cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14192591.v2
title: Table_2

description: Table_2 represents the available projections obtained in the main manuscript file under Table 2.
DOI: 10.6084/m9.figshare.14192591.v1
title: Table_2

description: Table_2 represents the available projections obtained in the main manuscript file under Table 2.
DOI: 10.6084/m9.figshare.13635305.v1
title: Data from: Early warning indicators of population collapse in a seasonal environment

description: Bi-seasonal (breeding, <b>b</b>, and <b>non-breeding</b>, <b>nb</b>) abundances (<b>count</b>) from experimental population of <i>Drosophila melanogaster</i> exposed to chronic season-specific habitat loss. Populations were exposed to habitat loss in either the breeding or non-breeding period (<b>seasonT</b>), and were subject to one of two rates of loss: 10% or 20% per generation (<b>lossT</b>). In addition, a subset of individuals were measured from half of the replicate populations (<b>rep</b>; <b>treat_rep</b>). From each individual, measures of locomotion (<b>activity</b>; <b>crosses</b>) were collected using a Drosophila activity monitor, in which the movements of individual flies are autonomously recorded using infrared light beams. These individuals were then frozen, dried, and weighed (<b>weight</b>). <br><b>See readme.txt for definitions of variables in both datasets.<br></b><br>The abundance data has been used to determine whether populations losing breeding and non-breeding habitat decline in different ways and whether the season of decline is detectable in simple vital rates. By combining the abundance and phenotype data, we explored whether the production of trait-based early warning indicators of population collapse differs between populations losing breeding and non-breeding habitat.
DOI: 10.5281/zenodo.4601450
title: Barkbeetle science review

description: Dataset consists of two databases of bark beetle literature review. One for European spruce bark beetle (ESBB) and one for mountain pine beetle (MPB). Databases contain an overview of main papers for research and their classifying to search level B-MR and C.
DOI: 10.5281/zenodo.4601449
title: Barkbeetle science review

description: Dataset consists of two databases of bark beetle literature review. One for European spruce bark beetle (ESBB) and one for mountain pine beetle (MPB). Databases contain an overview of main papers for research and their classifying to search level B-MR and C.
DOI: 10.5281/zenodo.4601292
title: Gridded population maps of Germany from disaggregated census data and bottom-up estimates

description: This dataset features three gridded population dadasets of Germany on a 10m grid. The units are people per grid cell. <strong>Datasets </strong> DE_POP_VOLADJ16: This dataset was produced by disaggregating national census counts to 10m grid cells based on a weighted dasymetric mapping approach. A building density, building height and building type dataset were used as underlying covariates, with an adjusted volume for multi-family residential buildings. DE_POP_TDBP: This dataset is considered a <em>best product</em>, based on a dasymetric mapping approach that disaggregated municipal census counts to 10m grid cells using the same three underyling covariate layers. DE_POP_BU: This dataset is based on a bottom-up gridded population estimate. A building density, building height and building type layer were used to compute a living floor area dataset in a 10m grid. Using federal statistics on the average living floor are per capita, this bottom-up estimate was created. Please refer to the related publication for details. <strong>Temporal extent</strong> The building density layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: http://doi.org/10.1594/PANGAEA.920894) The building height layer is representative for ca. 2015 (doi: 10.5281/zenodo.4066295) The building types layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: 10.5281/zenodo.4601219) The underlying census data is from 2018. <strong>Data format</strong> The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. <strong>Further information</strong> For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de).<br> A web-visualization of this dataset is available here. <strong>Publication</strong> Schug, F., Frantz, D., van der Linden, S., & Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLOS ONE. DOI: 10.1371/journal.pone.0249044 <strong>Acknowledgements</strong> Census data were provided by the German Federal Statistical Offices. <strong>Funding</strong><br> This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).
DOI: 10.5281/zenodo.4601291
title: Gridded population maps of Germany from disaggregated census data and bottom-up estimates

