cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8035346
title: WHUS2-CRv a global thin cloud removal dataset for Sentinel-2 images——Train part

description: The training parts of WHUS2-CRv dataset in which the paired cloud and cloud-free Sentinel-2 images are from different regions of the world. The types of land cover are rich and the acquisition dates of the experimental data cover a long time period (from 2015 to 2020) and all seasons. The validation and testing parts can be found on: https://doi.org/10.5281/zenodo.8035349 If you use this dataset for your research, please cite us accordingly: #Reference: [1]J. Li, Z. W, Z. Hu, J. Z, M. Li, L. Mo and M. Molinier, “Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion,” ISPRS J. Photogramm. Remote Sens., vol. 166, pp. 373-389, Aug. 2020,http://doi.org/10.1016/j.isprsjprs.2020.06.021. [2]J. Li, Z. Wu, Z. Hu, Z. Li, Y. Wang, and M. Molinier, “Deep learning based thin cloud removal fusing vegetation red edge and short wave infrared spectral information for Sentinel-2A imagery,” Remote Sens., vol. 13, no. 1, p. 157, Jan. 2021, http://doi.org/10.3390/rs13010157. [3]J. Li, Y. Zhang, Q. Sheng, Z. Wu, B. Wang, Z. Hu, G. Shen, M. Schmitt, M. Molinier, “Thin Cloud Removal Fusing Full Spectral and Spatial Features for Sentinel-2 Imagery,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 8759-8775, 2022, http://doi.org/10.1109/JSTARS.2022.3211857.
DOI: 10.5281/zenodo.8035349
title: WHUS2-CRv a global thin cloud removal dataset for Sentinel-2 images——Validation and testing parts

description: The validation and testing parts of WHUS2-CRv dataset in which the paired cloud and cloud-free Sentinel-2 images are from different regions of the world. The types of land cover are rich and the acquisition dates of the experimental data cover a long time period (from 2015 to 2020) and all seasons. If you use this dataset for your research, please cite us accordingly: #Reference: [1]J. Li, Z. W, Z. Hu, J. Z, M. Li, L. Mo and M. Molinier, “Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion,” ISPRS J. Photogramm. Remote Sens., vol. 166, pp. 373-389, Aug. 2020,http://doi.org/10.1016/j.isprsjprs.2020.06.021. [2]J. Li, Z. Wu, Z. Hu, Z. Li, Y. Wang, and M. Molinier, “Deep learning based thin cloud removal fusing vegetation red edge and short wave infrared spectral information for Sentinel-2A imagery,” Remote Sens., vol. 13, no. 1, p. 157, Jan. 2021, http://doi.org/10.3390/rs13010157. [3]J. Li, Y. Zhang, Q. Sheng, Z. Wu, B. Wang, Z. Hu, G. Shen, M. Schmitt, M. Molinier, “Thin Cloud Removal Fusing Full Spectral and Spatial Features for Sentinel-2 Imagery,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 8759-8775, 2022, doi: 10.1109/JSTARS.2022.3211857.
DOI: 10.5281/zenodo.8035348
title: WHUS2-CRv a global thin cloud removal dataset for Sentinel-2 images——Validation and testing parts

description: The validation and testing parts of WHUS2-CRv dataset in which the paired cloud and cloud-free Sentinel-2 images are from different regions of the world. The types of land cover are rich and the acquisition dates of the experimental data cover a long time period (from 2015 to 2020) and all seasons. If you use this dataset for your research, please cite us accordingly: #Reference: [1]J. Li, Z. W, Z. Hu, J. Z, M. Li, L. Mo and M. Molinier, “Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion,” ISPRS J. Photogramm. Remote Sens., vol. 166, pp. 373-389, Aug. 2020,http://doi.org/10.1016/j.isprsjprs.2020.06.021. [2]J. Li, Z. Wu, Z. Hu, Z. Li, Y. Wang, and M. Molinier, “Deep learning based thin cloud removal fusing vegetation red edge and short wave infrared spectral information for Sentinel-2A imagery,” Remote Sens., vol. 13, no. 1, p. 157, Jan. 2021, http://doi.org/10.3390/rs13010157. [3]J. Li, Y. Zhang, Q. Sheng, Z. Wu, B. Wang, Z. Hu, G. Shen, M. Schmitt, M. Molinier, “Thin Cloud Removal Fusing Full Spectral and Spatial Features for Sentinel-2 Imagery,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 8759-8775, 2022, doi: 10.1109/JSTARS.2022.3211857.
DOI: 10.34740/kaggle/dsv/5943312
title: Pypi sython panda
DOI: 10.5281/zenodo.7141424
title: IssueReports

