cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6632582
title: SuperDARN data in netCDF format (2006-Mar)

description: 2006-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.6632471
title: SuperDARN data in netCDF format (2006-Apr)

description: 2006-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.6631675
title: SuperDARN data in netCDF format (2006-Aug)

description: 2006-Aug 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.6631469
title: SuperDARN data in netCDF format (2006-Oct)

description: 2006-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.6632040
title: SuperDARN data in netCDF format (2006-Jun)

description: 2006-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.6631256
title: SuperDARN data in netCDF format (2006-Dec)

description: 2006-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.6631372
title: SuperDARN data in netCDF format (2006-Nov)

description: 2006-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.6631809
title: SuperDARN data in netCDF format (2006-Jul)

description: 2006-Jul 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.6637749
title: CITRIS - Causal Representation Learning Datasets

description: This repository contains the datasets from the paper "CITRIS: Causal Identifiability from Temporal Intervened Sequences" (link) by Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves. <strong>Temporal Causal3DIdent</strong> - The Temporal Causal3DIdent dataset is a collection of 3D object shapes, which are observed under varying positions, rotations, lightning, and colors. Overall, we this dataset contains 7 (multidimensional) causal factors. The 7 shapes used are Armadillo, Bunny, Cow, Dragon, Head, Horse, Teapot. We provide two versions of the dataset: one that only contains images of the Teapot, and one that uses all 7 shapes. For more details on the dataset, see our GitHub repository. <strong>Interventional Pong</strong> - The Interventional Pong environment is inspired by the game dynamics of Pong, where both paddles follow the policy of moving towards the ball, and the ball has slightly random movements. This dataset considers the 5 causal variables paddle left, paddle right, the ball position, the ball velocity, and the score. For more details on the dataset, see our GitHub repository. <strong>Ball-in-Boxes</strong> - The Ball-in-Boxes is a simple dataset for showcasing the concept of the minimal causal variables. The system consists of a ball which randomly moves within a box, but only under an intervention can swap between the two boxes. Thereby, the intervention does not affect the x-position in the box. Thus, one can only discover the box assignment as a causal variable, and not whether the inner x-position also belongs to it. For more details on the dataset, see our GitHub repository.
description: {"references": ["Lippe, P., Magliacane, S., L\u00f6we, S., Asano, Y. M., Cohen, T., & Gavves, E. (2022). CITRIS: Causal Identifiability from Temporal Intervened Sequences. In Proceedings of the 39th International Conference on Machine Learning, ICML 2022."]}
DOI: 10.5281/zenodo.6637748
title: CITRIS - Causal Representation Learning Datasets

description: This repository contains the datasets from the paper "CITRIS: Causal Identifiability from Temporal Intervened Sequences" (link) by Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves. <strong>Temporal Causal3DIdent</strong> - The Temporal Causal3DIdent dataset is a collection of 3D object shapes, which are observed under varying positions, rotations, lightning, and colors. Overall, we this dataset contains 7 (multidimensional) causal factors. The 7 shapes used are Armadillo, Bunny, Cow, Dragon, Head, Horse, Teapot. We provide two versions of the dataset: one that only contains images of the Teapot, and one that uses all 7 shapes. For more details on the dataset, see our GitHub repository. <strong>Interventional Pong</strong> - The Interventional Pong environment is inspired by the game dynamics of Pong, where both paddles follow the policy of moving towards the ball, and the ball has slightly random movements. This dataset considers the 5 causal variables paddle left, paddle right, the ball position, the ball velocity, and the score. For more details on the dataset, see our GitHub repository. <strong>Ball-in-Boxes</strong> - The Ball-in-Boxes is a simple dataset for showcasing the concept of the minimal causal variables. The system consists of a ball which randomly moves within a box, but only under an intervention can swap between the two boxes. Thereby, the intervention does not affect the x-position in the box. Thus, one can only discover the box assignment as a causal variable, and not whether the inner x-position also belongs to it. For more details on the dataset, see our GitHub repository.
description: {"references": ["Lippe, P., Magliacane, S., L\u00f6we, S., Asano, Y. M., Cohen, T., & Gavves, E. (2022). CITRIS: Causal Identifiability from Temporal Intervened Sequences. In Proceedings of the 39th International Conference on Machine Learning, ICML 2022."]}
DOI: 10.25934/pr00006469.2
title: Available IPD datapackage for study 'A Randomized Multicenter, Double-Blind, Placebo-Controlled Comparison of Chemotherapy Plus Trastuzumab Plus Placebo Versus Chemotherapy Plus Trastuzumab Plus Pertuzumab as Adjuvant Therapy in Patients With Operable HER2-Positive Primary Breast Cancer'

description: This randomized, double-blind, placebo-controlled, two-arm study will assess the safety and efficacy of pertuzumab in addition to chemotherapy plus trastuzumab as adjuvant therapy in participants with operable HER2-positive primary breast cancer. This study will be carried out in collaboration with the Breast International Group (BIG).
DOI: 10.34740/kaggle/dsv/3793591
title: Percentile  TMNIST for electric meters
DOI: 10.25934/pr00005790.0
title: Available IPD datapackage for study 'A Phase III, Double-Blind, Placebo-Controlled Study of Vemurafenib Versus Vemurafenib Plus GDC-0973 in Previously Untreated BRAF^600-Mutation Positive Patients With Unresectable Locally Advanced or Metastatic Melanoma'

description: To evaluate the efficacy of vemurafenib in combination with cobimetinib (GDC-0973), compared with vemurafenib and placebo, in previously untreated BRAF V600 mutation-positive patients with unresectable locally advanced or metastatic melanoma, as measured by progression-free survival (PFS), assessed by the study site investigator.
DOI: 10.5281/zenodo.6628823
title: Microwave-activated gates between a fluxonium and a transmon qubit

description: Numerical data and figures generators for the paper A. Ciani, B. Varbanov, N. Jolly, C. Andersen, B. Terhal "Microwave-activated gates between a fluxonium and a transmon qubit"
DOI: 10.34965/i10166
title: manuscript_dataset_for_Compressive_Sensing_for_Dynamic_XRF_Scanning

description: Open/FAIR Dataset of Synchrotron Low Energy XRF and STXM files used in a manuscript on "Compressive Sensing for Dynamic XRF Scanning"

