cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19601857
title: A new genus of land planarian (Platyhelminthes, Geoplanidae) for a new ‘blind’ species

description: The morphological and molecular (mitochondrial cytochrome c oxidase subunit I, COI) characterization of a new species of the subfamily Geoplaninae (Geoplanidae) supports the erection of a new genus, <i>Anophthalmoplana</i> gen. nov. The most remarkable feature of <i>Anophthalmoplana horticola</i> sp. nov. is the absence of eyes, a feature so far only recorded in one species of Geoplaninae, <i>Geobia subterranea</i>. Other features of the species are: a narrow, ribbon-shaped body; a gonopore near the posterior tip; a creeping sole almost as wide as body width; cylindrical pharynx; voluminous extrabulbar prostatic vesicle; protrusible penis papilla occupying the whole male atrium; a common ovovitelline duct dorsal to the female atrium; the atrium being lined by an epithelium with a stratified appearance. Based on the mtDNA phylogeny, the specimens studied here form a separate clade from the genus <i>Geobia</i>, thus contradicting its inclusion in this genus or any other of the subfamily Geoplaninae. These results reinforce the morphological differences observed, regarding internal anatomy, from <i>Geobia</i> and other members of Geoplaninae. http://zoobank.org/urn:lsid:zoobank.org:pub:317A4055-BCC8-4B89-A9DC-F96F98E803A5
DOI: 10.6084/m9.figshare.19601857.v1
title: A new genus of land planarian (Platyhelminthes, Geoplanidae) for a new ‘blind’ species

description: The morphological and molecular (mitochondrial cytochrome c oxidase subunit I, COI) characterization of a new species of the subfamily Geoplaninae (Geoplanidae) supports the erection of a new genus, <i>Anophthalmoplana</i> gen. nov. The most remarkable feature of <i>Anophthalmoplana horticola</i> sp. nov. is the absence of eyes, a feature so far only recorded in one species of Geoplaninae, <i>Geobia subterranea</i>. Other features of the species are: a narrow, ribbon-shaped body; a gonopore near the posterior tip; a creeping sole almost as wide as body width; cylindrical pharynx; voluminous extrabulbar prostatic vesicle; protrusible penis papilla occupying the whole male atrium; a common ovovitelline duct dorsal to the female atrium; the atrium being lined by an epithelium with a stratified appearance. Based on the mtDNA phylogeny, the specimens studied here form a separate clade from the genus <i>Geobia</i>, thus contradicting its inclusion in this genus or any other of the subfamily Geoplaninae. These results reinforce the morphological differences observed, regarding internal anatomy, from <i>Geobia</i> and other members of Geoplaninae. http://zoobank.org/urn:lsid:zoobank.org:pub:317A4055-BCC8-4B89-A9DC-F96F98E803A5
DOI: 10.25494/p65g67
title: SCR_Aerial_CoalOilPoint_1999_2012_KelpPersistence

description: These raster and vector datasets were developed for the Sea Grant South Coast MPA Baseline Program as part of the project “Nearshore Substrate Mapping and Change Analysis using Historical and Concurrent Multispectral Imagery” (#R/MPA 30 10-049). The study region is the South Coast Region (SCR). Kelp thematic maps from 1999-2012 were used for this product. Ocean Imaging acquired the imagery and classification data used for this analysis from its own Digital Multispectral Camera (DMSC), the Microsoft UltraCam-X aerial camera system and from kelp habitat classifications archived by the California Department of Fish and Wildlife (CDFW). Details on this systems and the data processing are below in the Lineage section of this document. The persistence analyses show the persistence of kelp beds in the SCR and MPAs as a percentage of the number of years analyzed. The coverage area is from Conception, CA south to Imperial Beach, CA. The GIS analysis product files are deliverd are in GeoTIFF (.tif) and ESRI Shapefile formats as well as Adobe Acrobat PDF files.This raster dataset contains a persistence analysis of giant kelp beds along the California South Coast Region (SCR) from from Point Conception, CA down to Imperial beach, CA. This specific raster classification includes the Naples SMCA and Campus Point SMCA.This dataset was originally uploaded to Oceanspaces (http://oceanspaces.org/) and the Knowledge Network for Biocomplexity (KNB, https://knb.ecoinformatics.org/data) in 2013 as part of the South Coast baseline monitoring program. In 2022 this dataset was moved to the California Ocean Protection Council Data Repository (https://opc.dataone.org/) by Mike Esgro (Michael.Esgro@resources.ca.gov) and Rani Gaddam (gaddam@ucsc.edu). At that time the GIS analysis products were added to the dataset.The long-term California MPA boundary and project info tables can be found as a separate dataset here: https://opc.dataone.org/view/doi:10.25494/P64S3W.
DOI: 10.6084/m9.figshare.5446681
title: Flight data

