cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8071675
title: Map Of Land Cover Agreement - MOLCA

description: Map Of Land Cover Agreement (MOLCA) is generated by reusing existing datasets for High-Resolution Land Cover (HRLC), but only select portions with unanimous agreement among multiple datasets. We combined multiple HRLCs using intersection methods, retaining only the areas where all datasets agree on the land cover classes and disregarding areas of disagreement. The following HRLCs were used: FROM-GLC 2017 ([1][2][3][4]), GL30 2020 ([5][6]), GHS BU S1NODSM 2016 ([7][8]), WSF 2019 ([9][10][11]), GSW 2019 ([12][13]), FNF 2018 ([14][15]), MapBiomas 2019 ([16][17]), CCI Africa Prototype 2016 ([18][19]), ESA DUE GlobPermafrost 2016 ([20][21] MOLCA contains around 117 billion 10-meter pixels (covering about 11.7 million square kilometers) distributed across a total area of 19 million square kilometers. It is available for 3 macro-regions in Siberia, Africa, and Amazon. The land cover classes represented in MOLCA are Bareland, Built-up, Cropland, Forest, Grassland, Shrubland, Water, Wetland, and Permanent ice and snow, covering the period between 2016 and 2020 The accuracy estimate for MOLCA indicates an Overall Accuracy (OA) of 96%. The repository contains: <strong>MOLCA_<em>nnxxx</em>_v1.tif </strong>x 2075: 2075 MOLCA tiles (893 in Africa, 658 in Amazon, 524 in Siberia). Tiles of MOLCA follow the Sentinel-2 Level-1C product tiling grid, and consequently, the identifier of MOLCA tiles (<em>nnxxx</em>) is the same as the corresponding Sentinel-2 Level-1C tile identifier. <strong>MOLCA_tiles_with_statistics.gpkg</strong>: Vector of MOLCA tile extents with statistics (number of pixels per class, total number of pixels, proportion of valid values) of each tile in the attribute table. <strong>MOLCA_legend.csv</strong>: Class code and labels of MOLCA The creation of the MOLCA dataset was done in the project Climate Change Initiative Extension (CCI+) Phase 1 New Essential Climate Variables (NEW ECVS) High Resolution Land Cover ECV (HR_LandCover_cci) funded by the European Space Agency (ESA) known under the abbreviation CCI HRLC or CCI+ HRLC [22].
DOI: 10.22002/9125q-yn792
title: Thesis Chapter 3 Supplementary Tables
DOI: 10.5281/zenodo.8071674
title: Map Of Land Cover Agreement - MOLCA

description: Map Of Land Cover Agreement (MOLCA) is generated by reusing existing datasets for High-Resolution Land Cover (HRLC), but only select portions with unanimous agreement among multiple datasets. We combined multiple HRLCs using intersection methods, retaining only the areas where all datasets agree on the land cover classes and disregarding areas of disagreement. The following HRLCs were used: FROM-GLC 2017 ([1][2][3][4]), GL30 2020 ([5][6]), GHS BU S1NODSM 2016 ([7][8]), WSF 2019 ([9][10][11]), GSW 2019 ([12][13]), FNF 2018 ([14][15]), MapBiomas 2019 ([16][17]), CCI Africa Prototype 2016 ([18][19]), ESA DUE GlobPermafrost 2016 ([20][21] MOLCA contains around 117 billion 10-meter pixels (covering about 11.7 million square kilometers) distributed across a total area of 19 million square kilometers. It is available for 3 macro-regions in Siberia, Africa, and Amazon. The land cover classes represented in MOLCA are Bareland, Built-up, Cropland, Forest, Grassland, Shrubland, Water, Wetland, and Permanent ice and snow, covering the period between 2016 and 2020 The accuracy estimate for MOLCA indicates an Overall Accuracy (OA) of 96%. The repository contains: <strong>MOLCA_<em>nnxxx</em>_v1.tif </strong>x 2075: 2075 MOLCA tiles (893 in Africa, 658 in Amazon, 524 in Siberia). Tiles of MOLCA follow the Sentinel-2 Level-1C product tiling grid, and consequently, the identifier of MOLCA tiles (<em>nnxxx</em>) is the same as the corresponding Sentinel-2 Level-1C tile identifier. <strong>MOLCA_tiles_with_statistics.gpkg</strong>: Vector of MOLCA tile extents with statistics (number of pixels per class, total number of pixels, proportion of valid values) of each tile in the attribute table. <strong>MOLCA_legend.csv</strong>: Class code and labels of MOLCA The creation of the MOLCA dataset was done in the project Climate Change Initiative Extension (CCI+) Phase 1 New Essential Climate Variables (NEW ECVS) High Resolution Land Cover ECV (HR_LandCover_cci) funded by the European Space Agency (ESA) known under the abbreviation CCI HRLC or CCI+ HRLC [22].
DOI: 10.5281/zenodo.8013279
title: Research data supporting: "Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling"

