cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/i57bff
title: Global Register of Introduced and Invasive Species - Sweden

description: The Global Register of Introduced and Invasive Species (GRIIS) presents validated and verified national checklists of introduced (alien) and invasive alien species at the country, territory, and associated island level. Checklists are living entities, especially for biological invasions given the growing nature of the problem. GRIIS checklists are based on a published methodology and supported by the Integrated Publishing Tool that jointly enable ongoing improvements and updates to expand their taxonomic coverage and completeness. Phase 1 of the project focused on developing validated and verified checklists of countries that are Party to the Convention on Biological Diversity (CBD). Phase 2 aimed to achieve global coverage including non-party countries and all overseas territories of countries, e.g. those of the Netherlands, France, and the United Kingdom. All kingdoms of organisms occurring in all environments and systems are covered. Checklists are reviewed and verified by networks of country or species experts. Verified checklists/ species records, as well as those under review, are presented on the online GRIIS website (www.griis.org) in addition to being published through the GBIF Integrated Publishing Tool.
DOI: 10.5281/zenodo.6683892
title: Code, benchmarks and experiment data for the IPC 2018 entry "Delfi: Online Planner Selection for Cost-Optimal Planning"

description: This bundle contains code, scripts and benchmarks for reproducing all experiments reported in the paper. It also contains the data generated for the paper. katz-et-al-icp2018-delfi.zip and katz-et-al-icp2018-delf2.zip contain the implementation based on Fast Downward (the two folders only differ in how the planner selection works). It also contains the experiment scripts (hopefully) compatible with Lab 2.1 for reproducing all experiments of the paper, under experiments/ipc2018. katz-et-al-ipc2018-scripts.zip contain all scripts for the learning pipeline used to train the planner selection models. katz-et-al-icp2018-benchmarks.zip contains the benchmarks. It consists of the IPC benchmarks used in all optimal sequential tracks of IPCs up to 2014 (suite optimal from https://github.com/aibasel/downward-benchmarks). katz-et-al-icp2018-lab.zip contains a copy of Lab 2.1 (https://github.com/aibasel/lab). katz-et-al-icp2018-raw-data.zip and katz-et-al-icp2018-parsed-data.zip contain the experimental data. Directories in katz-et-al-icp2018-raw-data.zip (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 symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), 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 in katz-et-al-icp2018-parsed-data.zip (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. The image data set used for training can be found online: https://github.com/IBM/IPC-image-data Note on license: we chose GPL v3.0 or later mainly because we consider our implementation based on Fast Downward the main contribution of this package, and Fast Downward comes with GPL v3.0. We only include a copy of Lab and the benchmarks for convenience.
DOI: 10.5281/zenodo.6683999
title: Code, benchmarks and experiment data for the AAAI 2019 paper "Deep Learning for Cost-Optimal Planning: Task-Dependent Planner Selection"

description: This bundle contains code, scripts and benchmarks for reproducing all experiments reported in the paper. It also contains the data generated for the paper. Except for the code base, it contains the same files as the Zenodo entry for our IPC 2018 planner Delfi: https://zenodo.org/record/6683892. sievers-et-al-aaai2019-fast-downward.zip contains the implementation based on Fast Downward. It also contains the experiment scripts (hopefully) compatible with Lab 2.1 for reproducing all experiments of the paper, under experiments/ipc2018. While this code base contains a few more scripts used for this AAAI paper compared to the IPC 2018 code, it hasn't been updated to contain, e.g., a requirements.txt file for setting up a Python virtual environment like we did for our IPC 2018 planners. Please see katz-et-al-ipc2018-delfi1.zip and katz-et-al-ipc2018-delfi2.zip in the above mentioned Zenodo entry. sievers-et-al-aaai2019-scripts.zip contain all scripts for the learning pipeline used to train the planner selection models. sievers-et-al-aaai2019-benchmarks.zip contains the benchmarks. It consists of the IPC benchmarks used in all optimal sequential tracks of IPCs up to 2014 (suite optimal from https://github.com/aibasel/downward-benchmarks). sievers-et-al-aaai2019-lab.zip contains a copy of Lab 2.1 (https://github.com/aibasel/lab). sievers-et-al-aaai2019-raw-data.zip and sievers-et-al-aaai2019-parsed-data.zip contain the experimental data. Directories in sievers-et-al-aaai2019-raw-data.zip (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 symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), 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 in sievers-et-al-aaai2019-parsed-data.zip (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. The image data set used for training can be found online: https://github.com/IBM/IPC-image-data Note on license: we chose GPL v3.0 or later mainly because we consider our implementation based on Fast Downward the main contribution of this package, and Fast Downward comes with GPL v3.0. We only include a copy of Lab and the benchmarks for convenience.
DOI: 10.5281/zenodo.6683998
title: Code, benchmarks and experiment data for the AAAI 2019 paper "Deep Learning for Cost-Optimal Planning: Task-Dependent Planner Selection"