description: The data include 66 radar tracks of aircraft pairs with short-term conflict alerts. <br>
DOI: 10.6084/m9.figshare.5446681.v2
title: Flight data

description: The data include 66 radar tracks of aircraft pairs with short-term conflict alerts. <br>
DOI: 10.6084/m9.figshare.5446681.v1
title: Flight data

description: The data include 66 radar tracks of aircraft pairs with short-term conflict alerts. <br>
DOI: 10.5281/zenodo.6461868
title: The Robot Joint Torque Measurements for Accidental Collisions and Intentional Contacts

description: This dataset contains the joint toque measurements of a robot manipulator (KUKA LWR4+) under accidental collisions and intentional contacts. It is specifically intended for the research study on robot collision detection, classification, diagnosis, or prediction. The dataset was recorded at Chair of Automatic Control Engineering, Technical University of Munich, Munich, Germany, by Dr. Zengjie Zhang, under the supervision of Dr. Dirk Wollherr, in 2017. Its detailed recording procedure is explained in the following work: [1] <strong>Zhang Z</strong>, Qian K, Schuller B W, and Wollherr D. An online robot collision detection and identification scheme by supervised learning and bayesian decision theory[J]. <em>IEEE Transactions on Automation Science and Engineering</em>, 2020, 18(3): 1144-1156. The dataset contains a number of external signal pieces of three classes: accidental collision (cls), with intentional manual contacts (ctc), and free from contacts (fre). Each signal piece lasts for 1.024s subject to the sampling rate 1kHz. Collisions or contacts occur at 0.256s of the signal pieces. The unit of the signal measurement is Nm. All the signals are recorded for the seven joints (#1 to #7) of the KUKA robot arm. The dataset is stored in .csv files. Each .csv file, containing the torque signal pieces for each class and each joint, is formed as an N by M matrix, where M = 1024 is the length of the signals and N is the number of signal pieces of the corresponding classes. For 'cls', N = 6960; for 'ctc', N = 7583; and for 'fre', N = 14098. Refer to the 'ReadMe.md' file for how to import the data to Python or MATLAB. This dataset is openly accessible for research work. Please cite this dataset and reference [1] if you publish the work based on them.
DOI: 10.5281/zenodo.6461867
title: The Robot Joint Torque Measurements for Accidental Collisions and Intentional Contacts

description: This dataset contains the joint toque measurements of a robot manipulator (KUKA LWR4+) under accidental collisions and intentional contacts. It is specifically intended for the research study on robot collision detection, classification, diagnosis, or prediction. The dataset was recorded at Chair of Automatic Control Engineering, Technical University of Munich, Munich, Germany, by Dr. Zengjie Zhang, under the supervision of Dr. Dirk Wollherr, in 2017. Its detailed recording procedure is explained in the following work: [1] <strong>Zhang Z</strong>, Qian K, Schuller B W, and Wollherr D. An online robot collision detection and identification scheme by supervised learning and bayesian decision theory[J]. <em>IEEE Transactions on Automation Science and Engineering</em>, 2020, 18(3): 1144-1156. The dataset contains a number of external signal pieces of three classes: accidental collision (cls), with intentional manual contacts (ctc), and free from contacts (fre). Each signal piece lasts for 1.024s subject to the sampling rate 1kHz. Collisions or contacts occur at 0.256s of the signal pieces. The unit of the signal measurement is Nm. All the signals are recorded for the seven joints (#1 to #7) of the KUKA robot arm. The dataset is stored in .csv files. Each .csv file, containing the torque signal pieces for each class and each joint, is formed as an N by M matrix, where M = 1024 is the length of the signals and N is the number of signal pieces of the corresponding classes. For 'cls', N = 6960; for 'ctc', N = 7583; and for 'fre', N = 14098. Refer to the 'ReadMe.md' file for how to import the data to Python or MATLAB. This dataset is openly accessible for research work. Please cite this dataset and reference [1] if you publish the work based on them.
DOI: 10.25345/c55717s02
title: MassIVE Reanalysis RMSV000000644.1 - CPTAC MSV000083095/ S014 Optimal Cutting Temperature Embedding Media Study, annotation file only
title: Annotation file for CPTAC MSV000083095/ S014 Optimal Cutting Temperature Embedding Media Study
DOI: 10.6084/m9.figshare.19601665
title: DATASET_ANAC_BRAZIL_2011_2019

