cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5874537
title: Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery

description: <strong>Power Generation Data Set</strong> This data set contains imaging data acquired by ESA's Sentinel-2<br> Earth-observing satellite constellation [1] for a sample of power stations that were picked using geographic coordinates <br> provided by the European Pollutant Release and Transfer Register [2]. The images<br> contain scenes of power stations, some of which are actively<br> emitting smoke plumes. This data set was created with the goal to automatically segment plumes, predict the type of fired fuel, predict the rate of power generation and estimate the amount of CO2 emissions, directly from remote sensing images. <br> <strong>Description</strong><br> Each image is provided in the GeoTIFF file format, contains a total of 13 bands. Images have either a shape of 120x120 or 300x300 pixels (corresponding to a square area with an edge length of respectively 1.2 km and 3.0 km on the ground)<br> . This repository contains a total of 2131 images. This<br> repository contains a collection of JSON files that hold manual segmentation labels for plumes. Segmentation<br> labels were generated using label-studio [3]. Please note that polygon edge coordinates have to be scaled to fit the images. <br> <strong>Content</strong> The following files are contained in this repository: README.md - this file images.zip [2.0GB] - contains 2131 GeoTIFF images segmentation_labels.zip [1.5MB] - contains 2131 JSON files labels.csv [310KB] - contains additional labels for each image: Generation output rate [4],[5] Country Type of fired fuel Latitude and longitude of the power plant Concurrent weather information (temperature, humidity and wind vector) <strong>Acknowledgement</strong> If you use this data set, please cite our publication: Hanna, J., Mommert, M., Scheibenreif, L., Borth, D.,<br> "Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery",<br> Tackling Climate Change with Machine Learning workshop at NeurIPS 2021. Please refer to this publication for additional information on the data set. The code used for this publication is available at https://github.com/HSG-AIML/RemoteSensingCO2Estimation. <br> <strong>Author</strong> Joëlle Hanna University of St. Gallen, AIML Lab, School of Computer Science joelle.hanna@unisg.ch <br> <strong>References</strong><br> [1]: https://earth.esa.int/web/sentinel/missions/sentinel-2<br> [2]: https://www.eea.europa.eu/data-and-maps/data/industrial-reporting-under-the-industrial<br> [3]: https://labelstud.io/<br> [4]: https://transparency.entsoe.eu/generation/r2/actualGenerationPerGenerationUnit/show<br> [5]: https://doi.org/10.5281/zenodo.3574566
DOI: 10.5281/zenodo.5644745
title: Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery

description: <strong>Power Generation Data Set</strong> This data set contains imaging data acquired by ESA's Sentinel-2<br> Earth-observing satellite constellation [1] for a sample of power stations that were picked using geographic coordinates <br> provided by the European Pollutant Release and Transfer Register [2]. The images<br> contain scenes of power stations, some of which are actively<br> emitting smoke plumes. This data set was created with the goal to automatically segment plumes, predict the type of fired fuel, predict the rate of power generation and estimate the amount of CO2 emissions, directly from remote sensing images. <br> <strong>Description</strong><br> Each image is provided in the GeoTIFF file format, contains a total of 13 bands. Images have either a shape of 120x120 or 300x300 pixels (corresponding to a square area with an edge length of respectively 1.2 km and 3.0 km on the ground)<br> . This repository contains a total of 2131 images. This<br> repository contains a collection of JSON files that hold manual segmentation labels for plumes. Segmentation<br> labels were generated using label-studio [3]. Please note that polygon edge coordinates have to be scaled to fit the images. <br> <strong>Content</strong> The following files are contained in this repository: README.md - this file images.zip [2.0GB] - contains 2131 GeoTIFF images segmentation_labels.zip [1.5MB] - contains 2131 JSON files labels.csv [310KB] - contains additional labels for each image: Generation output rate [4],[5] Country Type of fired fuel Latitude and longitude of the power plant Concurrent weather information (temperature, humidity and wind vector) <strong>Acknowledgement</strong> If you use this data set, please cite our publication: Hanna, J., Mommert, M., Scheibenreif, L., Borth, D.,<br> "Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery",<br> Tackling Climate Change with Machine Learning workshop at NeurIPS 2021. Please refer to this publication for additional information on the data set. The code used for this publication is available at https://github.com/HSG-AIML/RemoteSensingCO2Estimation. <br> <strong>Author</strong> Joëlle Hanna University of St. Gallen, AIML Lab, School of Computer Science joelle.hanna@unisg.ch <br> <strong>References</strong><br> [1]: https://earth.esa.int/web/sentinel/missions/sentinel-2<br> [2]: https://www.eea.europa.eu/data-and-maps/data/industrial-reporting-under-the-industrial<br> [3]: https://labelstud.io/<br> [4]: https://transparency.entsoe.eu/generation/r2/actualGenerationPerGenerationUnit/show<br> [5]: https://doi.org/10.5281/zenodo.3574566
DOI: 10.18112/openneuro.ds003592.v1.0.6
title: Neurocognitive aging data release with behavioral, structural, and multi-echo functional MRI measures
DOI: 10.13020/v6tp-tt74
title: Data for A small native predator reduces reproductive success of a large invasive fish as revealed by whole-lake experiments
DOI: 10.21239/v9609f
title: Datos recolectados pesca artesanal en Magdalena y Guajira