description: This dataset features three gridded population dadasets of Germany on a 10m grid. The units are people per grid cell. <strong>Datasets </strong> DE_POP_VOLADJ16: This dataset was produced by disaggregating national census counts to 10m grid cells based on a weighted dasymetric mapping approach. A building density, building height and building type dataset were used as underlying covariates, with an adjusted volume for multi-family residential buildings. DE_POP_TDBP: This dataset is considered a <em>best product</em>, based on a dasymetric mapping approach that disaggregated municipal census counts to 10m grid cells using the same three underyling covariate layers. DE_POP_BU: This dataset is based on a bottom-up gridded population estimate. A building density, building height and building type layer were used to compute a living floor area dataset in a 10m grid. Using federal statistics on the average living floor are per capita, this bottom-up estimate was created. Please refer to the related publication for details. <strong>Temporal extent</strong> The building density layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: http://doi.org/10.1594/PANGAEA.920894) The building height layer is representative for ca. 2015 (doi: 10.5281/zenodo.4066295) The building types layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: 10.5281/zenodo.4601219) The underlying census data is from 2018. <strong>Data format</strong> The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. <strong>Further information</strong> For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de).<br> A web-visualization of this dataset is available here. <strong>Publication</strong> Schug, F., Frantz, D., van der Linden, S., & Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLOS ONE. DOI: 10.1371/journal.pone.0249044 <strong>Acknowledgements</strong> Census data were provided by the German Federal Statistical Offices. <strong>Funding</strong><br> This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).
DOI: 10.6084/m9.figshare.14204288
title: ATAC_Day15.bw

description: ATAC_Day15
DOI: 10.6084/m9.figshare.14204288.v1
title: ATAC_Day15.bw

description: ATAC_Day15
DOI: 10.25919/282j-4g24
title: Parkes observations for project P595 semester 2020OCTS_15

description: We propose to continue the PULSE@Parkes project in which high school students from Australia and around the World use the Parkes radio telescope in a remote observing model to observe and analyse pulsar data. The data from some PULSE@Parkes observations are used to support the FERMI Gamma ray mission, other observations supplement the P456 Pulsar Timing Array project and the remainder were chosen in order to make a detailed analysis of pulsar timing irregularities and intermittency.
DOI: 10.6084/m9.figshare.14204234
title: RNA_Primary.bw

description: lung RNA<br>
DOI: 10.6084/m9.figshare.14204234.v1
title: RNA_Primary.bw

description: lung RNA<br>
DOI: 10.5281/zenodo.3232844
title: Experimental data and benchmarks of the paper "Merge-and-Shrink Task Reformulation for Classical Planning"

description: This dataset contains both benchmarks and data used in the paper. PDDL benchmark files can be found in the file optimal-strips-benchmarks.tar.gz<br> and satisficing-strips-benchmarks.tar.gz, which contain the STRIPS domains from<br> the sequential optimal/satisficing tracks of all IPCs as collected in the<br> repository https://bitbucket.org/aibasel/downward-benchmarks. All other files in this dataset contain raw and processed data of all<br> experiments, which were generated using Downward-Lab (see<br> https://doi.org/10.5281/zenodo.399255). The scripts used to run the experiments<br> can be found in the software bundle for this paper (see<br> https://doi.org/10.5281/zenodo.3232878). Directories without the "-eval" ending contain raw data, distributed over a<br> subdirectory for each experiment. Each of these contain a subdirectory tree<br> structure "runs-*" where each planner run has its own directory. For each run,<br> there are symbolic links to the input PDDL files domain.pddl and problem.pddl<br> (can be resolved by putting the benchmarks directory to the right place), the<br> run log file "run.log" (stdout), possibly also a run error file "run.err"<br> (stderr), the run script "run" used to start the experiment, and a "properties"<br> file that contains data parsed from the log file(s). Directories with the "-eval" ending contain a "properties" file, which contains<br> a JSON directory with combined data of all runs of the corresponding<br> experiment. In essence, the properties file is the union over all properties<br> files generated for each individual planner run.
DOI: 10.5281/zenodo.3232843
title: Experimental data and benchmarks of the paper "Merge-and-Shrink Task Reformulation for Classical Planning"