description: <pre># IssueReports -*-*-*-*-* Abstract *-*-*-*-*- The IssueReports dataset contains issues acquired from popular projects between 2017-2022.09. The popular projects are the 36 projects with more than 10,000 closed Issues since 2017. This data was collected with PyGitHub and GitHub API v3. We use this dataset in our TOSEM project (the details will be found in the paper). -*-*-*-*- Components -*-*-*-*- IssueReports/ ├─ PROJECT(FIRST)/ │ ├─ _index.csv │ ├─ _repo_raw_data.json │ └─ issue#(FIRST)/ │ │ ├─ issue_raw_data.json │ │ ├─ comments_raw_data.json │ │ ├─ img_urls.csv │ │ └─ vid_urls.csv │ . │ . │ . │ └─ issue#(LAST)/ . . . └─ PROJECT(LAST)/ -*-*-*-*-*- Details -*-*-*-*-*- ## _index.csv This file contains partial information about the Issues included in the project. - repo_org This refers to the name of the project owner. - issue_title This refers to the title of the issue. - issue_number This refers to the identification number of the issue. - issue_state This refers to the state (i.e., open/closed) of the issue. - issue_created_at This refers to the time when the issue was created. - issue_created_by This refers to the user name who created the issue. - issue_closed_at This refers to the time when the issue was closed. - issue_closed_by This refers to the user name who closed the issue. - issue_labels This refers to the labels attached to the issue. - num_of_vid This refers to the number of videos pasted into the issue description. Note that they must be registered in the GitHub database (i.e., https://user-images.githubusercontent.com/). - num_of_img This refers to the number of images pasted into the issue description. Note that they must be registered in the GitHub database (i.e., https://user-images.githubusercontent.com/). - First_comment_time This refers to the time when the first comment on the issue was received. - num_of_comments This refers to the number of comments on the issue. The number of comments in comments_raw_data.json and the number of comments in _index.csv may differ (very rarely). This is because there is a delay in data collection timing between _index.csv and comments_raw_data.json. comments_raw_data.json should be given priority. ## _repo_raw_data.json This file contains APIv3-available information about the project. Details can be found on the PyGitHub documentation site. ## issue_raw_data.json This file contains APIv3-available information about the issue. Details can be found on the PyGitHub documentation site. ## comments_raw_data.json This file contains APIv3-available information about all comments attached to the issue. Details can be found on the PyGitHub documentation site. ## img_urls.csv This file contains links to images pasted into the issue description. ## vid_urls.csv This file contains links to videos pasted into the issue description. -*-*-*-*-* Reference *-*-*-*-*- [PyGitHub](https://github.com/PyGithub/PyGithub) [PyGitHub Document](https://pygithub.readthedocs.io/en/latest/introduction.html) [GitHub REST API](https://docs.github.com/en/rest) </pre>
DOI: 10.5281/zenodo.7155234
title: IssueReports

description: <pre># IssueReports -*-*-*-*-* Abstract *-*-*-*-*- The IssueReports dataset contains GitHub issues acquired from popular projects between 2017-2022.06. The popular projects are the 36 publicly available projects with more than 10,000 closed Issues since 2017. This data was collected with PyGitHub and GitHub API v3. We use this dataset in our TOSEM project (the details will be found in the paper). -*-*-*-*- Components -*-*-*-*- IssueReports/ ├─ PROJECT(FIRST)/ │ ├─ _index.csv │ ├─ _repo_raw_data.json │ └─ issue#(FIRST)/ │ │ ├─ issue_raw_data.json │ │ ├─ comments_raw_data.json │ │ ├─ img_urls.csv │ │ └─ vid_urls.csv │ . │ . │ . │ └─ issue#(LAST)/ . . . └─ PROJECT(LAST)/ -*-*-*-*-*- Details -*-*-*-*-*- ## _index.csv This file contains partial information about the Issues included in the project. - repo_org This refers to the name of the project owner. - issue_title This refers to the title of the issue. - issue_number This refers to the identification number of the issue. - issue_state This refers to the state (i.e., open/closed) of the issue. - issue_created_at This refers to the time when the issue was created. - issue_created_by This refers to the user name who created the issue. - issue_closed_at This refers to the time when the issue was closed. - issue_closed_by This refers to the user name who closed the issue. - issue_labels This refers to the labels attached to the issue. - num_of_vid This refers to the number of videos pasted into the issue description. Note that they must be registered in the GitHub database (i.e., https://user-images.githubusercontent.com/). - num_of_img This refers to the number of images pasted into the issue description. Note that they must be registered in the GitHub database (i.e., https://user-images.githubusercontent.com/). - First_comment_time This refers to the time when the first comment on the issue was received. - num_of_comments This refers to the number of comments on the issue. The number of comments in comments_raw_data.json and the number of comments in _index.csv may differ (very rarely). This is because there is a delay in data collection timing between _index.csv and comments_raw_data.json. comments_raw_data.json should be given priority. ## _repo_raw_data.json This file contains APIv3-available information about the project. Details can be found on the PyGitHub documentation site. ## issue_raw_data.json This file contains APIv3-available information about the issue. Details can be found on the PyGitHub documentation site. ## comments_raw_data.json This file contains APIv3-available information about all comments attached to the issue. Details can be found on the PyGitHub documentation site. ## img_urls.csv This file contains links to images pasted into the issue description. ## vid_urls.csv This file contains links to videos pasted into the issue description. -*-*-*-*-* Reference *-*-*-*-*- [PyGitHub](https://github.com/PyGithub/PyGithub) [PyGitHub Document](https://pygithub.readthedocs.io/en/latest/introduction.html) [GitHub REST API](https://docs.github.com/en/rest) </pre>
DOI: 10.3886/e112824v1-139479
title: Replication data for: The Wage Effects of Offshoring: Evidence from Danish Matched Worker-Firm Data

