cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2mpmcb
title: CCDC 2433852: Experimental Crystal Structure Determination

description: Related Article: Zechen Hao, Haolin Lu, Qihang Feng, Yunxin Zhang, Wenkai Zhao, Tianyin Shao, Xinyi Niu, Hebin Wang, Zhaoyu Wang, Bing Sun, Hao-Li Zhang, Guankui Long
|2025|Nano Lett.|25|12611|doi:10.1021/acs.nanolett.5c02909
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.cc2p01d5
title: CCDC 2472696: Experimental Crystal Structure Determination

description: Related Article: Xiangyu Chen, Mingxin Chen, Xuyan Wang, Zhenzhen Miao, Guangbao Wu, Chenlu He, Ligang Xu, Wenzhen Lv, Runfeng Chen|2025|Chem.Mater.|||doi:10.1021/acs.chemmater.5c02976
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.cc2p1kdq
title: CCDC 2474153: Experimental Crystal Structure Determination

description: Related Article: Zechen Hao, Haolin Lu, Qihang Feng, Yunxin Zhang, Wenkai Zhao, Tianyin Shao, Xinyi Niu, Hebin Wang, Zhaoyu Wang, Bing Sun, Hao-Li Zhang, Guankui Long
|2025|Nano Lett.|25|12611|doi:10.1021/acs.nanolett.5c02909
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.cc2ngy9d
title: CCDC 2457224: Experimental Crystal Structure Determination

description: Related Article: Yu-Lan Xie, Meng-Jun Lin, Meng-Qiang Li, Qian Lin, Pei-Guo Liu, Ya-Ting Zou, Le Shu, Lang Wang, Rui-Ying Ren, Gele Teri, Chang-Feng Wang, Yi Zhang, Da-Wei Fu|2025|Cryst.Growth Des.|||doi:10.1021/acs.cgd.5c01557
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.cc2psdc8
title: CCDC 2496100: Experimental Crystal Structure Determination

description: Related Article: Xiangyu Chen, Mingxin Chen, Xuyan Wang, Zhenzhen Miao, Guangbao Wu, Chenlu He, Ligang Xu, Wenzhen Lv, Runfeng Chen|2025|Chem.Mater.|||doi:10.1021/acs.chemmater.5c02976
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.cc2q240z
title: CCDC 2504490: Experimental Crystal Structure Determination

description: Related Article: Lauren E. McNamara, Aimei Zhou, Alexandra Krupinski, Yin Yang, Sophie W. Anferov, Christopher Melnychuk, Matthew D. Krzyaniak, Michael R. Wasielewski, Jan-Niklas Boyn, Richard D. Schaller, Lei Sun, John S. Anderson|2025|J.Am.Chem.Soc.|||doi:10.1021/jacs.5c12570
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.cc2qdytx
title: CCDC 2514900: Experimental Crystal Structure Determination

description: Related Article: M. DiGrandi, M.C. Neary, P.W.R. Corfield|2025|IUCrData|10|x251115|doi:10.1107/S2414314625011150
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: 10.15468/dl.68a7uu
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Brigham Young University Arthropod Museum",
 "TaxonKey is Notonecta irrorata"
 ]
}

 The dataset includes 20 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082322-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18190105
title: Multiple strategies and cues define baleen whale migration - DATA

description: This datasets includes materials related to a submitted paper titled "Multiple strategies and cues define baleen whale migration"
Short title: SRW migration route and cuesRelevant code/scripts using these datasets can be found here: https://github.com/leena-r/SRWs/tree/main/SRW%20migratory%20route%20and%20cues File "df_with_turn_and_inbound.csv" includes data used in scripts "SRW migration route and cues_L-turn dist to ice and compass bearing.R" and"SRW migration route and cues_plot extracted env variables during migration.R"File "extraction_of_env_variables__curr_bathy_ice_magnetic" includes data used in script"SRW migration route and cues_plot extracted env variables during migration.R"NOTE that scrip "SRW migratory route and cues_extract geomagnetic inclination values to locations.R"reads in a file called "extraction_of_env_variables__curr_bathy_ice.csv". This file has not been uploaded hereinstead you can read in file "extraction_of_env_variables__curr_bathy_ice_magnetic" (i.e. the final file outputed from script "SRW migration route and cues_plot extracted env variables during migration.R)and drop out column "mag_magi"File "data for speed calc_first oubound migration only_updated 20250819.csv" includes data used in script"SRW migration route and cues_speed comparison.R"Script "SRW migration route and cues_route choice analysis GLM.R"reads in a file "data summary for analysis_first outbound migration only_updated 20251104.csv"This has not been uploaded is here but is provided as Supplementary Table S1
DOI: 10.5281/zenodo.18190106
title: Multiple strategies and cues define baleen whale migration - DATA