description: This repository contains the set of data shown in the paper "Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling" published on PNAS (DOI: 10.1073/pnas.2300565120).
DOI: 10.5281/zenodo.8013278
title: Research data supporting: "Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling"

description: This repository contains the set of data shown in the paper "Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling" published on PNAS (DOI: 10.1073/pnas.2300565120).
DOI: 10.5517/ccdc.csd.cc2d843y
title: CCDC 2212349: Experimental Crystal Structure Determination

description: Related Article: Jiaojiao Han, Hao Li, Honglei Shen, Ying Xu, Xuejuan Zou, Xi Kang, Manzhou Zhu|2023|Precis. Chem.|1|139|doi:10.1021/prechem.2c00007
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: 10.5281/zenodo.7934682
title: A large expert-curated cryo-EM image dataset for machine learning protein particle picking

description: Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking.
DOI: 10.5281/zenodo.7934683
title: A large expert-curated cryo-EM image dataset for machine learning protein particle picking

description: Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking.
DOI: 10.5517/ccdc.csd.cc2dfcv2
title: CCDC 2217394: Experimental Crystal Structure Determination

description: Related Article: Fatima Klongdee, Kittipong Chainok, Sujittra Youngme, Jaursup Boonmak|2023|CrystEngComm|25|3628|doi:10.1039/D3CE00384A
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.cc2dfcw3
title: CCDC 2217395: Experimental Crystal Structure Determination

description: Related Article: Fatima Klongdee, Kittipong Chainok, Sujittra Youngme, Jaursup Boonmak|2023|CrystEngComm|25|3628|doi:10.1039/D3CE00384A
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: 10.6073/pasta/b78fc5dbec3fcdfe4a53a009fce2e13d
title: Gap-filled precipitation data for Saddle, 1981 - ongoing, daily.

description: As part of its long-term climate data core collection, the Niwot Ridge LTER has collected daily precipitation at the Saddle site since 1981. The Saddle station is located at 3525 m.a.s.l. and is an important point location to capture local, ambient meteorological conditions for many biological and environmental datasets collected nearby. The location of the Saddle station has also presented challenges to its operation. Freezing temperatures, snow deposition from strong winds following storms, and exposure to lightning are some elements that have disrupted instrument functionality, affected data quality, and made access for research staff difficult over time, especially in winter months. Here we present a quality-controlled, gap-filled, daily precipitation time series corrected for blowing snow overcatch at the Saddle station. Methods follow those used to gap-fill long-term daily precipitation at the Niwot Ridge LTER D1 and C1 stations so there is consistency among core collection daily precipitation datasets. Metadata for this data package centralizes the most complete station history for Saddle precipitation and includes notes to data users on aspects and limitations of the dataset to consider when using these data in scientific analyses. Because of unresolved data quality concerns with winter precipitation the first several years of the record, gap-filled data for winter months (October–May) 1981-10-01 through 1987-05-30 are removed.
DOI: 10.6084/m9.figshare.21764129.v2
title: Data and code to reproduce "Thinning presence points does not improve species distribution model performance"