description: Archivo Excel con 7 hojas de trabajo: Hoja 1: Esfuerzo pesquero (13,648 registros). Variables: FECHA_ZARPE, FECHA_ARRIBO, SITIO_DESEMBARCO, REGISTRADOR CALADERO, PROFUNDIDAD, ARTE DE PESCA, EMBARCACION, PROPULSION, POTENCIA PESCADORES, HORA_INICO,HORA_FINAL. Hoja 2: Captura desembarcada(55,336 registros). Variables: Tamaño (cualitativo), grupo taxonómico, nombre vulgar, nombre científico, estado número de ejemplares, peso (g). Hoja 3: Actividad diaria (42,453 registros). Variables: Fecha, sitio de desembarco, arte de pesca, embarcaciones activas, embarcaciones muestreadas. Hoja 4: Dias efectivo (2,302 registros). Variables Año, mes, sitio de desembarco, arte de pesca, por cada dia del mes si se encuentra activo/no activo y numero de dias efectivos total por mes. Hoja 5: Frecuencias de tallas(201,880 registros). Variables: Fecha de zarpe, fecha_arribo, sitio_desembarco, registrador, arte de pesca, caladero, nombre embarcación, grupo taxonomico, nombre vulgar, nombre_cientifico, longitud especimen (cms), numero de ejemplares. Hoja 6. Costos de la operacion (4.599 registros). Variables: Item del gasto, costo. Hoja 7. Precios comerciales (1.748 registros). Variables: Fecha, sitio de desembarco, nombre científico de la especie, nombre vulgar, longitud desde (cm), longitud hasta (cm), precio (COP).
DOI: 10.6084/m9.figshare.11881332
title: Development and Evaluation of a Low-Cost Data Acquisition System using Heterogeneous Sensors

description: Acquiring the human’s musculoskeletal movements is pivotal in conducting physiotherapeutic studies, rehabilitation exercises and, designing assistive devices to cater mobility impairments. Existing studies are generally more focused on measuring a specific phenomenon and/or lack the diversity needed to cover a range of human activities. In this context, the research presented herein is aimed to develop a low-cost wearable sensory system, having a diverse set of sensors, which can acquire data of healthy and rehabilitated subjects alike during diverse locomotor activities in both indoor and outdoor environments. The system consists of twenty wearable sensors, and two off-the-shelf NI’s myRIO microcontroller boards. The statistical analysis showed no significant difference between and among the subjects for various locomotor activities (P-values &lt; 0.05), hence, showing the system’s reliability and reproducibility. Gait-event identification (i.e., heel contact/toe off) has also been evaluated and showed promising results (overall time difference= ±50ms during level ground and ramp activities).
DOI: 10.6084/m9.figshare.11881332.v8
title: Development and Evaluation of a Low-Cost Data Acquisition System using Heterogeneous Sensors

