cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15645029
title: Joseph San Nicolas Farm (RGB) - June 9 2025

description: "This orthomosaic of Talofofo, Guam was created from 109 images taken on June 9 2025, at 100 ft AGL, with a DJI Matrice 350. The mission was planned using the DJI Pilot application and post-processed using Pix4D .

 

This project was made possible through funding by the following grant(s): NACD (Award No. HQ00342020012)."
description: Disclaimer: The following aerial imagery is intended solely for research purposes related to the assessment of the captured area. It is strictly prohibited to distribute this imagery to any unauthorized individuals or third-party entities without prior authorization.

The University of Guam Drone Corps funded by NASA Guam Space Grant and NASA Guam EPSCoR retain all rights to the aerial imagery, including but not limited to copyrights and intellectual property rights. Any unauthorized modification, reproduction, or distribution of the imagery is strictly prohibited.

By using this data, you acknowledge and agree to abide by the terms and conditions set forth in this disclaimer.

 

 

Privacy Disclaimer: The aerial imagery captured for assessment may inadvertently include images of private property and individuals. While every effort has been made to respect privacy, it is possible that identifiable information or sensitive locations may be visible in the imagery. University of Guam Drone Corps and related parties disclaim any responsibility for the unintentional collection or dissemination of such information. The recipient(s) of this imagery are urged to handle it with due care and take necessary precautions to protect the privacy rights of individuals and comply with applicable privacy laws and regulations.
DOI: 10.5281/zenodo.15645030
title: Joseph San Nicolas Farm (RGB) - June 9 2025

description: "This orthomosaic of Talofofo, Guam was created from 109 images taken on June 9 2025, at 100 ft AGL, with a DJI Matrice 350. The mission was planned using the DJI Pilot application and post-processed using Pix4D .

 

This project was made possible through funding by the following grant(s): NACD (Award No. HQ00342020012)."
description: Disclaimer: The following aerial imagery is intended solely for research purposes related to the assessment of the captured area. It is strictly prohibited to distribute this imagery to any unauthorized individuals or third-party entities without prior authorization.

The University of Guam Drone Corps funded by NASA Guam Space Grant and NASA Guam EPSCoR retain all rights to the aerial imagery, including but not limited to copyrights and intellectual property rights. Any unauthorized modification, reproduction, or distribution of the imagery is strictly prohibited.

By using this data, you acknowledge and agree to abide by the terms and conditions set forth in this disclaimer.

 

 

Privacy Disclaimer: The aerial imagery captured for assessment may inadvertently include images of private property and individuals. While every effort has been made to respect privacy, it is possible that identifiable information or sensitive locations may be visible in the imagery. University of Guam Drone Corps and related parties disclaim any responsibility for the unintentional collection or dissemination of such information. The recipient(s) of this imagery are urged to handle it with due care and take necessary precautions to protect the privacy rights of individuals and comply with applicable privacy laws and regulations.
DOI: 10.25549/whit-c170-5521
title: dw_Volume74
DOI: 10.25549/whit-c170-5533
title: dw_Volume75
DOI: 10.25549/whit-c170-11473
title: dw_Volume157
DOI: 10.25549/whit-ouc113790427
title: Army/Navy "E" Award, Shell Chemical Co., Long Beach, Calif., 1944

description: 25 photographs of Army/Navy "E" Award, Shell Chemical Company, 20945 Wilmington Avenue, Long Beach, California, USA, 27 January 1944. "#228516". Client: Shell Chemical Company (Mr. Norton, Box 728, Wilmington, Newmark 1-6141). "Colonel Earle R. Sarles, commanding officer of Camp Anza, made the presentation." -- Long Beach Sun (28 January 1944): p. 5.
DOI: 10.25549/whit-ouc113790778
title: Army/Navy "E" Award, Shell Chemical Co., Long Beach, Calif., 1944

description: 25 photographs of Army/Navy "E" Award, Shell Chemical Company, 20945 Wilmington Avenue, Long Beach, California, USA, 27 January 1944. "#228516". Client: Shell Chemical Company (Mr. Norton, Box 728, Wilmington, Newmark 1-6141). "Colonel Earle R. Sarles, commanding officer of Camp Anza, made the presentation." -- Long Beach Sun (28 January 1944): p. 5.
DOI: 10.25549/whit-ouc113791253
title: Army/Navy "E" Award, Shell Chemical Co., Long Beach, Calif., 1944

description: 25 photographs of Army/Navy "E" Award, Shell Chemical Company, 20945 Wilmington Avenue, Long Beach, California, USA, 27 January 1944. "#228516". Client: Shell Chemical Company (Mr. Norton, Box 728, Wilmington, Newmark 1-6141). "Colonel Earle R. Sarles, commanding officer of Camp Anza, made the presentation." -- Long Beach Sun (28 January 1944): p. 5.
DOI: 10.25549/whit-ouc113790826
title: GAR members & Red Cross workers & blood donor truck, Los Angeles, Calif., 1944

description: 7 photographs of Grand Army of the Republic (GAR) members with Red Cross truck and Red Cross workers, Los Angeles, California, USA, 28 January 1944. Client: American Red Cross (Miss Mills, 1200 South Vemont, FI.5261). Operator: Vannice.
DOI: 10.25549/whit-ouc113791155
title: Filtrol Corp. products such as Humitrol & Percol, Los Angeles, Calif., 1944