description: We employ data that match the population of Danish workers to the universe of private-sector Danish firms, with product-level trade flows by origin- and destination-countries. We document new stylized facts about offshoring and instrument for offshoring and exporting. Within job spells, offshoring increases (decreases) the high-skilled (low-skilled) wage; exporting increases the wages of all skill-types; the net wage-effect of trade varies substantially within the same skill-type; conditional on skill, the wage-effect of offshoring varies across task characteristics. We estimate the overall effects of offshoring on workers' present and future income streams by constructing pre-offshoring-shock worker-cohorts and tracking them over time.
DOI: 10.5281/zenodo.8045418
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Kathleen Elizabeth Ball, http://www.wikidata.org/entity/Q68797376. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q68797376
DOI: 10.5281/zenodo.8045417
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Kathleen Elizabeth Ball, http://www.wikidata.org/entity/Q68797376. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q68797376
DOI: 10.5281/zenodo.8045420
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Olaf Bang, http://www.wikidata.org/entity/Q106981054. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q106981054
DOI: 10.5281/zenodo.8045419
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Olaf Bang, http://www.wikidata.org/entity/Q106981054. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q106981054
DOI: 10.5281/zenodo.8045422
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Elso Sterrenberg Barghoorn, http://www.wikidata.org/entity/Q1333852. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q1333852
DOI: 10.5281/zenodo.8045421
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Elso Sterrenberg Barghoorn, http://www.wikidata.org/entity/Q1333852. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q1333852
DOI: 10.5281/zenodo.8045425
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Rudolf Baschant, http://www.wikidata.org/entity/Q21484627. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q21484627
DOI: 10.5281/zenodo.8045424
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Rudolf Baschant, http://www.wikidata.org/entity/Q21484627. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q21484627
DOI: 10.5281/zenodo.8045429
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Pierre Basilewsky, http://www.wikidata.org/entity/Q18692146. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q18692146
DOI: 10.5281/zenodo.8045428
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Pierre Basilewsky, http://www.wikidata.org/entity/Q18692146. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q18692146
DOI: 10.5439/1888171
title: hsr1 (00)

description: hsr1
DOI: 10.6084/m9.figshare.23528805
title: Repeated measures social network analysis data

description: This is the data of our manuscript on social networks of students under academic streaming. It includes ego network measures of network size, multiplexity, network variation and turnovers.
DOI: 10.5281/zenodo.8045416
title: Multi scalar data integration reveals the immunopathological mechanisms associated with IgA nephropathy progression

description: IgA nephropathy (IgAN), the most common primary mesangial proliferative glomerulonephritis (MsPGN), represents the main cause of renal failure, while the precise pathogenetic mechanisms have not been fully determined. In this study, we employed multi-module data integration and functional experiment to explore the pathogenic programs underlying IgAN progression. Protein profiling of 21 IgAN samples showing progression and 28 samples without progression revealed that protein CXCL12, complement C3, and macrophage markers MRC1, and CD163 were negatively correlated with estimated glomerular filtration rate (eGFR) value, and poor prognosis (30% eGFR decline). Analysis of the single-cell RNA-sequencing (scRNA-seq) revealed that IgAN macrophages expressed high levels of CXCR4, PDGFB, TREM2, TNF, and complement C3, while Monocle pseudotime analysis suggested that these cells derived from the differentiation of infiltrating blood monocytes. Cross-species intercellular crosstalk analysis in human IgAN and ddY-mice IgAN model revealed that mesangial cells (MCs) in IgAN expressed high levels of CXCL12, CSF1 and PDGFRB and interacted with macrophages via the CXCL12-CXCR4, PDGFB-PDGFRB, and ITGAX/ITGAM-C3 axes. Interestingly, analysis of anti-Thy1.1 MsPGN scRNA-seq atlas revealed an inflammatory MCs (iMCs) phenotype which expressed Pdgfrb, Cxcl12, Csf1, and Il34 was associated with MsPGN injury process. Functional experiments revealed that specific blockade of the Cxcl12-Cxcr4 pathway significantly attenuated inflammatory injury, fibrosis, and decline of renal function in the MsPGN model. This study provides new insights into IgAN progression and may aid in the refinement of IgAN diagnosis and the optimization of treatment strategies.
DOI: 10.5281/zenodo.8045415
title: Multi scalar data integration reveals the immunopathological mechanisms associated with IgA nephropathy progression