description: Acquiring the human’s musculoskeletal movements is pivotal in conducting physiotherapeutic studies, rehabilitation exercises and, designing assistive devices to cater mobility impairments. Existing studies are generally more focused on measuring a specific phenomenon and/or lack the diversity needed to cover a range of human activities. In this context, the research presented herein is aimed to develop a low-cost wearable sensory system, having a diverse set of sensors, which can acquire data of healthy and rehabilitated subjects alike during diverse locomotor activities in both indoor and outdoor environments. The system consists of twenty wearable sensors, and two off-the-shelf NI’s myRIO microcontroller boards. The statistical analysis showed no significant difference between and among the subjects for various locomotor activities (P-values &lt; 0.05), hence, showing the system’s reliability and reproducibility. Gait-event identification (i.e., heel contact/toe off) has also been evaluated and showed promising results (overall time difference= ±50ms during level ground and ramp activities).
DOI: 10.3886/e115824
title: How school and district leaders access, perceive, and use research

description: This study examined how school and district leaders access, value, and use research. From a representative sample of school districts across the United States, we surveyed 733 school and district leaders as part of an effort to develop understanding of the prevalence of research use, the nature of leaders’ attitudes toward research, and individual and organizational correlates of research use. School and district leaders alike reported frequent use of research use and generally positive attitudes toward research. Leaders reported accessing research primarily through their professional networks. Those in certain roles, those pursuing or holding an advanced degree, and those who reported a strong organizational culture of evidence use reported higher levels of research use. These findings suggest that policy efforts to promote evidence use among education leaders will be welcomed but that policy makers need to take into account the prevalence of various types of research use in designing supports for evidence use.
description: Response Rates: 51.5%
DOI: 10.3886/e115824v2
title: How school and district leaders access, perceive, and use research

description: This study examined how school and district leaders access, value, and use research. From a representative sample of school districts across the United States, we surveyed 733 school and district leaders as part of an effort to develop understanding of the prevalence of research use, the nature of leaders’ attitudes toward research, and individual and organizational correlates of research use. School and district leaders alike reported frequent use of research use and generally positive attitudes toward research. Leaders reported accessing research primarily through their professional networks. Those in certain roles, those pursuing or holding an advanced degree, and those who reported a strong organizational culture of evidence use reported higher levels of research use. These findings suggest that policy efforts to promote evidence use among education leaders will be welcomed but that policy makers need to take into account the prevalence of various types of research use in designing supports for evidence use.
description: Response Rates: 51.5%
DOI: 10.5281/zenodo.5874249
title: BotHunter: An Approach to Detect Software Bots in GitHub

description: Contains the oracle, BIMAN's output, and BotHunter tool.
DOI: 10.5281/zenodo.5824264
title: Data for paper "Zone extrapolations in parametric timed automata"

description: Data for paper "<em>Zone extrapolations in parametric timed automata</em>" This data set comes with two zip files: <strong>artifact.zip</strong> contains the current version of IMITATOR, all models and necessary scripts to reproduce all experiments on our benchmarks set. A file named README.md gives all instructions for reproducibility. <strong>results.zip</strong> contains an HTML page with a table summarizing all results, and all raw results (.res) as well.
DOI: 10.5281/zenodo.5824263
title: Data for paper "Zone extrapolations in parametric timed automata"

description: Data for paper "<em>Zone extrapolations in parametric timed automata</em>" This data set comes with two zip files: <strong>artifact.zip</strong> contains the current version of IMITATOR, all models and necessary scripts to reproduce all experiments on our benchmarks set. A file named README.md gives all instructions for reproducibility. <strong>results.zip</strong> contains an HTML page with a table summarizing all results, and all raw results (.res) as well.
DOI: 10.5281/zenodo.5873701
title: Dataset related to article "A 'Multiomic' Approach of Saliva Metabolomics, Microbiota, and Serum Biomarkers to Assess the Need of Hospitalization in COVID-19"