description: This bundle contains code, scripts and benchmarks for reproducing all experiments reported in the paper. It also contains the data generated for the paper. Except for the code base, it contains the same files as the Zenodo entry for our IPC 2018 planner Delfi: https://zenodo.org/record/6683892. sievers-et-al-aaai2019-fast-downward.zip contains the implementation based on Fast Downward. It also contains the experiment scripts (hopefully) compatible with Lab 2.1 for reproducing all experiments of the paper, under experiments/ipc2018. While this code base contains a few more scripts used for this AAAI paper compared to the IPC 2018 code, it hasn't been updated to contain, e.g., a requirements.txt file for setting up a Python virtual environment like we did for our IPC 2018 planners. Please see katz-et-al-ipc2018-delfi1.zip and katz-et-al-ipc2018-delfi2.zip in the above mentioned Zenodo entry. sievers-et-al-aaai2019-scripts.zip contain all scripts for the learning pipeline used to train the planner selection models. sievers-et-al-aaai2019-benchmarks.zip contains the benchmarks. It consists of the IPC benchmarks used in all optimal sequential tracks of IPCs up to 2014 (suite optimal from https://github.com/aibasel/downward-benchmarks). sievers-et-al-aaai2019-lab.zip contains a copy of Lab 2.1 (https://github.com/aibasel/lab). sievers-et-al-aaai2019-raw-data.zip and sievers-et-al-aaai2019-parsed-data.zip contain the experimental data. Directories in sievers-et-al-aaai2019-raw-data.zip (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 symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), 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 in sievers-et-al-aaai2019-parsed-data.zip (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. The image data set used for training can be found online: https://github.com/IBM/IPC-image-data Note on license: we chose GPL v3.0 or later mainly because we consider our implementation based on Fast Downward the main contribution of this package, and Fast Downward comes with GPL v3.0. We only include a copy of Lab and the benchmarks for convenience.
DOI: 10.5281/zenodo.6683891
title: Code, benchmarks and experiment data for the IPC 2018 entry "Delfi: Online Planner Selection for Cost-Optimal Planning"

description: This bundle contains code, scripts and benchmarks for reproducing all experiments reported in the paper. It also contains the data generated for the paper. katz-et-al-icp2018-delfi.zip and katz-et-al-icp2018-delf2.zip contain the implementation based on Fast Downward (the two folders only differ in how the planner selection works). It also contains the experiment scripts (hopefully) compatible with Lab 2.1 for reproducing all experiments of the paper, under experiments/ipc2018. katz-et-al-ipc2018-scripts.zip contain all scripts for the learning pipeline used to train the planner selection models. katz-et-al-icp2018-benchmarks.zip contains the benchmarks. It consists of the IPC benchmarks used in all optimal sequential tracks of IPCs up to 2014 (suite optimal from https://github.com/aibasel/downward-benchmarks). katz-et-al-icp2018-lab.zip contains a copy of Lab 2.1 (https://github.com/aibasel/lab). katz-et-al-icp2018-raw-data.zip and katz-et-al-icp2018-parsed-data.zip contain the experimental data. Directories in katz-et-al-icp2018-raw-data.zip (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 symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), 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 in katz-et-al-icp2018-parsed-data.zip (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. The image data set used for training can be found online: https://github.com/IBM/IPC-image-data Note on license: we chose GPL v3.0 or later mainly because we consider our implementation based on Fast Downward the main contribution of this package, and Fast Downward comes with GPL v3.0. We only include a copy of Lab and the benchmarks for convenience.
DOI: 10.5281/zenodo.6697044
title: Climate and Crop variables of tomato greenhouse simulation

