exemplar coding excerpts in Spanish<br> 2- Extract of transcriptions in English<br> 3- Full Report in Spanish as extracted from NVIVO, including the extracted codes for the synthesis (1,2) in blue, and the comments made by the two researchers engaged in the interrater agreement.<br> 4- General Content Analysis (Spreadsheet ODS)</p> <p> </p>
Dataset of the deleted DOIs extracted from the difference set between Crossref DOIs as of March 2017 and January 2021
<p><strong>Abstract</strong></p> <p>Digital Object Identifiers (DOIs) are regarded as persistent; however, they are sometimes deleted. Deleted DOIs are an important issue not only for persistent access to scholarly content but also for bibliometrics, because they may cause problems in correctly identifying scholarly articles. However, little is known about how much of deleted DOIs and what causes them. We identified deleted DOIs by comparing the datasets of all Crossref DOIs on two different dates, investigated the number of deleted DOIs in the scholarly content along with the corresponding document types, and analyzed the factors that cause deleted DOIs. Using the proposed method, 708,282 deleted DOIs were identified. The majority corresponded to individual scholarly articles such as journal articles, proceedings articles, and book chapters. There were cases of many DOIs assigned to the same content, e.g., retracted journal articles and abstracts of international conferences. We show the publishers and academic societies which are the most common in deleted DOIs. In addition, the top cases of single scholarly content with a large number of deleted DOIs were revealed. The findings of this study are useful for citation analysis and altmetrics, as well as for avoiding deleted DOIs.</p> <p> </p> <p><strong>Data Records</strong></p> <p>The data format of the dataset is JSON lines, where each line is a single record. In this dataset, we identified the deleted DOIs by from the difference set between Crossref DOIs as of March 2017 and January 2021. We note that the file "00_Non-Crossref_DOIs.jsonl.gz" is not deleted DOIs but other files are deleted DOIs. Please refer the conference paper shown in the references for details. Sample of the record is the following.</p> <ul> <li>doi -- DOI name (String), e.g., "10.xxxx/xxxx"</li> <li>whichRA -- Registration agency name or error message for the DOI name according to the “<a href="https://www.doi.org/factsheets/DOIProxy.html#whichra">whichRA?</a>.” (String). "Airiti," "Crossref," "DOI does not exist," "DataCite," "KISTI," "Public," or "mEDRA."</li> <li>redirects -- Redirected URIs for the DOI name obtained by curl command (Array of String), e.g., ["https://doi.org/10.1001/archinte.166.4.387","http://archinte.jamanetwork.com/article.aspx?doi=10.1001/archinte.166.4.387"]</li> <li>redirect_to_other_doi -- The other DOI when the DOI link redirects to. (Array of String), e.g., "[10.1001/archinte.166.4.387]"</li> <li>timestamp -- Date the data was retrieved (Datetime), "2022-01-30T02:10:45Z"</li> <li>label -- The group where the DOI belongs to. Alias DOIs," "DOIs with Deleted Description in Metadata," "DOIs without Redirects," "Defunct DOIs," "Non-Crossref DOIs," "Non-existing DOIs," or "Other DOIs."</li> </ul> <p>As for the file "04_DOIs_with_Deleted_Description_on_Metadata.jsonl.gz," additional records are available as follows.</p> <ul> <li>alias_doi -- Alias DOI name, the same as the value of "doi." (String), e.g., "10.1007/bf00400428."</li> <li>primary_doi -- Primary DOI name for the alias DOI name, the same as the first value of "redirect_to_other_doi". (String), e.g., "10.1007/bf00400429"</li> <li>container_title_of_alias_doi -- the container title for the alias DOI according to the Crossref REST API. e.g., "CrossRef Listing of Deleted DOIs."</li> <li>title_of_alias_doi -- the title for the alias DOI according to the Crossref REST API. e.g., "CrossRef Listing of Deleted DOIs."</li> <li>container_title_of_primary_doi -- the container title for the primary DOI according to the Crossref REST API. e.g., "CrossRef Listing of Deleted DOIs."</li> <li>title_of_alias_doi -- the title for the primary DOI according to the Crossref REST API. e.g., "CrossRef Listing of Deleted DOIs."</li> </ul> <p><strong>References</strong></p> <ul> <li>Kikkawa, J., Takaku, M. & Yoshikane, F. "Analysis of the deletions of DOIs: What factors undermine their persistence and to what extent?", Proceedings of the 26th International Conference on Theory and Practice of Digital Libraries (<a href="http://tpdl2022.dei.unipd.it/"><em>TPDL 2022</em></a>), (to appear), 2022.</li> </ul> <p><strong>FUNDING</strong></p> <ul> <li>JSPS KAKENHI Grant Number <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-21K21303">JP21K21303</a>, <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-22K18147">JP22K18147</a>, <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-20K12543">JP20K12543</a>, and <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-21K12592">JP21K12592</a></li> </ul>