description: This dataset contains both benchmarks and data used in the paper. PDDL benchmark files can be found in the file optimal-strips-benchmarks.tar.gz<br> and satisficing-strips-benchmarks.tar.gz, which contain the STRIPS domains from<br> the sequential optimal/satisficing tracks of all IPCs as collected in the<br> repository https://bitbucket.org/aibasel/downward-benchmarks. All other files in this dataset contain raw and processed data of all<br> experiments, which were generated using Downward-Lab (see<br> https://doi.org/10.5281/zenodo.399255). The scripts used to run the experiments<br> can be found in the software bundle for this paper (see<br> https://doi.org/10.5281/zenodo.3232878). Directories without the "-eval" ending contain raw data, distributed over a<br> subdirectory for each experiment. Each of these contain a subdirectory tree<br> structure "runs-*" where each planner run has its own directory. For each run,<br> there are symbolic links to the input PDDL files domain.pddl and problem.pddl<br> (can be resolved by putting the benchmarks directory to the right place), the<br> run log file "run.log" (stdout), possibly also a run error file "run.err"<br> (stderr), the run script "run" used to start the experiment, and a "properties"<br> file that contains data parsed from the log file(s). Directories with the "-eval" ending contain a "properties" file, which contains<br> a JSON directory with combined data of all runs of the corresponding<br> experiment. In essence, the properties file is the union over all properties<br> files generated for each individual planner run.
DOI: 10.6084/m9.figshare.14204237.v1
title: RNA_NGPT.bw

description: lung
DOI: 10.6084/m9.figshare.14204237
title: RNA_NGPT.bw

description: lung
DOI: 10.5281/zenodo.2628693
title: Experimental data of the paper "Counterexample-Guided Abstraction Refinement for Pattern Selection in Optimal Classical Planning"

description: This dataset contains experimental data used in the paper. They were generated<br> using Downward-Lab (see https://doi.org/10.5281/zenodo.399255). The scripts<br> used to run the experiments can be found in the software bundle for this paper<br> (see https://doi.org/10.5281/zenodo.2628703). Directories without the "-eval" ending contain raw data, distributed over a<br> subdirectory for each experiment. Each of these contain a subdirectory tree<br> structure "runs-*" where each planner run has its own directory. For each run,<br> there are symbolic links to the input PDDL files domain.pddl and problem.pddl<br> (can be resolved by putting the benchmarks directory to the right place), the<br> run log file "run.log" (stdout), possibly also a run error file "run.err"<br> (stderr), the run script "run" used to start the experiment, and a "properties"<br> file that contains data parsed from the log file(s). Directories with the "-eval" ending contain a "properties" file, which contains<br> a JSON directory with combined data of all runs of the corresponding<br> experiment. In essence, the properties file is the union over all properties<br> files generated for each individual planner run.
DOI: 10.5281/zenodo.2628692
title: Experimental data of the paper "Counterexample-Guided Abstraction Refinement for Pattern Selection in Optimal Classical Planning"

description: This dataset contains experimental data used in the paper. They were generated<br> using Downward-Lab (see https://doi.org/10.5281/zenodo.399255). The scripts<br> used to run the experiments can be found in the software bundle for this paper<br> (see https://doi.org/10.5281/zenodo.2628703). Directories without the "-eval" ending contain raw data, distributed over a<br> subdirectory for each experiment. Each of these contain a subdirectory tree<br> structure "runs-*" where each planner run has its own directory. For each run,<br> there are symbolic links to the input PDDL files domain.pddl and problem.pddl<br> (can be resolved by putting the benchmarks directory to the right place), the<br> run log file "run.log" (stdout), possibly also a run error file "run.err"<br> (stderr), the run script "run" used to start the experiment, and a "properties"<br> file that contains data parsed from the log file(s). Directories with the "-eval" ending contain a "properties" file, which contains<br> a JSON directory with combined data of all runs of the corresponding<br> experiment. In essence, the properties file is the union over all properties<br> files generated for each individual planner run.
DOI: 10.6084/m9.figshare.14204240
title: ATAC_IAEC2.bw