description: This repository contain the data and code to reproduce the analyses in:<br> Ten Caten, C., Dallas, T. "Thinning presence points does not improve species distribution model performance".<br> This repository is structured in a way such that the analyses in the manuscript can be run from different startig points. For that reason, codes are labelled with numbers 0-6, such that code 0 is the first step needed to reproduce the entire analytical process used in our simulations whereas code 6 is mostly plotting/running analyses.<br> In case the user decides to start from part 2, for example, the user can unzip the folder "2_Data", which contains all the data generated in parts 0-1 that is needed to run part 2. Similarly, to start from part 3, unzip the folder "3_Data" that has all the data generated in parts 0-2 needed to run part 3. The same idea is used for parts 4-5.<br> An important detail is that, if the user decides to run the analyses from part 1 ("1_LandScapeAndSps.R"), they should avoid sourcing part 0 ("0_SourceFunctions.R") as it is in the first line of code 1. Instead, they can run lines 1-119 to get what is needed to run part 1. Sourcing code 0 from part 1 will still work, but it will throw an error message that occurs because the object "LandScapesAndSps.RData" is needed to run the rest of code 0 (and generate some of the objects that are used in the later parts), but this object is only officially created in part 1. Otherwise, the codes can be run following the structure provided in the previous paragraph.
DOI: 10.5066/p92shg66
title: Compilation of multi-agency water temperature observations for streams within the Chesapeake Bay watershed

description: This data release collates stream water temperature observations across the Chesapeake Bay watershed from the USGS National Water Information System (NWIS), Water Quality Portal (WQP) and the USGS Aquarius (AQ) Time-Series database. Data retrieved from NWIS consists of aggregate (minimum, maximum and mean) daily values and continuous data from USGS monitoring stations. Values from the WQP contain discrete data from multiple agencies. The dataset compiled from AQ includes miscellaneous stream temperature observations collected during discharge measurements. This stream temperature data release was completed to support the USGS goal to make scientific data publicly usable, easily discoverable, and widely available. A subset of these data will be used and published in the future to assess the status and trends of key indicators of stream health in the Chesapeake Bay watershed.
DOI: 10.34740/kaggle/dsv/6040083
title: Cristiano Ronaldo | All Club Goals
DOI: 10.5517/ccdc.csd.cc2dnfs9
title: CCDC 2224181: Experimental Crystal Structure Determination

description: Related Article: Yating Dai, Hongchun Huang, Shuangshuang Liang, Yanli Yin, Xu Ban, Xiaowei Zhao, Zhiyong Jiang|2023|Org.Lett.|25|4551|doi:10.1021/acs.orglett.3c01585
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.cc2dpv76
title: CCDC 2225528: Experimental Crystal Structure Determination

description: Related Article: Samuel M. Bhutto, Brandon Q. Mercado, Patrick L. Holland|2023|Inorg.Chem.|62|9335|doi:10.1021/acs.inorgchem.3c00167
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.cc2f1hvv
title: CCDC 2235777: Experimental Crystal Structure Determination

description: Related Article: Zhen Zhang, Xiangshan Fang, Ayimnisa Aili, Shilong Wang, Junhong Tang, Wanjun Lin, Lei Xie, Jinchun Chen, Kai Sun|2023|Org.Lett.|25|4598|doi:10.1021/acs.orglett.3c01714
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: 10.15151/esrf-es-1225020562
title: In situ X-ray imaging of LPBF process - exploring exotic materials and new printing strategies

description: Laser Additive Manufacturing (LAM), has the potential to revolutionize manufacturing processes. It is critical to establish a scientific understanding of the process, especially when it comes to new materials and new printing strategies where little knowledge can be applied. At the European Synchrotron (ESRF), taking advantage of the recent Extremely Brilliant Source upgrade and the most advanced synchrotron material characterization techniques, the in-situ investigation will be used to establish a well-rounded picture of the LAM process. The outstanding photon flux density at ESRF can reach ultra-high temporal resolution at hard X-ray energies in combination with coherence levels which allows for imaging with high sensitivity. Combining fast synchrotron radiography (&gt; 40 kHz) with an in-situ LAM rig, fast X-ray imaging enables the observation, of the laser-matter interaction, defects formation, material phase transformations and microstructural features evolution.
DOI: 10.6084/m9.figshare.23587770
title: RSSC Maximum-Likelihood tree

description: RSSC Maximum-Likelihood tree for the paper "Influence of insertion sequences on population structure of phytopathogenic bacteria in the <em>Ralstonia solanacearum</em> species complex" by Greenrod et al
DOI: 10.6084/m9.figshare.23587770.v1
title: RSSC Maximum-Likelihood tree