Molecular sidebands for radioactive ion beam extraction
<p>Summary of potential radioactive molecular ion beams seen and predicted from ISOL targets and ion sources including data compiled from [1] and [2]. Some of the molecules (e.g. the transition metal carbonyl or oxide compounds or COTe) are conceptual and would require ion source developments. The actinides other than U are predicted to form compounds with a variety of valence states and require beam developments. Plasma or electron bombardment ion sources may cause molecular fragmentation and change availability of molecular sidebands.</p> <p>[1] U. Köster et al., <em>NIMB </em><strong>266</strong>, 10.1016/J.NIMB.2008.05.152 (2008)</p> <p>[2] J. Ballof, <em>Radioactive Molecular Beams at CERN-ISOLDE,</em> PhD thesis, JGU Mainz (2021)</p>
References extracted from an archived version of the hyperfiction novel "The Unknown"
<p>This repository contains reference data extracted from an archived version of the hyperfiction novel "The Unknown". [1] Archiving was done with `wget`, data was written to a WARC file. We can't provide the WARC file here due to copyright reasons, but we're happy to provide the WARC file upon request, e.g. if you want to reproduce our findings starting from the original WARC.</p> <p>All reference data has been extracted using warc2graph, a Python package available via the Python Package Index and on Github: <a href="https://github.com/dla-marbach/warc2graph/">https://github.com/dla-marbach/warc2graph/</a> . This software is under development, extraction for this study was done with <a href="https://pypi.org/project/warc2graph/0.1.1/">version 0.1.1</a>.</p> <p>We use the term reference data as warc2graph extracts more information than what is defined as a link in the HTML 5 specification. For more info about the data model see <a href="https://elmcip.net/node/16380">(Schlesinger, Blessing, Hein, Ulrich 2021)</a>. [2]</p> <p>We provide the following files, which are all derivatives of the original WARC file. Gephi files contain layout decisions and reduced data according to our research question and as presented in our presentation of this study at <a href="https://dh2022.adho.org/">DH 2022</a>.</p> <ul> <li><strong>extracted-references.complete.the-unknown.hypertext-novel.gexf</strong>: full reference data extracted with warc2graph in GEXF format.</li> <li><strong>extracted-references.internal-only.the-unknown.hypertext-novel.gephi</strong>: internal links only (= no links pointing to sites other than unknownhypertext.com), Gephi format.</li> <li><strong>extracted-references.internal-only.no-navigation.the-unknown.hypertext.novel.gephi</strong>: internal links only, minus nodes that mirror the navigation menu on the bottom of very many pages, Gephi format.</li> <li><strong>external_links</strong>: list of outgoing links, plain text format</li> </ul> <p> </p> <p>[1] Gillespie, William, Scott Rettberg, and Dirk Stratton. ‘The Unknown’, 2002 1998. <a href="https://unknownhypertext.com/">https://unknownhypertext.com/</a>.</p> <p>[2] Schlesinger, Claus-Michael, Mona Ulrich, André Blessing, and Pascal Hein. ‘Networks of Net Literature - Modelling, Extracting and Visualizing Link-Based Networks in the DLA Corpus of Net Literature’. Bergen: ELMCIP, 2021. https://elmcip.net/node/16380.</p>
Extracting Session Keys From the Main Memory Using Brute-force and Machine Learning
<p>This dataset contains:</p> <ol> <li>Heap dump of three version of OpenSSH (V_7_9_P1, V_8_0_P1 and V_8_1_P1)</li> <li>Heap dump of two applications that uses TLS (lynx and curl)</li> <li>Network recording in format of pcap</li> <li>JSON file that contains the keys' information</li> </ol> <p>The source code is available at: https://github.com/smartvmi/SSH-TLS-key-extraction</p>
Extracting abundance information from DNA-based data