description: 7 photographs of Humitrol, Percol, and other products of the Filtrol Corporation, Los Angeles, California, USA, 28 January 1944. Client: The Filtrol Corporation. Agency: deliver to Heinz Pickering (Carl Heinz). Operator: Cross.
DOI: 10.25549/whit-c170-11474
title: dw_Volume157
DOI: 10.25549/whit-c170-11476
title: dw_Volume157
DOI: 10.25549/whit-c170-5587
title: dw_Volume75
DOI: 10.25549/whit-c170-5588
title: dw_Volume75
DOI: 10.25549/whit-c170-6318
title: dw_Volume85
DOI: 10.15468/dl.tw6bx9
title: Occurrence Download

description: A dataset containing 123251 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Cirsium palustre",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 123251 records from 256 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000934-260226173443078/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15517960
title: 2018-London - L0

description: University of Reading  Grimmond Urban Micromet Group

City: London

Year: 2018

Sites: mutiple

Data Levels: L0

See also: Other levels, Other years, meta data
DOI: 10.5281/zenodo.15517961
title: 2018-London - L0

description: University of Reading  Grimmond Urban Micromet Group

City: London

Year: 2018

Sites: mutiple

Data Levels: L0

See also: Other levels, Other years, meta data
DOI: 10.5517/ccdc.csd.cc2jgp90
title: CCDC 2337812: Experimental Crystal Structure Determination

description: Related Article: Disha P. Vala, Bhavesh N. Socha, Victoria G. Collins, Mehul P. Parmar, Chirag D. Patel, Savan S. Bhalodiya, Subham G. Patel, Sourav Banerjee, Hitendra M. Patel|2025|RSC Advances|15|15712|doi:10.1039/D5RA02166A
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.15528486
title: Hybrid model results for the interaction between the Moon and the solar wind

description: Simulation runs. Parameter files, log files, and plot files. Ordered in directories for the different simulation runs. Each directory contains an HDF5  file with the actual data.
DOI: 10.5281/zenodo.15528487
title: Hybrid model results for the interaction between the Moon and the solar wind

description: Simulation runs. Parameter files, log files, and plot files. Ordered in directories for the different simulation runs. Each directory contains an HDF5  file with the actual data.
DOI: 10.5517/ccdc.csd.cc2jpp53
title: CCDC 2344535: Experimental Crystal Structure Determination

description: Related Article: Yukun Zhao, Wei He, Jingwen Xu, Jinglong Wang, Lin Gan, Lin Hu|2025|Org.Lett.|27|5146|doi:10.1021/acs.orglett.5c01285
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: 10.15156/bio/sh1422297.10fu
title: SH1422297.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1422298.10fu
title: SH1422298.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1422299.10fu
title: SH1422299.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1422300.10fu
title: SH1422300.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1422301.10fu
title: SH1422301.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1422302.10fu
title: SH1422302.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1422303.10fu
title: SH1422303.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1422304.10fu
title: SH1422304.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1422305.10fu
title: SH1422305.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15468/dl.6deuz2
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eleocharis dulcis",
 "Country is Micronesia (Federated States of)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086666-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ythg6s
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Lepthoplosternum ucamara)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 8 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086667-250717081556266/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.29304439
title: Spatial von-Mises Fisher Regression for Directional Data

description: Spatially varying directional data are routinely observed in several modern applications such as meteorology, biology, geophysics, engineering, etc. However, only a few approaches are available for covariate-dependent statistical analysis for such data. To address this gap, we propose a novel generalized linear model to analyze such data using a von Mises Fisher (vMF) distributed error structure. Using a novel link function that relies on the transformation between Cartesian and spherical coordinates, we regress the vMF-distributed directional data on the external covariates. This regression model enables us to quantify the impact of external factors on the observed directional data. Furthermore, we impose the spatial dependence using an autoregressive model, appropriately accounting for the directional dependence in the outcome. This novel specification renders computational efficiency and flexibility. In addition, a comprehensive Bayesian inferential toolbox is thoroughly developed and applied to our analysis. Subsequently, employing our regression model on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we gain new insights into the relationship between cognitive impairment and the orientations of brain fibers, along with examining empirical efficacy through simulation experiments. The code for implementing our proposed method is available on GitHub: https://github.com/lanzhouBWH/Spatial_VMF_Regression.
DOI: 10.6084/m9.figshare.29304439.v1
title: Spatial von-Mises Fisher Regression for Directional Data

description: Spatially varying directional data are routinely observed in several modern applications such as meteorology, biology, geophysics, engineering, etc. However, only a few approaches are available for covariate-dependent statistical analysis for such data. To address this gap, we propose a novel generalized linear model to analyze such data using a von Mises Fisher (vMF) distributed error structure. Using a novel link function that relies on the transformation between Cartesian and spherical coordinates, we regress the vMF-distributed directional data on the external covariates. This regression model enables us to quantify the impact of external factors on the observed directional data. Furthermore, we impose the spatial dependence using an autoregressive model, appropriately accounting for the directional dependence in the outcome. This novel specification renders computational efficiency and flexibility. In addition, a comprehensive Bayesian inferential toolbox is thoroughly developed and applied to our analysis. Subsequently, employing our regression model on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we gain new insights into the relationship between cognitive impairment and the orientations of brain fibers, along with examining empirical efficacy through simulation experiments. The code for implementing our proposed method is available on GitHub: https://github.com/lanzhouBWH/Spatial_VMF_Regression.