Synchrotron Low Energy XRF and STXM Dataset used in a research manuscript on "Compressive Sensing for Dynamic XRF Scanning". This dataset includes HDF5 files with XRF (/dante) and STXM (/andor) maps and metadata such as XRF lifetime and sample stage positions (/sample_motors). The dataset also includes as TIFF images various outputs such as the sparse maps, the masked areas and the results of in-painting methods. In the DAT file, there is the output of the fitted XRF data as ASCII from PyMCA. In HTML there is included the relevant part of the electronic logbook (DonkiLOG). These data were acquired during the beamtime experiments 20180178 and 20192072 in the TwinMic soft X-ray microscopy beamline of Elettra Sincrotrone Trieste.

The data were acquired during the beamtime experiments 20180178 and 20192072 in the TwinMic soft X-ray microscopy beamline of Elettra Sincrotrone Trieste.
DOI: 10.6084/m9.figshare.20059679
title: Trusilk Study Complete PP Data

description: This was a multicentric, prospective, two-arm, parallel-group, randomized (1:1), single-blind study, conducted in two different centers between July 2020 and November 2021. The primary objective of the study was to compare tissue reaction/ inflammation with Trusilk® and Mersilk® silk sutures post primary closure of mucosa in subjects undergoing planned surgical removal of impacted mandibular third molar at 3 and 7 days. The secondary objectives were to compare the incidence of tissue reaction and infection, the effect of suture material on wound healing and other surgical outcomes, overall intraoperative handling, and the common post-surgical complications between the two groups, Trusilk® and Mersilk®. Post-screening (screening visit), the study participants underwent surgical procedures (baseline visit, Day 0). All routine aseptic precautions according to the existing standards were followed before, during and after the surgery. The subjects were treated as per the standard of care, after transferring to the observation room. Inflammation recording card was given to subjects after surgery to manually mark the level of pain, swelling and trismus, after onset of symptoms at the home. Follow-up visits were conducted regularly to verify subject’s well-being. The subjects were followed-up on Day 3, Day 7, Day 12, Day 30 and Month 3 post-surgery.
DOI: 10.6084/m9.figshare.20059679.v1
title: Trusilk Study Complete PP Data

description: This was a multicentric, prospective, two-arm, parallel-group, randomized (1:1), single-blind study, conducted in two different centers between July 2020 and November 2021. The primary objective of the study was to compare tissue reaction/ inflammation with Trusilk® and Mersilk® silk sutures post primary closure of mucosa in subjects undergoing planned surgical removal of impacted mandibular third molar at 3 and 7 days. The secondary objectives were to compare the incidence of tissue reaction and infection, the effect of suture material on wound healing and other surgical outcomes, overall intraoperative handling, and the common post-surgical complications between the two groups, Trusilk® and Mersilk®. Post-screening (screening visit), the study participants underwent surgical procedures (baseline visit, Day 0). All routine aseptic precautions according to the existing standards were followed before, during and after the surgery. The subjects were treated as per the standard of care, after transferring to the observation room. Inflammation recording card was given to subjects after surgery to manually mark the level of pain, swelling and trismus, after onset of symptoms at the home. Follow-up visits were conducted regularly to verify subject’s well-being. The subjects were followed-up on Day 3, Day 7, Day 12, Day 30 and Month 3 post-surgery.
DOI: 10.5517/ccdc.csd.cc29hvh5
title: CCDC 2130397: Experimental Crystal Structure Determination

description: Related Article: Chao Yu, Chen-Yu Cao, Pei-Dong Shi, An-An Yang, Ying-Xiang Yang, De-Seng Huang, Xin Chen, Zi-Ming Chen, Jin-Ming Gao, Xia Yin|2022|J.Ethnopharmacol.|295|115393|doi:10.1016/j.jep.2022.115393
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc29hvj6
title: CCDC 2130398: Experimental Crystal Structure Determination

description: Related Article: Chao Yu, Chen-Yu Cao, Pei-Dong Shi, An-An Yang, Ying-Xiang Yang, De-Seng Huang, Xin Chen, Zi-Ming Chen, Jin-Ming Gao, Xia Yin|2022|J.Ethnopharmacol.|295|115393|doi:10.1016/j.jep.2022.115393
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc29m267
title: CCDC 2133488: Experimental Crystal Structure Determination

description: Related Article: Suvendu Halder, Rupa Bai Addanki, Sangay Moktan, Pavan K. Kancharla|2022|J.Org.Chem.|87|7033|doi:10.1021/acs.joc.2c00093
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc29y6j0
title: CCDC 2143232: Experimental Crystal Structure Determination

description: Related Article: Ania Sotuyo, Christopher A. V. Swendris, Kuthuru Suresh, Adam J. Matzger|2022|Cryst.Growth Des.|22|3581|doi:10.1021/acs.cgd.2c00147
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc29y6l2
title: CCDC 2143234: Experimental Crystal Structure Determination

description: Related Article: Ania Sotuyo, Christopher A. V. Swendris, Kuthuru Suresh, Adam J. Matzger|2022|Cryst.Growth Des.|22|3581|doi:10.1021/acs.cgd.2c00147
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.