description: This datasets includes materials related to a submitted paper titled "Multiple strategies and cues define baleen whale migration"
Short title: SRW migration route and cuesRelevant code/scripts using these datasets can be found here: https://github.com/leena-r/SRWs/tree/main/SRW%20migratory%20route%20and%20cues File "df_with_turn_and_inbound.csv" includes data used in scripts "SRW migration route and cues_L-turn dist to ice and compass bearing.R" and"SRW migration route and cues_plot extracted env variables during migration.R"File "extraction_of_env_variables__curr_bathy_ice_magnetic" includes data used in script"SRW migration route and cues_plot extracted env variables during migration.R"NOTE that scrip "SRW migratory route and cues_extract geomagnetic inclination values to locations.R"reads in a file called "extraction_of_env_variables__curr_bathy_ice.csv". This file has not been uploaded hereinstead you can read in file "extraction_of_env_variables__curr_bathy_ice_magnetic" (i.e. the final file outputed from script "SRW migration route and cues_plot extracted env variables during migration.R)and drop out column "mag_magi"File "data for speed calc_first oubound migration only_updated 20250819.csv" includes data used in script"SRW migration route and cues_speed comparison.R"Script "SRW migration route and cues_route choice analysis GLM.R"reads in a file "data summary for analysis_first outbound migration only_updated 20251104.csv"This has not been uploaded is here but is provided as Supplementary Table S1
DOI: 10.15468/dl.xkhqkj
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((73.270939 23.607065, 73.194303 23.601488, 73.119573 23.584896, 73.048607 23.557702, 72.983164 23.52058, 72.924865 23.474454, 72.875148 23.420468, 72.835234 23.359958, 72.806096 23.294423, 72.78843999999999 23.225483, 72.78268199999999 23.154839, 72.788945 23.08423, 72.807058 23.015396, 72.836557 22.950025, 72.876704 22.889723, 72.926501 22.835966, 72.98472 22.79007, 73.04993 22.753156, 73.120535 22.726126, 73.19480900000001 22.70964, 73.270939 22.704099, 73.347069 22.70964, 73.42134299999999 22.726126, 73.49194799999999 22.753156, 73.557158 22.79007, 73.615377 22.835966, 73.66517399999999 22.889723, 73.705321 22.950025, 73.73482 23.015396, 73.752933 23.08423, 73.759196 23.154839, 73.753438 23.225483, 73.735782 23.294423, 73.706644 23.359958, 73.66673 23.420468, 73.617013 23.474454, 73.55871399999999 23.52058, 73.49327099999999 23.557702, 73.42230499999999 23.584896, 73.34757500000001 23.601488, 73.270939 23.607065))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 30 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082323-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7gqbfv
title: Occurrence Download

description: A dataset containing 2752 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is one of (CC0 1.0, CC-BY 4.0)",
 "TaxonKey is Meles meles"
 ]
}

 The dataset includes 2752 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082325-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5sgz24
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((73.232041 23.5918, 73.15541399999999 23.586223, 73.080693 23.569631, 73.00973399999999 23.542436, 72.944299 23.505315, 72.88600700000001 23.459189, 72.83629500000001 23.405202, 72.796385 23.344693, 72.767251 23.279158, 72.74959699999999 23.210218, 72.74383899999999 23.139573, 72.750102 23.068965, 72.76821200000001 23.00013, 72.797708 22.93476, 72.83785 22.874457, 72.887641 22.8207, 72.945854 22.774803, 73.01105699999999 22.737889, 73.081654 22.710859, 73.155919 22.694373, 73.232041 22.688832, 73.30816299999999 22.694373, 73.382428 22.710859, 73.453025 22.737889, 73.51822799999999 22.774803, 73.576441 22.8207, 73.626232 22.874457, 73.666374 22.93476, 73.69587 23.00013, 73.71398000000001 23.068965, 73.720243 23.139573, 73.714485 23.210218, 73.696831 23.279158, 73.667697 23.344693, 73.627787 23.405202, 73.578075 23.459189, 73.519783 23.505315, 73.454348 23.542436, 73.38338899999999 23.569631, 73.308668 23.586223, 73.232041 23.5918))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 30 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082326-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18190267
title: 61-four-part-harmony covers A
DOI: 10.5281/zenodo.18190268
title: 61-four-part-harmony covers A
DOI: 10.15468/dl.nbbcqu
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bothrops chloromelas"
 ]
}