description: *These authors contributed equally to the work<sup> #</sup>Corresponding author This record contains raw data related to article “A ‘Multiomic’ Approach of Saliva Metabolomics, Microbiota, and Serum Biomarkers to Assess the Need of Hospitalization in COVID-19" Abstract: The SARS-CoV-2 pandemic has overwhelmed the treatment capacity of the healthcare systems during the highest viral diffusion rate. Patients reaching the emergency department had to be either hospitalized or discharged. Still, the decision was taken based on the individual assessment of the actual clinical condition, without specific biomarkers to predict future improvement or deterioration. Often discharged patients returned to the hospital for aggravation of their condition. Here we have developed a new combined approach of omics to identify factors that could distinguish COVID-19 inpatients from outpatients. We tested the metabolome in the saliva and identified nine metabolites that separated the inpatient from the outpatient population, but not completely. When combined with serum biomarkers, just two salivary metabolites (myo-inositol and 2-pyrollidine acetic acid) and one serum protein, Chitinase 3-like-1(CHI3L1) were sufficient to separate the two groups completely. These metabolites positively or negatively correlated with four modulated microbiota taxa. This is a proof-of-concept that a combined omic analysis can be used to stratify patients.
DOI: 10.5281/zenodo.5151210
title: Dataset related to article "A 'Multiomic' Approach of Saliva Metabolomics, Microbiota, and Serum Biomarkers to Assess the Need of Hospitalization in COVID-19"

description: *These authors contributed equally to the work<sup> #</sup>Corresponding author This record contains raw data related to article “A ‘Multiomic’ Approach of Saliva Metabolomics, Microbiota, and Serum Biomarkers to Assess the Need of Hospitalization in COVID-19" Abstract: The SARS-CoV-2 pandemic has overwhelmed the treatment capacity of the healthcare systems during the highest viral diffusion rate. Patients reaching the emergency department had to be either hospitalized or discharged. Still, the decision was taken based on the individual assessment of the actual clinical condition, without specific biomarkers to predict future improvement or deterioration. Often discharged patients returned to the hospital for aggravation of their condition. Here we have developed a new combined approach of omics to identify factors that could distinguish COVID-19 inpatients from outpatients. We tested the metabolome in the saliva and identified nine metabolites that separated the inpatient from the outpatient population, but not completely. When combined with serum biomarkers, just two salivary metabolites (myo-inositol and 2-pyrollidine acetic acid) and one serum protein, Chitinase 3-like-1(CHI3L1) were sufficient to separate the two groups completely. These metabolites positively or negatively correlated with four modulated microbiota taxa. This is a proof-of-concept that a combined omic analysis can be used to stratify patients.
DOI: 10.5281/zenodo.5874587
title: Datasets for Spiliotis et. al., Chem. Phys. Lett. 747 (2020) 137345

description: Datasets supplementing published article with DOI: https://doi.org/10.1016/j.cplett.2020.137345
description: This research was partially supported by ERA-NETs co-fund action (EPOCHSE, grant no. Τ3ΕΡΑ-00043). This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning» in the context of the project "Reinforcement of Postdoctoral Researchers" (MIS-5001552), implemented by the State Scholarships Foundation (ΙΚΥ). We also acknowledge the European Commission Horizon 2020, ULTRACHIRAL Project (grant no. FETOPEN-737071) for the financial support. GK acknowledges funding from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under grant agreement No [1789].
DOI: 10.5281/zenodo.5874586
title: Datasets for Spiliotis et. al., Chem. Phys. Lett. 747 (2020) 137345

description: Datasets supplementing published article with DOI: https://doi.org/10.1016/j.cplett.2020.137345
description: This research was partially supported by ERA-NETs co-fund action (EPOCHSE, grant no. Τ3ΕΡΑ-00043). This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning» in the context of the project "Reinforcement of Postdoctoral Researchers" (MIS-5001552), implemented by the State Scholarships Foundation (ΙΚΥ). We also acknowledge the European Commission Horizon 2020, ULTRACHIRAL Project (grant no. FETOPEN-737071) for the financial support. GK acknowledges funding from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under grant agreement No [1789].
DOI: 10.5281/zenodo.5873983
title: Automated microbial library generation using the bioinformatics platform IDBac