description: Climate and growth variables of a simulated tomato greenhouse are shown. Description of columns: ---------------------------------- 'DateTime' : Time Stamp [dd-MM-yyyy hh:mm:ss] 'Cppm' : outdoor Concentration of CO2 [ppm] 'Wind' : wind velocity [m/s] 'HR' : exterior relative humidity [%] 'Rad' : exterior radiation [W/m^2] 'Temp' : outdoor temperature [K] 'Temp__Tcover' : temperature of cover [K] 'Temp__Tair' : temperature of air [K] 'Temp__Tfloor' : temperature of floor [K] 'Temp__Tsoil' : temperature of soil [K] 'QT__QT' : heat loss of crop by evapotranspiration [W] 'QS__R_int' : Indoor Radiation [W/m^2] 'Gas__C_w' : Absolute Humidity [kg/m^3] 'Gas__C_c' : CO2 Concentration [kg/m^3] 'Gas__rho_i' : Air density [kg/m^3] 'Gas__C_c_ppm' : CO2 Concentration [ppm] 'Gas__HRInt' : Indoor relative humidity [%] 'R' : Ratio of ventilation [1/s] 'Windows__value' : percent open window [%] 'Screen__value' : percent open screen [%] 'Carbon__Cbuff' : dry carbon in buffer by square meter of cultivation [kg/m^2] 'Carbon__Cfruit' : dry carbon in fruit by square meter of cultivation [kg/m^2] 'Carbon__Cleaf' : dry carbon in leaf by square meter of cultivation [kg/m^2] 'Carbon__Cstem' : dry carbon in stem by square meter of cultivation [kg/m^2] 'Tsum' : Acumulative temperature [ºC day] 'C_Total' : total dry carbon by square meter of cultivation [kg/m^2] 'WC' : water capacity of crop [kg/m^2] 'LAI' : leaf area index [-] 'CC' : CO2 flux from crop to air 'VPD' : Vapor pressure deficit [Pa] 'Water__WaterFlows__WaterUptake' : Water uptake of crop by square meter of cultivation [kg/(sm^2)] <br>
description: Acknowledgments: The HortiMED project (Grant Number 1915) is part of the PRIMA Programme supported by the European Union.
DOI: 10.5281/zenodo.6697043
title: Climate and Crop variables of tomato greenhouse simulation

description: Climate and growth variables of a simulated tomato greenhouse are shown. Description of columns: ---------------------------------- 'DateTime' : Time Stamp [dd-MM-yyyy hh:mm:ss] 'Cppm' : outdoor Concentration of CO2 [ppm] 'Wind' : wind velocity [m/s] 'HR' : exterior relative humidity [%] 'Rad' : exterior radiation [W/m^2] 'Temp' : outdoor temperature [K] 'Temp__Tcover' : temperature of cover [K] 'Temp__Tair' : temperature of air [K] 'Temp__Tfloor' : temperature of floor [K] 'Temp__Tsoil' : temperature of soil [K] 'QT__QT' : heat loss of crop by evapotranspiration [W] 'QS__R_int' : Indoor Radiation [W/m^2] 'Gas__C_w' : Absolute Humidity [kg/m^3] 'Gas__C_c' : CO2 Concentration [kg/m^3] 'Gas__rho_i' : Air density [kg/m^3] 'Gas__C_c_ppm' : CO2 Concentration [ppm] 'Gas__HRInt' : Indoor relative humidity [%] 'R' : Ratio of ventilation [1/s] 'Windows__value' : percent open window [%] 'Screen__value' : percent open screen [%] 'Carbon__Cbuff' : dry carbon in buffer by square meter of cultivation [kg/m^2] 'Carbon__Cfruit' : dry carbon in fruit by square meter of cultivation [kg/m^2] 'Carbon__Cleaf' : dry carbon in leaf by square meter of cultivation [kg/m^2] 'Carbon__Cstem' : dry carbon in stem by square meter of cultivation [kg/m^2] 'Tsum' : Acumulative temperature [ºC day] 'C_Total' : total dry carbon by square meter of cultivation [kg/m^2] 'WC' : water capacity of crop [kg/m^2] 'LAI' : leaf area index [-] 'CC' : CO2 flux from crop to air 'VPD' : Vapor pressure deficit [Pa] 'Water__WaterFlows__WaterUptake' : Water uptake of crop by square meter of cultivation [kg/(sm^2)] <br>
description: Acknowledgments: The HortiMED project (Grant Number 1915) is part of the PRIMA Programme supported by the European Union.
DOI: 10.5281/zenodo.6697640
title: fg-baseline-model