description: This is a public dataset about national flights in Brazil. The dataset has more than 40 milion airline-specific commercial flights. Format: csv
DOI: 10.6084/m9.figshare.19601665.v1
title: DATASET_ANAC_BRAZIL_2011_2019

description: This is a public dataset about national flights in Brazil. The dataset has more than 40 milion airline-specific commercial flights. Format: csv
DOI: 10.25494/p61s4b
title: SCR_Aerial_DanaPoint_LagunaBeach_1999_2012_KelpPersistence

description: These raster and vector datasets were developed for the Sea Grant South Coast MPA Baseline Program as part of the project “Nearshore Substrate Mapping and Change Analysis using Historical and Concurrent Multispectral Imagery” (#R/MPA 30 10-049). The study region is the South Coast Region (SCR). Kelp thematic maps from 1999-2012 were used for this product. Ocean Imaging acquired the imagery and classification data used for this analysis from its own Digital Multispectral Camera (DMSC), the Microsoft UltraCam-X aerial camera system and from kelp habitat classifications archived by the California Department of Fish and Wildlife (CDFW). Details on this systems and the data processing are below in the Lineage section of this document. The persistence analyses show the persistence of kelp beds in the SCR and MPAs as a percentage of the number of years analyzed. The coverage area is from Conception, CA south to Imperial Beach, CA. The GIS analysis product files are deliverd are in GeoTIFF (.tif) and ESRI Shapefile formats as well as Adobe Acrobat PDF files.This raster dataset contains a persistence analysis of giant kelp beds along the California South Coast Region (SCR) from from Point Conception, CA down to Imperial beach, CA. This specific raster classification includes the Crystal Cove SMCA, Laguna Beach SMR, Laguna SMCA, and Dana Point SMCA.This dataset was originally uploaded to Oceanspaces (http://oceanspaces.org/) and the Knowledge Network for Biocomplexity (KNB, https://knb.ecoinformatics.org/data) in 2013 as part of the South Coast baseline monitoring program. In 2022 this dataset was moved to the California Ocean Protection Council Data Repository (https://opc.dataone.org/) by Mike Esgro (Michael.Esgro@resources.ca.gov) and Rani Gaddam (gaddam@ucsc.edu). At that time the GIS analysis products were added to the dataset.The long-term California MPA boundary and project info tables can be found as a separate dataset here: https://opc.dataone.org/view/doi:10.25494/P64S3W.
DOI: 10.25494/p6x01b
title: SCR_Aerial_LaJolla_PointLoma_1999_2012_KelpPersistence

description: These raster and vector datasets were developed for the Sea Grant South Coast MPA Baseline Program as part of the project “Nearshore Substrate Mapping and Change Analysis using Historical and Concurrent Multispectral Imagery” (#R/MPA 30 10-049). The study region is the South Coast Region (SCR). Kelp thematic maps from 1999-2012 were used for this product. Ocean Imaging acquired the imagery and classification data used for this analysis from its own Digital Multispectral Camera (DMSC), the Microsoft UltraCam-X aerial camera system and from kelp habitat classifications archived by the California Department of Fish and Wildlife (CDFW). Details on this systems and the data processing are below in the Lineage section of this document. The persistence analyses show the persistence of kelp beds in the SCR and MPAs as a percentage of the number of years analyzed. The coverage area is from Conception, CA south to Imperial Beach, CA. The GIS analysis product files are deliverd are in GeoTIFF (.tif) and ESRI Shapefile formats as well as Adobe Acrobat PDF files.This raster dataset contains a persistence analysis of giant kelp beds along the California South Coast Region (SCR) from from Point Conception, CA down to Imperial beach, CA. This specific raster classification includes the South La Jolla SMR.This dataset was originally uploaded to Oceanspaces (http://oceanspaces.org/) and the Knowledge Network for Biocomplexity (KNB, https://knb.ecoinformatics.org/data) in 2013 as part of the South Coast baseline monitoring program. In 2022 this dataset was moved to the California Ocean Protection Council Data Repository (https://opc.dataone.org/) by Mike Esgro (Michael.Esgro@resources.ca.gov) and Rani Gaddam (gaddam@ucsc.edu). At that time the GIS analysis products were added to the dataset.The long-term California MPA boundary and project info tables can be found as a separate dataset here: https://opc.dataone.org/view/doi:10.25494/P64S3W.
DOI: 10.25494/p6s89w
title: SCR_Aerial_PointConception_1999_2012_KelpPersistence