description: Data and code to reproduce analyses and figures
DOI: 10.5281/zenodo.5873982
title: Automated microbial library generation using the bioinformatics platform IDBac

description: Data and code to reproduce analyses and figures
DOI: 10.5281/zenodo.5874143
title: The Protective Function of Directed Asymmetry in the Pericellular Matrix Enveloping Chondrocytes (Supporting Data)

description: This dataset contains the images necessary to reproduce the study "The Protective Function of Directed Asymmetry in the Pericellular Matrix Enveloping Chondrocytes (DOI: 10.1007/s10439-021-02900-1)"
DOI: 10.5281/zenodo.5874142
title: The Protective Function of Directed Asymmetry in the Pericellular Matrix Enveloping Chondrocytes (Supporting Data)

description: This dataset contains the images necessary to reproduce the study "The Protective Function of Directed Asymmetry in the Pericellular Matrix Enveloping Chondrocytes (DOI: 10.1007/s10439-021-02900-1)"
DOI: 10.5281/zenodo.5874375
title: Pyrrhura lucianii decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5874374
title: Pyrrhura lucianii decontaminated FSCR

description: .
DOI: 10.7284/143539
title: Multibeam Echosounder data collected during research cruise FK210321 using a Kongsberg EM302 instrument system onboard the platform RV Falkor

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/114726
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise HLY1402 using a Hawaii UHDAS instrument system onboard the platform USCGC Healy

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/115528
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise HLY1402 using a Sea-Bird SBE-911+ instrument system onboard the platform USCGC Healy

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/121325
title: Fluorometer data collected during research cruise HLY1402 using a Turner C3 instrument system onboard the platform USCGC Healy

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/121326
title: Fluorometer data collected during research cruise HLY1402 using a Turner C3 instrument system onboard the platform USCGC Healy

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/121327
title: Fluorometer data collected during research cruise HLY1402 using a Seapoint SCF instrument system onboard the platform USCGC Healy

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/121328
title: Oxygen data collected during research cruise HLY1402 using a Sea-Bird SBE-43 instrument system onboard the platform USCGC Healy

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/121329
title: Oxygen data collected during research cruise HLY1402 using a Sea-Bird SBE-43 instrument system onboard the platform USCGC Healy

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/142411
title: GNSS (Global Navigation Satellite System) data collected during research cruise HLY1603 using a MX Marine MX512 instrument system onboard the platform USCGC Healy

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/142404
title: Gyrocompass data collected during research cruise HLY1603 using a Sperry MK-27F instrument system onboard the platform USCGC Healy

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/142405
title: INS (Inertial Navigation System) data collected during research cruise HLY1603 using a Kongsberg Seatex MRU-6 instrument system onboard the platform USCGC Healy

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/142406
title: INS (Inertial Navigation System) data collected during research cruise HLY1603 using a Sperry MK-39 instrument system onboard the platform USCGC Healy

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/142407
title: Speedlog data collected during research cruise HLY1603 using a Sperry SRD-500 instrument system onboard the platform USCGC Healy

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/142408
title: Winch data collected during research cruise HLY1603 using a InterOcean 10628 instrument system onboard the platform USCGC Healy

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/142409
title: Winch data collected during research cruise HLY1603 using a InterOcean 10628 instrument system onboard the platform USCGC Healy

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/142410
title: Winch data collected during research cruise HLY1603 using a InterOcean 71217 instrument system onboard the platform USCGC Healy

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/142412
title: Winch data collected during research cruise HLY1603 using a InterOcean 71217 instrument system onboard the platform USCGC Healy

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/141909
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise HLY2001 using a Hawaii UHDAS instrument system onboard the platform USCGC Healy

description: Original (unprocessed) data collected during a Field_expedition