<p><span><span><span><span>The accurate extraction of species-abundance information from DNA-based data (metabarcoding, metagenomics) could contribute usefully to the reconstruction of diets and quantitative foodwebs, the inference of species interactions, the modelling of population dynamics and species distributions, the biomonitoring of environmental state and change, and the inference of false positives and negatives. However, capture bias, capture noise, species pipeline biases, and pipeline noise all combine to inject error into DNA-based datasets. This review focuses on methods for correcting the latter two error sources, as the first two are addressed extensively in the ecological survey literature. To extract abundance information from DNA-based data, it is useful to distinguish two concepts. (1) <em>Across</em>-species quantification describes relative species abundances within a single sample. (2) In contrast, <em>within</em>-species quantification describes how the abundance of each individual species varies across samples, where the samples could be a time series, an environmental gradient, or different experimental treatments. In the first part of this paper, we review methods to remove species pipeline biases and pipeline noise. In the second part, we provide a detailed protocol and demonstrate experimentally how to use a 'DNA spike-in' (an internal standard) to remove pipeline noise and recover within-species abundance information.</span></span></span></span></p>
Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning
<p>This repository contains resources developed for the paper: Gupta, V., Zhang, S., Vempala, A., He, Y., Choji, T., Srikumar V., Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning. In: Proceeding of the The Association of Computational Linguistic 2022 (ACL ’22), May 2022".</p> <p><strong>I</strong>t includes the relevant rows marking for the train set of the InfoTabS dataset (<a href="https://infotabs.github.io/">https://infotabs.github.io/</a>) Gupta et. al. 2020 [1]. </p> <p>We followed the protocol of Gupta et al. (2022) [2] which annotated the development and test sets (alpha1, alpha2, alpha3) sets: one table and three distinct hypotheses formed a HIT. We divide the tasks equally into 110 batches, each batch having 51 HITs each having three examples. In total, we collected 81,282 annotations from 90 distinct annotators. </p> <p>Overall, twenty five annotators completed over 1000 tasks, corresponding to 87.75 % of the examples, indicating a tail distribution with the annotations. Overall, 16,248 training set table-hypothesis pairs were successfully labeled with the evidence rows. On average, we obtain 89.49% F1-score with equal precision and recall for annotation agreement when compared with majority vote. It also includes an annotation template used on the mTurk platform for crowdsourcing. The cited datasets were used in this work. The cited datasets were used in this work.</p> <p>Files to access the annotation follow the below structure:</p> <ul> <li>annotation_batches</li> <li>batches_test: contain final results “.csv” files for all the development and test set batches (taken from Gupta et. al. 2022)</li> <li>batches_train: contain our annotated results “.csv” files for all the train set batches</li> <li>README.md: contain the readme for the annotation batches details</li> <li>main_template_row_relevant.html: content the annotation template used for each HIT i.e. marking the relevant row for each instance</li> <li>annotation_stats.md: Have details of the annotation statistics</li> <li>release_mturk: contain the release batches details i.e. csv for corresponding batches released</li> </ul> <p>Files to recreate the annotation statistics and pre-processed data:</p> <ul> <li>results_test: contain the pre-processed batch csv for dev and test set each batch. In the dev and test set. The integrated one computes the agreement stats for all the batches.(taken from Gupta et. al. 2022)</li> <li>results_train: similar to resutls_train expect contain the pre-processed batch csv for train set.</li> <li>scripts: contain the scripts needed to create the csv in the results_test and results_train sets. The script title denotes the function (the statistic it computes) for the scripts.</li> <li>src: the scripts use these python files to create the relevant statistics.</li> </ul> <p><strong>References:</strong></p> <p>[1] InfoTabS: Inference on Tables as Semi-structured Data, Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, Vivek Srikumar, <a href="https://acl2020.org/">ACL 2020</a></p> <p>[2] Is My Model Using The Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning, Vivek Gupta, Riyaz A. Bhat, Atreya Ghosal, Manish Srivastava, Maneesh Singh, Vivek Srikumar, <a href="https://transacl.org/index.php/tacl">TACL 2022</a>, presented at <a href="https://www.2022.aclweb.org/">ACL 2022</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.