description: These raster and vector datasets were developed for the Sea Grant South Coast MPA Baseline Program as part of the project “Nearshore Substrate Mapping and Change Analysis using Historical and Concurrent Multispectral Imagery” (#R/MPA 30 10-049). The study region is the South Coast Region (SCR). Kelp thematic maps from 1999-2012 were used for this product. Ocean Imaging acquired the imagery and classification data used for this analysis from its own Digital Multispectral Camera (DMSC), the Microsoft UltraCam-X aerial camera system and from kelp habitat classifications archived by the California Department of Fish and Wildlife (CDFW). Details on this systems and the data processing are below in the Lineage section of this document. The persistence analyses show the persistence of kelp beds in the SCR and MPAs as a percentage of the number of years analyzed. The coverage area is from Conception, CA south to Imperial Beach, CA. The GIS analysis product files are deliverd are in GeoTIFF (.tif) and ESRI Shapefile formats as well as Adobe Acrobat PDF files.This raster dataset contains a persistence analysis of giant kelp beds along the California South Coast Region (SCR) from from Point Conception, CA down to Imperial beach, CA. This specific raster classification includes the Point Conception SMR and Kashtayit SMCA.This dataset was originally uploaded to Oceanspaces (http://oceanspaces.org/) and the Knowledge Network for Biocomplexity (KNB, https://knb.ecoinformatics.org/data) in 2013 as part of the South Coast baseline monitoring program. In 2022 this dataset was moved to the California Ocean Protection Council Data Repository (https://opc.dataone.org/) by Mike Esgro (Michael.Esgro@resources.ca.gov) and Rani Gaddam (gaddam@ucsc.edu). At that time the GIS analysis products were added to the dataset.The long-term California MPA boundary and project info tables can be found as a separate dataset here: https://opc.dataone.org/view/doi:10.25494/P64S3W.
DOI: 10.25494/p6ng6k
title: SCR_Aerial_PointVicente_1999_2012_KelpPersistence

description: These raster and vector datasets were developed for the Sea Grant South Coast MPA Baseline Program as part of the project “Nearshore Substrate Mapping and Change Analysis using Historical and Concurrent Multispectral Imagery” (#R/MPA 30 10-049). The study region is the South Coast Region (SCR). Kelp thematic maps from 1999-2012 were used for this product. Ocean Imaging acquired the imagery and classification data used for this analysis from its own Digital Multispectral Camera (DMSC), the Microsoft UltraCam-X aerial camera system and from kelp habitat classifications archived by the California Department of Fish and Wildlife (CDFW). Details on this systems and the data processing are below in the Lineage section of this document. The persistence analyses show the persistence of kelp beds in the SCR and MPAs as a percentage of the number of years analyzed. The coverage area is from Conception, CA south to Imperial Beach, CA. The GIS analysis product files are deliverd are in GeoTIFF (.tif) and ESRI Shapefile formats as well as Adobe Acrobat PDF files.This raster dataset contains a persistence analysis of giant kelp beds along the California South Coast Region (SCR) from from Point Conception, CA down to Imperial beach, CA. This specific raster classification includes the Point Vincente SMCA and Abalone Cove SMCA.This dataset was originally uploaded to Oceanspaces (http://oceanspaces.org/) and the Knowledge Network for Biocomplexity (KNB, https://knb.ecoinformatics.org/data) in 2013 as part of the South Coast baseline monitoring program. In 2022 this dataset was moved to the California Ocean Protection Council Data Repository (https://opc.dataone.org/) by Mike Esgro (Michael.Esgro@resources.ca.gov) and Rani Gaddam (gaddam@ucsc.edu). At that time the GIS analysis products were added to the dataset.The long-term California MPA boundary and project info tables can be found as a separate dataset here: https://opc.dataone.org/view/doi:10.25494/P64S3W.
DOI: 10.25494/p6hs4p
title: SCR_Aerial_PointDume_1999_2012_KelpPersistence

description: This raster dataset contains a persistence analysis of giant kelp beds along the California South Coast Region (SCR) from from Point Conception, CA down to Imperial beach, CA. This specific raster classification includes the Point Dume SMCA and Point Dume SMR.These raster and vector datasets were developed for the Sea Grant South Coast MPA Baseline Program as part of the project “Nearshore Substrate Mapping and Change Analysis using Historical and Concurrent Multispectral Imagery” (#R/MPA 30 10-049). The study region is the South Coast Region (SCR). Kelp thematic maps from 1999-2012 were used for this product. Ocean Imaging acquired the imagery and classification data used for this analysis from its own Digital Multispectral Camera (DMSC), the Microsoft UltraCam-X aerial camera system and from kelp habitat classifications archived by the California Department of Fish and Wildlife (CDFW). Details on this systems and the data processing are below in the Lineage section of this document. The persistence analyses show the persistence of kelp beds in the SCR and MPAs as a percentage of the number of years analyzed. The coverage area is from Conception, CA south to Imperial Beach, CA. The GIS analysis product files are deliverd are in GeoTIFF (.tif) and ESRI Shapefile formats as well as Adobe Acrobat PDF files.This dataset was originally uploaded to Oceanspaces (http://oceanspaces.org/) and the Knowledge Network for Biocomplexity (KNB, https://knb.ecoinformatics.org/data) in 2013 as part of the South Coast baseline monitoring program. In 2022 this dataset was moved to the California Ocean Protection Council Data Repository (https://opc.dataone.org/) by Mike Esgro (Michael.Esgro@resources.ca.gov) and Rani Gaddam (gaddam@ucsc.edu). At that time the GIS analysis products were added to the dataset.The long-term California MPA boundary and project info tables can be found as a separate dataset here: https://opc.dataone.org/view/doi:10.25494/P64S3W.
DOI: 10.18739/a2g737525
title: Calibrated mass loss projections from the Greenland Ice Sheet

