cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6729986
title: SuperDARN data in netCDF format (2003-Jun)

description: 2003-Jun SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6730239
title: SuperDARN data in netCDF format (2003-May)

description: 2003-May SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6730688
title: SuperDARN data in netCDF format (2003-Feb)

description: 2003-Feb SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6730397
title: SuperDARN data in netCDF format (2003-Apr)

description: 2003-Apr SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6730820
title: SuperDARN data in netCDF format (2003-Jan)

description: 2003-Jan SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6730567
title: SuperDARN data in netCDF format (2003-Mar)

description: 2003-Mar SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6726109
title: SuperDARN data in netCDF format (2004-Oct)

description: 2004-Oct SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6726577
title: SuperDARN data in netCDF format (2004-Sep)

description: 2004-Sep SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6723413
title: SuperDARN data in netCDF format (2004-Dec)

description: 2004-Dec SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6724712
title: SuperDARN data in netCDF format (2004-Nov)

description: 2004-Nov SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6460303
title: 3D+time nuclei tracking dataset of confocal fluorescence microscopy time series of C. elegans embryos

description: The dataset consists of 3 confocal microscopy time series of <em>C. elegans</em> embryos, fully tracked with StarryNite followed by manual curation 3 raw time-series and the corresponding tracks/lineage trees temporal resolution; 75s temporal extent: 400 frames, tracked for at least 370 frames spatial resolution (zyx): 0.75 x 0.15 x 0.15 μm spatial extent (zyx):/ 41 x 512 x 512px Microscope: Zeiss Axio Observer.Z1 The annotations were created using the method described in: <em> Santella, A., Du, Z. & Bao, Z. A semi-local neighborhood-based framework for probabilistic cell lineage tracing. BMC Bioinformatics 15, 217 (2014). https://doi.org/10.1186/1471-2105-15-217</em> Additionally the data was extended and used for the development of a new tracking method in the following publication: <em> Hirsch, P., Malin-Mayor C., Santella, A., Preibisch, S., Kainmueller, D., Funke, J. Tracking by weakly-supervised learning and graph optimization for whole-embryo C. elegans lineages. MICCAI 2022.</em> For questions please contact Peter Hirsch (peterhirsch@posteo.de).
DOI: 10.5281/zenodo.6460302
title: 3D+time nuclei tracking dataset of confocal fluorescence microscopy time series of C. elegans embryos

description: The dataset consists of 3 confocal microscopy time series of <em>C. elegans</em> embryos, fully tracked with StarryNite followed by manual curation 3 raw time-series and the corresponding tracks/lineage trees temporal resolution; 75s temporal extent: 400 frames, tracked for at least 370 frames spatial resolution (zyx): 0.75 x 0.15 x 0.15 μm spatial extent (zyx):/ 41 x 512 x 512px Microscope: Zeiss Axio Observer.Z1 The annotations were created using the method described in: <em> Santella, A., Du, Z. & Bao, Z. A semi-local neighborhood-based framework for probabilistic cell lineage tracing. BMC Bioinformatics 15, 217 (2014). https://doi.org/10.1186/1471-2105-15-217</em> Additionally the data was extended and used for the development of a new tracking method in the following publication: <em> Hirsch, P., Malin-Mayor C., Santella, A., Preibisch, S., Kainmueller, D., Funke, J. Tracking by weakly-supervised learning and graph optimization for whole-embryo C. elegans lineages. MICCAI 2022.</em> For questions please contact Peter Hirsch (peterhirsch@posteo.de).
DOI: 10.5281/zenodo.6460375
title: 3D+time nuclei tracking dataset of diSPIM lightsheet fluorescence microscopy time series of C. elegans embryos

description: The dataset consists of 3 diSPIM microscopy time series of <em>C. elegans</em> embryos, fully tracked. 3 raw time-series and the corresponding tracks/lineage trees temporal resolution: 1min temporal extent: 350-400 frames, tracked for at least 330 frames spatial resolution (zyx): 0.1625 x 0.1625 x 0.1625μm spatial extent (zyx): 250 x 250 x 400px (average) Microscope: dual-view ASI diSPIM (fused and deconvolved using the MIPAV GenerateFusion plugin) The original raw data and annotations were part of the following publication (please also cite this if you use the dataset): <em> </em>Moyle, M.W., Barnes, K.M., Kuchroo, M. <em>et al.</em> Structural and developmental principles of neuropil assembly in <em>C. elegans</em>. <em>Nature</em> 591<strong>, </strong>99–104 (2021). https://doi.org/10.1038/s41586-020-03169-5 Additionally the data was extended and slightly curated further by Peter Hirsch (MDC) and used for the development of a new tracking method in the following publication: <em> Hirsch, P., Malin-Mayor, C., Santella, A., Preibisch, S., Kainmueller, D., Funke, J. Tracking by weakly-supervised learning and graph optimization for whole-embryo C. elegans lineages. MICCAI 2022</em> For questions please contact Peter Hirsch (peterhirsch@posteo.de).
DOI: 10.5281/zenodo.6460374
title: 3D+time nuclei tracking dataset of diSPIM lightsheet fluorescence microscopy time series of C. elegans embryos