 The dataset includes 16 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082327-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6085/aa/marine_pub.10.1
title: Emergency Response Plan for Intertidal Abalone (non-oil spill emergencies) Full Protocol. Version Date: December 2025

description: Black abalone (Haliotis cracherodii) are marine mollusks that occur in rocky intertidal and shallow subtidal habitats from Mendocino County, California to Baja California, Mexico. In 2009, the National Marine Fisheries Service (NMFS) listed black abalone as Endangered under the Federal Endangered Species Act (ESA). The NMFS Final Black Abalone Recovery Plan (NMFS 2020) and associated Five-Year Recovery Implementation Strategy identify the following as a recovery priority: Develop a plan to remove black abalone in response to events such as oil spills, landslides, and vessel groundings.

 The purpose of this Emergency Response Plan (Plan) is to guide response to impacts or anticipated impacts on black abalone in emergency situations. We define an “emergency” to be any impact event that poses a threat to abalone in the intertidal zone. This Plan focuses on responses to non-spill events. A separate Oil Spill Response Plan is available and includes additional, specific guidance needed to respond to oil spill (and other chemical/contaminant spills) events that may affect black abalone.
DOI: 10.6085/aa/marine_pub.11.1
title: (:unkn)
DOI: 10.7910/dvn/qxx6jg
title: Replication Data for: You Have One Job: Political Institutions and Strategic Investments in Human Capital

description: The attached R code and data creates the images and underlying data in the tables in Political Institutions and Strategic Investments in Human Capital published in the American Review of Public Administration.
description: The files below contain a modified version of the data needed to replicate most of the tables and figures in Political Institutions and Strategic Investments in Human Capital published in the American Review of Public Administration. Due to a change in Texas state law, about how data on water utility operators is classified and how it can be disclosed the complete underlying data cannot be made publicly available. The attached files have been anonymized to contain no personally identifiable information. If you have any questions about the data please do not hesitate to contact Dr. Robin Rose Saywitz (robin.saywitz@slu.edu).
DOI: 10.5281/zenodo.18190244
title: Physical fitness field-test dataset in sport sciences university students
DOI: 10.5281/zenodo.18190243
title: Physical fitness field-test dataset in sport sciences university students
DOI: 10.5281/zenodo.18187489
title: UNITE conda environment

description: This record contains:

(1) the conda environment file (packed using conda-pack): R4_1.tar.gz

(2) an explicit package list installed in the env, which can be used to re-build the conda env when necessary: R4_1.explicit.txt

Please see GitHub for details in usage: https://github.com/nrlab-CRUK/UNITE-paper
DOI: 10.5281/zenodo.18187488
title: UNITE conda environment

description: This record contains:

(1) the conda environment file (packed using conda-pack): R4_1.tar.gz

(2) an explicit package list installed in the env, which can be used to re-build the conda env when necessary: R4_1.explicit.txt

Please see GitHub for details in usage: https://github.com/nrlab-CRUK/UNITE-paper
DOI: 10.7910/dvn/6stura
title: Amyloidosis Speakers Bureau (ASB) patient narratives: Impact on information-seeking behaviors among medical students

description: This dataset includes survey data set as well as mailings data used in this study.
The Amyloidosis Speakers Bureau arranges for amyloidosis patients to speak about their diagnostic and treatment experiences with medical students. Using survey of U.S. medical students (N=1,634) and ASB amyloidosis health information mailings (N=50), this study hypothesized that patients' narratives about their diagnostic and treatment journeys would positively impact medical students’ information-seeking behavior about this disease.
DOI: 10.58024/aguqd44783c4
title: Ernest Henry apatite 

description: Supplementary data package to "Tracking the evolution of mineral systems using apatite petrochronology and geochemistry: a case study from the Ernest Henry iron oxide-copper-gold (IOCG) deposit, Australia".