description: IgA nephropathy (IgAN), the most common primary mesangial proliferative glomerulonephritis (MsPGN), represents the main cause of renal failure, while the precise pathogenetic mechanisms have not been fully determined. In this study, we employed multi-module data integration and functional experiment to explore the pathogenic programs underlying IgAN progression. Protein profiling of 21 IgAN samples showing progression and 28 samples without progression revealed that protein CXCL12, complement C3, and macrophage markers MRC1, and CD163 were negatively correlated with estimated glomerular filtration rate (eGFR) value, and poor prognosis (30% eGFR decline). Analysis of the single-cell RNA-sequencing (scRNA-seq) revealed that IgAN macrophages expressed high levels of CXCR4, PDGFB, TREM2, TNF, and complement C3, while Monocle pseudotime analysis suggested that these cells derived from the differentiation of infiltrating blood monocytes. Cross-species intercellular crosstalk analysis in human IgAN and ddY-mice IgAN model revealed that mesangial cells (MCs) in IgAN expressed high levels of CXCL12, CSF1 and PDGFRB and interacted with macrophages via the CXCL12-CXCR4, PDGFB-PDGFRB, and ITGAX/ITGAM-C3 axes. Interestingly, analysis of anti-Thy1.1 MsPGN scRNA-seq atlas revealed an inflammatory MCs (iMCs) phenotype which expressed Pdgfrb, Cxcl12, Csf1, and Il34 was associated with MsPGN injury process. Functional experiments revealed that specific blockade of the Cxcl12-Cxcr4 pathway significantly attenuated inflammatory injury, fibrosis, and decline of renal function in the MsPGN model. This study provides new insights into IgAN progression and may aid in the refinement of IgAN diagnosis and the optimization of treatment strategies.
DOI: 10.5439/1984765
title: hsrl (00)

description: High-spectral-resolution lidar (HSRL) systems collect data about clouds and aerosols. These systems provide vertical profiles of optical depth, backscatter cross-section, depolarization, and backscatter phase function. All HSRL measurements are absolutely calibrated by reference to molecular scattering, which is measured at each point in the lidar profile. Like the Raman lidar, but unlike simple backscatter lidars such as the micropulse lidar, the HSRL can measure backscatter cross-sections and optical depths without prior assumptions about the scattering properties of the atmosphere. The depolarization observations also allow robust discrimination between ice and water clouds. In addition, rigorous error estimates can be computed for all measurements. A very narrow, angular field of view reduces multiple scattering contributions. The small field of view, coupled with a narrow optical bandwidth, nearly eliminates noise due to scattered sunlight. ARM operates two HSRL systems, one at the Barrow, North Slope of Alaska (NSA) site and the other in the second ARM Mobile Facility (AMF2), which is deployed at various locations on field campaigns.
DOI: 10.5439/1984774
title: oacomp1zhang (c1)

description: oacomp1zhang
DOI: 10.5281/zenodo.8045438
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Vladimír Balthasar, http://www.wikidata.org/entity/Q11903540. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q11903540
DOI: 10.5281/zenodo.8045437
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Vladimír Balthasar, http://www.wikidata.org/entity/Q11903540. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q11903540
DOI: 10.5281/zenodo.8045440
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Christen Thomsen Barfoed, http://www.wikidata.org/entity/Q512785. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q512785
DOI: 10.5281/zenodo.8045439
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Christen Thomsen Barfoed, http://www.wikidata.org/entity/Q512785. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q512785
DOI: 10.5281/zenodo.8045444
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Jean-Baptiste Barla, http://www.wikidata.org/entity/Q2415098. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q2415098
DOI: 10.5281/zenodo.8045443
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Jean-Baptiste Barla, http://www.wikidata.org/entity/Q2415098. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q2415098