description: lung
DOI: 10.6084/m9.figshare.14204243
title: ATAC_Primary.bw

description: lung
DOI: 10.6084/m9.figshare.14204243.v1
title: ATAC_Primary.bw

description: lung
DOI: 10.5281/zenodo.4596751
title: NPTEL2020-Indian-English-Speech-Test-part3

description: English Speech Recognition dataset collected from NPTEL videos. For more information, check:<br> https://github.com/AI4Bharat/NPTEL2020-Indian-English-Speech-Dataset
DOI: 10.5281/zenodo.4596750
title: NPTEL2020-Indian-English-Speech-Test-part3

description: English Speech Recognition dataset collected from NPTEL videos. For more information, check:<br> https://github.com/AI4Bharat/NPTEL2020-Indian-English-Speech-Dataset
DOI: 10.6084/m9.figshare.14204249
title: ATAC_NGPT.bw

description: lung
DOI: 10.6084/m9.figshare.14204249.v1
title: ATAC_NGPT.bw

description: lung
DOI: 10.5281/zenodo.2621424
title: Experimental data and benchmarks used in the paper "Theoretical Foundations for Structural Symmetries of Lifted PDDL Tasks"

description: This dataset contains both benchmarks and data used in the paper. PDDL benchmark files can be found in the files benchmarks.tar.gz and<br> bagged-benchmarks.tar.gz. The former contains all domains from all IPCs from the<br> repository https://bitbucket.org/aibasel/downward-benchmarks, without duplicate<br> domains that have been used in multiple IPCs. The latter contains the subset of<br> these tasks for which the reformulation in the "bagged representation" from the<br> following paper succeeded: Riddle, P.; Douglas, J.; Barley, M.; and Franco, S. 2016. Improving<br> performance by reformulating PDDL into a bagged representation.<br> In ICAPS 2016 Workshop on Heuristics and Search for Domain-<br> independent Planning, 28–36. We obtained the implementation of the baggy reformluation from the authors. All other files in this dataset contain raw and processed data of all<br> experiments, which were generated using Downward-Lab (see<br> https://doi.org/10.5281/zenodo.399255). The scripts used to run the experiments<br> can be found in the software bundle for this paper (see<br> https://doi.org/10.5281/zenodo.2621897). Directories without the "-eval" ending contain raw data, distributed over a<br> subdirectory for each experiment. Each of these contain a subdirectory tree<br> structure "runs-*" where each planner run has its own directory. For each run,<br> there are symbolic links to the input PDDL files domain.pddl and problem.pddl<br> (can be resolved by putting the benchmarks directory to the right place), the<br> run log file "run.log" (stdout), possibly also a run error file "run.err"<br> (stderr), the run script "run" used to start the experiment, and a "properties"<br> file that contains data parsed from the log file(s). Some directories (not for<br> the ground experiments, where these files got too large) also contain a file<br> generators.py that contain all symmetry group generators in permutation<br> notation. Directories with the "-eval" ending contain a "properties" file, which contains<br> a JSON directory with combined data of all runs of the corresponding<br> experiment. In essence, the properties file is the union over all properties<br> files generated for each individual planner run.
DOI: 10.5281/zenodo.1290644
title: Dataset used in the paper "Merge-and-Shrink Heuristics for Classical Planning: Efficient Implementation and Partial Abstractions"