Wang, X., Kontonikas-Charos, A., Belousova, E., Dhnaram, C., Lisitsin, V., Zhou, R., 2025. Economic Geology.
DOI: 10.7910/dvn/uryasl
title: UN Peacekeeping Operations Personnel Authorization Dataset

description: This is the first release of the UN Peacekeeping Operations Personnel Authorization Dataset. The data cover the period from November 1, 1990, to December 31, 2024. The dataset includes monthly and annual authorized strength data for troops, military observers, and police personnel for all UN PKOs authorized by the UN Security Council (UNSC) during this period. It also provides a summary of all included PKOs. The dataset is available in two formats: .csv and .Rda.
DOI: 10.5281/zenodo.18190304
title: Simulation checkpoint for a local ocean domain and its diel cycle

description: This is for use with the Oceananigans.jl via ClimateModels.jl (v0.3.12 and above).

A six day spin-up was carried out (with Oceananigans.jl v0.103.1) and the resulting checkpoint is what's stored here.

See the ClimateModels.jl documentation for user direction on how to run the Oceananigans.jl notebook.
DOI: 10.15468/dl.yvz658
title: Occurrence Download

description: A dataset containing 1163214 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is Benin"
 ]
}

 The dataset includes 1163214 records from 597 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082324-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.31032609
title: Sparse Gaussianized Canonical Correlation Analysis with Applications to Portfolio Analysis

description: Canonical correlation analysis (CCA) is an important statistical technique that explores the linear relationships between two sets of variables. In this paper, we propose a new generalization of CCA named sparse Gaussianized CCA (SGCCA) for high-dimensional data analysis. SGCCA has a number of favorable properties. First, it is conceptually easy to comprehend and efficient to implement. Second, it not only yields sparse and nested canonical vectors, but is also invariant against monotone transformations of any of the variables and hence robust to heavy-tailed data which is a well-known issue that severely dampens the classical CCA. Furthermore, SGCCA is shown to enjoy both the estimation consistency and variable selection consistency under a semiparametric copula model with mild regularity conditions. Extensive simulations demonstrate the superior performance of SGCCA over existing CCA methods with or without the underlying data-generating model being a semiparametric copula model. An analysis of correlation structures between consumer cyclical and non-cyclical stocks is illustrated as an empirical application.
DOI: 10.6084/m9.figshare.31032609.v1
title: Sparse Gaussianized Canonical Correlation Analysis with Applications to Portfolio Analysis

description: Canonical correlation analysis (CCA) is an important statistical technique that explores the linear relationships between two sets of variables. In this paper, we propose a new generalization of CCA named sparse Gaussianized CCA (SGCCA) for high-dimensional data analysis. SGCCA has a number of favorable properties. First, it is conceptually easy to comprehend and efficient to implement. Second, it not only yields sparse and nested canonical vectors, but is also invariant against monotone transformations of any of the variables and hence robust to heavy-tailed data which is a well-known issue that severely dampens the classical CCA. Furthermore, SGCCA is shown to enjoy both the estimation consistency and variable selection consistency under a semiparametric copula model with mild regularity conditions. Extensive simulations demonstrate the superior performance of SGCCA over existing CCA methods with or without the underlying data-generating model being a semiparametric copula model. An analysis of correlation structures between consumer cyclical and non-cyclical stocks is illustrated as an empirical application.
DOI: 10.6084/m9.figshare.31032610
title: Trans-Glasso: A Transfer Learning Approach to Precision Matrix Estimation

description: Precision matrix estimation is essential in various fields, yet it is challenging when samples for the target study are limited. Transfer learning can enhance estimation accuracy by leveraging data from related source studies. We propose Trans-Glasso, a two-step transfer learning method for precision matrix estimation. First, we obtain initial estimators using a multi-task learning objective that captures both shared and unique features across studies. Then, we refine these estimators through differential network estimation to adjust for structural differences between the target and source precision matrices. Under the assumption that most entries of the target precision matrix are shared with those of the source matrices, we derive non-asymptotic error bounds and show that Trans-Glasso achieves minimax optimality under certain conditions. Extensive simulations demonstrate Trans-Glasso’s superior performance compared to baseline methods, particularly in small-sample settings. We further validate Trans-Glasso in applications to gene networks across brain tissues and protein networks for various cancer subtypes, showcasing its effectiveness in biological contexts. Additionally, we derive the minimax optimal rate for differential network estimation, representing the first such guarantee in this area.