description: fg-baseline-model
DOI: 10.5281/zenodo.6697639
title: fg-baseline-model

description: fg-baseline-model
DOI: 10.15468/wvocwo
title: Global Register of Introduced and Invasive Species - Netherlands

description: The Global Register of Introduced and Invasive Species (GRIIS) presents validated and verified national checklists of introduced (alien) and invasive alien species at the country, territory, and associated island level. Checklists are living entities, especially for biological invasions given the growing nature of the problem. GRIIS checklists are based on a published methodology and supported by the Integrated Publishing Tool that jointly enable ongoing improvements and updates to expand their taxonomic coverage and completeness. Phase 1 of the project focused on developing validated and verified checklists of countries that are Party to the Convention on Biological Diversity (CBD). Phase 2 aimed to achieve global coverage including non-party countries and all overseas territories of countries, e.g. those of the Netherlands, France, and the United Kingdom. All kingdoms of organisms occurring in all environments and systems are covered. Checklists are reviewed and verified by networks of country or species experts. Verified checklists/ species records, as well as those under review, are presented on the online GRIIS website (www.griis.org) in addition to being published through the GBIF Integrated Publishing Tool.
DOI: 10.25919/a7ba-6z83
title: Parkes observations for project P1054 semester 2022APRS_10

description: With this proposal we ask time to continue our timing follow-up campaign of pulsars discovered with the MeerKAT telescope. Fourteen sources have been discovered in targeted observations of Fermi unidentified point sources, three in the Small Magellanic Cloud, 37 in Globular Clusters and 34 in a survey of the Galactic plane. A large fraction of the new discoveries are recycled pulsars (including at least one double neutron star system and several 'spider' binaries), or young pulsars. Timing observations have an essential role in exploiting the full potential of any pulsar discovery, allowing the precise measurement of rotational, astrometric and orbital parameters which, in turn, give us powerful tools to improve our understanding of the physics in extreme environments as well as of the population of neutron stars as a whole. The UWL receiver of the Parkes telescope is a sensitive, versatile instrument that is allowing us to successfully time these new sources, in the bright-end of TRAPUM discoveries. The UWL has also the unique advantage of allowing detailed studies of the eclipses of the so-called 'spider' pulsars, which make up about 1/3rd of the sources we aim at following-up with this project.
DOI: 10.5281/zenodo.6697795
title: Optimization and evaluation of a two-stage chromogenic tenase assay for measurement of emicizumab plasma levels

description: A two-stage chromogenic tenase assay allows specific measurement of emicizumab plasma levels over a broad concentration range (10 µg/ml to 100 µg/ml). The assay showed a reasonable agreement with one-stage clotting assy used for emicizumab quantificaition and can be applied on an automated coagulation analyzer, demonstrating its applicability within a routine laboratory setting.
DOI: 10.5281/zenodo.6697794
title: Optimization and evaluation of a two-stage chromogenic tenase assay for measurement of emicizumab plasma levels

description: A two-stage chromogenic tenase assay allows specific measurement of emicizumab plasma levels over a broad concentration range (10 µg/ml to 100 µg/ml). The assay showed a reasonable agreement with one-stage clotting assy used for emicizumab quantificaition and can be applied on an automated coagulation analyzer, demonstrating its applicability within a routine laboratory setting.
DOI: 10.6084/m9.figshare.20109377
title: Petri dishes digital images dataset of e.coli, s.aureus and p.aeruginosa.