description: RSSC Maximum-Likelihood tree for the paper "Influence of insertion sequences on population structure of phytopathogenic bacteria in the <em>Ralstonia solanacearum</em> species complex" by Greenrod et al
DOI: 10.34740/kaggle/dsv/6039298
title: ⚔ Dota 2 Pro League Matches 2019-2023 ⛨
DOI: 10.6084/m9.figshare.23587563
title: Output of IBM simulations of giraffe populaiton from Tarangire

description: These data files are outputs from IBM simulations of: <br> Bond M, Lee DE, Paniw M. <strong>Extinction risks and mitigation for a megaherbivore, the giraffe, in a human-influenced landscape under climate change </strong> <br> The metadata is described in https://github.com/MariaPaniw/Masai_giraffe_ibm
DOI: 10.6084/m9.figshare.23587707
title: Research Data_Exploring Social Media Information Seeking During the COVID-19 Pandemic to Inform Health and Science Communication_Sage Open.csv

description: This study utilized a non-experimental descriptive survey design to examine social media information-seeking behaviors during the COVID-19 outbreak, particularly during lockdown periods. The objectives were to describe perceptions of COVID-19 information on social media, explore the platforms used during lockdown, identify groups of connections on social media, and determine if platform use varied based on connected groups. To gather data on information-seeking behaviors, an online survey was administered via Qualtrics, reaching 1,048 respondents in the United States through non-probability opt-in sampling. The survey included the perceptions of the information availability scale (Woolley & Propst, 2005) and an information-seeking behavior scale (Timmers & Glas, 2010) the information availability scale (Woolley & Propst, 2005), and some researcher-adapted Likert-type scales. The results revealed that more than 70% of respondents felt overwhelmed while searching for COVID-19 information, encountered difficulties in accessing and interpreting additional information, and sometimes even avoided news about the pandemic. Among social media platforms, Facebook, Instagram, and Twitter were the most popular for obtaining COVID-19 information. Notably, Facebook emerged as the most widely used platform during lockdowns. Furthermore, respondents primarily utilized Facebook to connect with friends and family during the pandemic, and those with larger social networks tended to access social media platforms more frequently. These findings highlight the significant role of Facebook in disseminating reliable information during the COVID-19 pandemic. They also emphasize the importance of implementing strategies to help individuals navigate the overwhelming amount of information, including misinformation, present on social media platforms, particularly during times of crisis. It is worth noting that there is limited generalizability due to US-centric sample.
DOI: 10.6084/m9.figshare.23587707.v1
title: Research Data_Exploring Social Media Information Seeking During the COVID-19 Pandemic to Inform Health and Science Communication_Oyugi Masambuka Lamm_Sage Open.csv

description: This study utilized a non-experimental descriptive survey design to examine social media information-seeking behaviors during the COVID-19 outbreak, particularly during lockdown periods. The objectives were to describe perceptions of COVID-19 information on social media, explore the platforms used during lockdown, identify groups of connections on social media, and determine if platform use varied based on connected groups. To gather data on information-seeking behaviors, an online survey was administered via Qualtrics, reaching 1,048 respondents in the United States through non-probability opt-in sampling. The survey included the perceptions of the information availability scale (Woolley & Propst, 2005) and an information-seeking behavior scale (Timmers & Glas, 2010) the information availability scale (Woolley & Propst, 2005), and some researcher-adapted Likert-type scales. The results revealed that more than 70% of respondents felt overwhelmed while searching for COVID-19 information, encountered difficulties in accessing and interpreting additional information, and sometimes even avoided news about the pandemic. Among social media platforms, Facebook, Instagram, and Twitter were the most popular for obtaining COVID-19 information. Notably, Facebook emerged as the most widely used platform during lockdowns. Furthermore, respondents primarily utilized Facebook to connect with friends and family during the pandemic, and those with larger social networks tended to access social media platforms more frequently. These findings highlight the significant role of Facebook in disseminating reliable information during the COVID-19 pandemic. They also emphasize the importance of implementing strategies to help individuals navigate the overwhelming amount of information, including misinformation, present on social media platforms, particularly during times of crisis. It is worth noting that there is limited generalizability due to US-centric sample.