description: The dataset consists of 3 diSPIM microscopy time series of <em>C. elegans</em> embryos, fully tracked. 3 raw time-series and the corresponding tracks/lineage trees temporal resolution: 1min temporal extent: 350-400 frames, tracked for at least 330 frames spatial resolution (zyx): 0.1625 x 0.1625 x 0.1625μm spatial extent (zyx): 250 x 250 x 400px (average) Microscope: dual-view ASI diSPIM (fused and deconvolved using the MIPAV GenerateFusion plugin) The original raw data and annotations were part of the following publication (please also cite this if you use the dataset): <em> </em>Moyle, M.W., Barnes, K.M., Kuchroo, M. <em>et al.</em> Structural and developmental principles of neuropil assembly in <em>C. elegans</em>. <em>Nature</em> 591<strong>, </strong>99–104 (2021). https://doi.org/10.1038/s41586-020-03169-5 Additionally the data was extended and slightly curated further by Peter Hirsch (MDC) and used for the development of a new tracking method in the following publication: <em> Hirsch, P., Malin-Mayor, C., Santella, A., Preibisch, S., Kainmueller, D., Funke, J. Tracking by weakly-supervised learning and graph optimization for whole-embryo C. elegans lineages. MICCAI 2022</em> For questions please contact Peter Hirsch (peterhirsch@posteo.de).
DOI: 10.5281/zenodo.6759136
title: Derby database for mapping secondary to primary HGNC gene symbols

description: The datasets (hgnc_complete_set and withdrawn) used to create this ID mapping database were downloaded from HGNC (<em>HUGO Gene Nomenclature Committee at the European Bioinformatics Institute, </em>website URL: https://www.genenames.org/) on 09/05/2022. This database was used for the BridgeDb demo at BioSB 2022 conference. The scripts used to create this database based on HGNC: https://github.com/tabbassidaloii/create-bridgedb-secondary2primary This work was funded by the FAIRplus project (grant agreement no 802750) and NWO Open Science Fund (grant no 203.001.121).
description: We thank Denise Slenter, who contributed to the BridgeDb demo at BioSB 2022.
DOI: 10.5281/zenodo.6759135
title: Derby database for mapping secondary to primary HGNC gene symbols

description: The datasets (hgnc_complete_set and withdrawn) used to create this ID mapping database were downloaded from HGNC (<em>HUGO Gene Nomenclature Committee at the European Bioinformatics Institute, </em>website URL: https://www.genenames.org/) on 09/05/2022. This database was used for the BridgeDb demo at BioSB 2022 conference. The scripts used to create this database based on HGNC: https://github.com/tabbassidaloii/create-bridgedb-secondary2primary This work was funded by the FAIRplus project (grant agreement no 802750) and NWO Open Science Fund (grant no 203.001.121).
description: We thank Denise Slenter, who contributed to the BridgeDb demo at BioSB 2022.
DOI: 10.5281/zenodo.6759307
title: Derby database for mapping secondary to primary HMDB identifiers

description: The data (hmdb_metabolites, released on 17/11/2021) used to create this ID mapping database was downloaded from HMDB (<em>Human Metabolome Database, </em>website URL: https://hmdb.ca/). This database was used for the BridgeDb demo at BioSB 2022 conference. The scripts used to create this database based on HGNC: https://github.com/tabbassidaloii/create-bridgedb-secondary2primary This work was funded by the FAIRplus project (grant agreement no 802750) and NWO Open Science Fund (grant no 203.001.121).
description: We thank Denise Slenter, who contributed to the BridgeDb demo at BioSB 2022.
DOI: 10.5281/zenodo.6759306
title: Derby database for mapping secondary to primary HMDB identifiers

description: The data (hmdb_metabolites, released on 17/11/2021) used to create this ID mapping database was downloaded from HMDB (<em>Human Metabolome Database, </em>website URL: https://hmdb.ca/). This database was used for the BridgeDb demo at BioSB 2022 conference. The scripts used to create this database based on HGNC: https://github.com/tabbassidaloii/create-bridgedb-secondary2primary This work was funded by the FAIRplus project (grant agreement no 802750) and NWO Open Science Fund (grant no 203.001.121).
description: We thank Denise Slenter, who contributed to the BridgeDb demo at BioSB 2022.
DOI: 10.5281/zenodo.6760608
title: Platelet bioenergetics are associated with resting metabolic rate and exercise capacity in older adult women

description: Heimler SR*, Phang HJ*, Bergstrom J, Mahapatra G, Dozier S, Gnaiger E, Molina AJA** (2022). Platelet bioenergetics are associated with resting metabolic rate and exercise capacity in older women. MitoFit Preprints 2022.7. https://doi.org/10.26124/mitofit:2022-0007 *Co-first authors ** Corresponding author: ajmolina@health.ucsd.edu
description: We thank Oroboros Instruments for technical support and the loan of O2k respirometers for the conduct of this study. This project was supported by grants awarded to Dr. Molina from the National Insitutes for Aging (R01 AG054523, R21 AG051077) and the American Heart Association (15MCPRP25680019).
DOI: 10.5281/zenodo.6452013
title: Platelet bioenergetics are associated with resting metabolic rate and exercise capacity in older adult women

description: Heimler SR*, Phang HJ*, Bergstrom J, Mahapatra G, Dozier S, Gnaiger E, Molina AJA** (2022). Platelet bioenergetics are associated with resting metabolic rate and exercise capacity in older women. MitoFit Preprints 2022.7. https://doi.org/10.26124/mitofit:2022-0007 *Co-first authors ** Corresponding author: ajmolina@health.ucsd.edu
description: We thank Oroboros Instruments for technical support and the loan of O2k respirometers for the conduct of this study. This project was supported by grants awarded to Dr. Molina from the National Insitutes for Aging (R01 AG054523, R21 AG051077) and the American Heart Association (15MCPRP25680019).