description: Data files are available at: https://arcticdata.io/data/10.18739/A2G737525/ The potential contribution of ice sheets remains the largest source of uncertainty in projecting sea-level due to the limited predictive skill of numerical ice sheet models, yet effective planning for coming sea level rise necessitates that predictions are credible and accompanied by a defensible assessment of uncertainty. Characterization of the likelihood of upper-end contributions are particularly important for developing adaptation strategies. While the use of large ensembles of simulations allows these kinds probabilistic assessments, there is no guarantee that simulations are aligned with observations. Here, we show that calibrating an ensemble of model simulations on observations reduces uncertainties in projecting 21st century mass loss from the Greenland Ice Sheet relative to a plausible a priori distribution of model configurations. We find that jointly conditioning on surface speeds and cumulative mass loss reduces the projected 2100 median contribution and 5--95th percentile by 16-30% and 38-56, respectively, compared to the un-calibrated ensemble, resulting in calibrated sea-level contributions ranging from 4 to 30 centimeters at the year 2100. This data set contains several products: - Surrogate model training data. ~1000 surface speed realizations in netCDF format, prepared with the Parallel Ice Sheet Model (PISM, www.pism.io) - Trained emulators. 50 trained emulators in HDF5 format, prepared with PyTorch (www.pytorch.org) - Posterior parameter distributions. 50 posterior distributions in CSV format. - Time series of projected mass change. Time series of projected mass change from 2008 until 2100 in CSV format, prepared with the Parallel Ice Sheet Model (PISM, www.pism.io), for both the ensemble using the Prior and the Posterior (calibrated) parameter distribution. 500 realizations for each Representative Concentration Pathway (RCP) scenario 2.6, 4.5, and 8.5.
DOI: 10.25494/p6d022
title: SCR_Aerial_SanMiguelIsland_1999_2012_KelpPersistence

description: These raster and vector datasets were developed for the Sea Grant South Coast MPA Baseline Program as part of the project “Nearshore Substrate Mapping and Change Analysis using Historical and Concurrent Multispectral Imagery” (#R/MPA 30 10-049). The study region is the South Coast Region (SCR). Kelp thematic maps from 1999-2012 were used for this product. Ocean Imaging acquired the imagery and classification data used for this analysis from its own Digital Multispectral Camera (DMSC), the Microsoft UltraCam-X aerial camera system and from kelp habitat classifications archived by the California Department of Fish and Wildlife (CDFW). Details on this systems and the data processing are below in the Lineage section of this document. The persistence analyses show the persistence of kelp beds in the SCR and MPAs as a percentage of the number of years analyzed. The coverage area is from Conception, CA south to Imperial Beach, CA. The GIS analysis product files are deliverd are in GeoTIFF (.tif) and ESRI Shapefile formats as well as Adobe Acrobat PDF files.This raster dataset contains a persistence analysis of giant kelp beds along the California South Coast Region (SCR) from from Point Conception, CA down to Imperial beach, CA. This specific raster classification includes the Judith Rock SMR and Harris Point SMR.This dataset was originally uploaded to Oceanspaces (http://oceanspaces.org/) and the Knowledge Network for Biocomplexity (KNB, https://knb.ecoinformatics.org/data) in 2013 as part of the South Coast baseline monitoring program. In 2022 this dataset was moved to the California Ocean Protection Council Data Repository (https://opc.dataone.org/) by Mike Esgro (Michael.Esgro@resources.ca.gov) and Rani Gaddam (gaddam@ucsc.edu). At that time the GIS analysis products were added to the dataset.The long-term California MPA boundary and project info tables can be found as a separate dataset here: https://opc.dataone.org/view/doi:10.25494/P64S3W.