description: This dataset contains all raw and processed data used in the paper. It has been generated using Downward-Lab (see https://doi.org/10.5281/zenodo.399255). Directories without the "-eval" ending contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are the input PDDL files, domain.pddl and problem.pddl, the compressed output as generated by the translator component of Fast Downward (output.sas.xz), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s). Directories with the "-eval" ending contain a "properties" file, which contains a JSON directory with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run. To process the data further, we used the scripts available in the software bundle of the paper: https://doi.org/10.5281/zenodo.1290524
description: {"references": ["Silvan Sievers. Merge-and-Shrink Heuristics for Classical Planning: Efficient Implementation and Partial Abstractions. SoCS 2018"]}
DOI: 10.5281/zenodo.1290643
title: Dataset used in the paper "Merge-and-Shrink Heuristics for Classical Planning: Efficient Implementation and Partial Abstractions"

description: This dataset contains all raw and processed data used in the paper. It has been generated using Downward-Lab (see https://doi.org/10.5281/zenodo.399255). Directories without the "-eval" ending contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are the input PDDL files, domain.pddl and problem.pddl, the compressed output as generated by the translator component of Fast Downward (output.sas.xz), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s). Directories with the "-eval" ending contain a "properties" file, which contains a JSON directory with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run. To process the data further, we used the scripts available in the software bundle of the paper: https://doi.org/10.5281/zenodo.1290524
description: {"references": ["Silvan Sievers. Merge-and-Shrink Heuristics for Classical Planning: Efficient Implementation and Partial Abstractions. SoCS 2018"]}
DOI: 10.5281/zenodo.2621423
title: Experimental data and benchmarks used in the paper "Theoretical Foundations for Structural Symmetries of Lifted PDDL Tasks"

description: This dataset contains both benchmarks and data used in the paper. PDDL benchmark files can be found in the files benchmarks.tar.gz and<br> bagged-benchmarks.tar.gz. The former contains all domains from all IPCs from the<br> repository https://bitbucket.org/aibasel/downward-benchmarks, without duplicate<br> domains that have been used in multiple IPCs. The latter contains the subset of<br> these tasks for which the reformulation in the "bagged representation" from the<br> following paper succeeded: Riddle, P.; Douglas, J.; Barley, M.; and Franco, S. 2016. Improving<br> performance by reformulating PDDL into a bagged representation.<br> In ICAPS 2016 Workshop on Heuristics and Search for Domain-<br> independent Planning, 28–36. We obtained the implementation of the baggy reformluation from the authors. All other files in this dataset contain raw and processed data of all<br> experiments, which were generated using Downward-Lab (see<br> https://doi.org/10.5281/zenodo.399255). The scripts used to run the experiments<br> can be found in the software bundle for this paper (see<br> https://doi.org/10.5281/zenodo.2621897). Directories without the "-eval" ending contain raw data, distributed over a<br> subdirectory for each experiment. Each of these contain a subdirectory tree<br> structure "runs-*" where each planner run has its own directory. For each run,<br> there are symbolic links to the input PDDL files domain.pddl and problem.pddl<br> (can be resolved by putting the benchmarks directory to the right place), the<br> run log file "run.log" (stdout), possibly also a run error file "run.err"<br> (stderr), the run script "run" used to start the experiment, and a "properties"<br> file that contains data parsed from the log file(s). Some directories (not for<br> the ground experiments, where these files got too large) also contain a file<br> generators.py that contain all symmetry group generators in permutation<br> notation. Directories with the "-eval" ending contain a "properties" file, which contains<br> a JSON directory with combined data of all runs of the corresponding<br> experiment. In essence, the properties file is the union over all properties<br> files generated for each individual planner run.
DOI: 10.6084/m9.figshare.14204252
title: RNA_Day15.bw

description: lung
DOI: 10.6084/m9.figshare.14204252.v1
title: RNA_Day15.bw

description: lung
DOI: 10.6084/m9.figshare.14204255
title: RNA_IAEC2.bw

description: lung
DOI: 10.6084/m9.figshare.14204255.v1
title: RNA_IAEC2.bw

description: lung
DOI: 10.5519/qd.lb5ugm9h
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (15 records)