description: Database of Petri plates with <em>e.coli</em>,<em>p.aeruginosa and s.aureus </em>cataloged in this way: attributing number, bacterial species and total manual count to each image, e.g., CEMTimages_0278_EC_C76, where 0278, EC and C76 meaning the image number, bacterial species and total count, respectively. The built dataset consisted of about 1150 labeled images with approximately the same number of images for each bacterial species to attain a balanced database. More info check: <strong>https://doi.org/10.3390/bioengineering9070271</strong>
DOI: 10.5281/zenodo.5647409
title: Parkinsonian syndromes in motor neuron disease: a clinical study

description: Raw data of the manuscript "Parkinsonian syndromes in motor neuron disease: a clinical study". doi: 10.3389/fnagi.2022.917706
DOI: 10.5281/zenodo.5647408
title: Parkinsonian syndromes in motor neuron disease: a clinical study

description: Raw data of the manuscript "Parkinsonian syndromes in motor neuron disease: a clinical study". doi: 10.3389/fnagi.2022.917706
DOI: 10.48420/19978016
title: Data and data dictionary for "Links between Personality, Affective Cognition, Emotion Regulation and Affective Disorders"

description: Vulnerability to anxiety and depressive disorders is affected by risk and resilience factors, such as personality, use of emotion regulation strategies, and affective cognition. Previous research has identified personality constructs best explaining variance in anxiety and depression (Lyon et al, 2020; 2021), however the mediating mechanisms are unknown. This study aimed to investigate the mediating roles of emotion regulation strategies and affective cognition in the relationship between personality constructs and affective disorders. Data were collected from a sample of 276 students and staff at the University of Manchester. Measures included both broad and narrow Big Five personality constructs; COPE Inventory strategies; a dot-probe task to measure attentional biases to emotional information; both a questionnaire and a computerised cognitive to measure interpretation of emotional images; and measures of anxiety and depression.
DOI: 10.48420/19978016.v3
title: Data and data dictionary for "Links between Personality, Affective Cognition, Emotion Regulation and Affective Disorders"

description: Vulnerability to anxiety and depressive disorders is affected by risk and resilience factors, such as personality, use of emotion regulation strategies, and affective cognition. Previous research has identified personality constructs best explaining variance in anxiety and depression (Lyon et al, 2020; 2021), however the mediating mechanisms are unknown. This study aimed to investigate the mediating roles of emotion regulation strategies and affective cognition in the relationship between personality constructs and affective disorders. Data were collected from a sample of 276 students and staff at the University of Manchester. Measures included both broad and narrow Big Five personality constructs; COPE Inventory strategies; a dot-probe task to measure attentional biases to emotional information; both a questionnaire and a computerised cognitive to measure interpretation of emotional images; and measures of anxiety and depression.
DOI: 10.48420/19978016.v2
title: Data for "Links between Personality, Affective Cognition, Emotion Regulation and Affective Disorders"

description: Vulnerability to anxiety and depressive disorders is affected by risk and resilience factors, such as personality, use of emotion regulation strategies, and affective cognition. Previous research has identified personality constructs best explaining variance in anxiety and depression (Lyon et al, 2020; 2021), however the mediating mechanisms are unknown. This study aimed to investigate the mediating roles of emotion regulation strategies and affective cognition in the relationship between personality constructs and affective disorders. Data were collected from a sample of 276 students and staff at the University of Manchester. Measures included both broad and narrow Big Five personality constructs; COPE Inventory strategies; a dot-probe task to measure attentional biases to emotional information; both a questionnaire and a computerised cognitive to measure interpretation of emotional images; and measures of anxiety and depression.