cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.p8vh9u
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acipenser nudiventris Lovetsky, 1828",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is Uzbekistan"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013357-241007104925546/datasets/export for details.
DOI: 10.15468/dl.n75zfg
title: Occurrence Download

description: A dataset containing 81 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Boophis opisthodon (Boulenger, 1888)"
 ]
}

 The dataset includes 81 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013358-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r8temd
title: Occurrence Download

description: A dataset containing 38425 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is RBINS Fish collection"
 ]
}

 The dataset includes 38425 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013360-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sekuxq
title: Occurrence Download

description: A dataset containing 273 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ochotona curzoniae (Hodgson, 1858)"
 ]
}

 The dataset includes 273 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013361-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ksnkyu
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Alburnus akili Battalgil, 1942",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is Turkey"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013362-241007104925546/datasets/export for details.
DOI: 10.6084/m9.figshare.27222696
title: Controls on the accumulation of low molecular weight dissolved organic matter in estuarine and coastal sediment porewaters

description: In this study, we analyzed the concentrations of dissolved organic carbon (DOC), the optical absorption and fluorescence properties of chromophoric dissolved organic matter (CDOM) in surface and bottom waters, and sediment porewaters, in the Pearl River Estuary and adjacent Coasts (PREC) in December 2021 and April 2022. The overarching objective of this study was to examine the spatial and temporal variations, as well as the controlling mechanisms, of the concentrations, sources and composition of DOM in the PREC.
DOI: 10.6084/m9.figshare.27222696.v1
title: Controls on the accumulation of low molecular weight dissolved organic matter in estuarine and coastal sediment porewaters

description: In this study, we analyzed the concentrations of dissolved organic carbon (DOC), the optical absorption and fluorescence properties of chromophoric dissolved organic matter (CDOM) in surface and bottom waters, and sediment porewaters, in the Pearl River Estuary and adjacent Coasts (PREC) in December 2021 and April 2022. The overarching objective of this study was to examine the spatial and temporal variations, as well as the controlling mechanisms, of the concentrations, sources and composition of DOM in the PREC.
DOI: 10.15468/dl.hywca3
title: Occurrence Download

description: A dataset containing 86 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Boophis periegetes Cadle, 1995"
 ]
}

 The dataset includes 86 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013364-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4p6447
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mozambique",
 "OccurrenceStatus is Present",
 "TaxonKey is Crotalaria schliebenii Polhill"
 ]
}

 The dataset includes 10 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013365-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9yn5dj
title: Occurrence Download

description: A dataset containing 860339 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mozambique",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 860339 records from 897 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013334-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5s4pcv
title: Occurrence Download

description: A dataset containing 38425 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is RBINS Fish collection"
 ]
}

 The dataset includes 38425 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013366-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ees2cg
title: Occurrence Download

description: A dataset containing 1732 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Specimen, Living Specimen, Occurrence evidence, Human Observation)",
 "TaxonKey is one of (Codonopsis pilosula (Franch.) Nannf., Codonopsis pilosula subsp. pilosula, Codonopsis pilosula subsp. handeliana (Nannf.) D.Y.Hong & L.M.Ma, Codonopsis pilosula var. glaberrima (Nannf.) P.C.Tsoong, Codonopsis pilosula var. modesta (Nannf.) L.D.Shen, Codonopsis pilosula var. volubilis (Nannf.) L.D.Shen, Codonopsis pilosula subsp. tangshen (Oliv.) D.Y.Hong, Codonopsis pilosula var. handeliana (Nannf.) L.D.Shen, Codonopsis pilosula var. pilosula)"
 ]
}

 The dataset includes 1732 records from 71 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013367-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/p35k2vsvkc.1
title: Bio-mediated flocculation of freshwater microplastics: effects of microalgae with exopolymer attachments

description: This dataset includes supplementary data for the experimental results of “Bio-mediated flocculation of freshwater microplastics: effects of microalgae with exopolymer attachments”
Dataset consist of the following items:
Table 1 – Examples of the measured cell density and TEP concentration of different microalgae
Table 2 – Examples of floc sizes distribution of different microalgae during the incubation period
Table 3 – Examples of evolution of D50 of different microalgae at different incubation days
DOI: 10.15468/dl.wz8kxp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Alburnus nicaeensis Battalgil, 1941",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is Turkey"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013368-241007104925546/datasets/export for details.
DOI: 10.17632/x32x6rn5kv.1
title: Dataset for seismic stratigraphy interpretation

description: The dataset includes seismic data, seismic interpretation and maps
DOI: 10.17632/x45wvbd3sv.1
title: Python Code for Statistical Mirroring-based Ordinalysis

description: Statistical mirroring-based ordinalysis (SM-based ordinalysis) measures the proximity or deviation of an individual's composite set of ordinal assessment scores from the highest positive ordinal scale point. Within the framework of Kabirian-based optinalysis [1] and statistical mirroring [2], Statistical mirroring-based ordinalysis is conceptualized as the isoreflectivity (isoreflective pairing) of the composite set of ordinal assessment scores of an individual to the highest positive ordinal scale point of an established ordinal assessment scale, under customized and optimized choice of parameters. This represents the underlying assumption of statistical mirroring-based ordinalysis. 
The process of Statistical mirroring-based ordinalysis comprises three distinct phases: 
a) Adaptive customization and optimization phase [3]: This phase represents the core of the methodology. This involves the adaptive customization and optimization of parameters to suit the requirements for statistical mirroring estimation in the given task. 
b) Statistical mirroring computation phase [2]: This involves applying the adopted statistical mirroring type based on the phase 1 adaption. 
c) Optinalytic model calculation phase [1]: This phase is focused on computing estimates (such as the Kabirian coefficient of proximity, the probability of proximity, and the deviation) based on Kabirian-based isomorphic optinalysis models. 

References:
[1] K.B. Abdullahi, Kabirian-based optinalysis: A conceptually grounded framework for symmetry/asymmetry, similarity/dissimilarity, and identity/unidentity estimations in mathematical structures and biological sequences, MethodsX 11 (2023) 102400. doi: 10.1016/j.mex.2023.102400
[2] K.B. Abdullahi, Statistical mirroring: A robust method for statistical dispersion estimation, MethodsX 12 (2024) 102682. https://doi.org/10.1016/j.mex.2024.102682 
[3] K.B. Abdullahi, Statistical mirroring-based ordinalysis: A sensitive, robust, and efficient methodology for analysis of ordinal assessments data, (2024). [You can follow the published version of the paper for the other reference details).
DOI: 10.15468/dl.zp4djx
title: Occurrence Download

description: A dataset listing the 342 species recorded in GBIF matching the query:
{
 "and" : [
 "GadmGid is ESP.8.1_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves",
 "Year 2021-2021"
 ]
}

 The dataset's 342 records were derived from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013338-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/bkb7dr9pd2.1
title: Fluid inclusion and O isotope data of Minami-Ensei hydrothermal barite

description: Microthermometry and O isotope data of fluid inclusions in Minami-Ensei hydrothermal barite.
DOI: 10.17632/fbzf8p3jhc.1
title: ETS and biotechnology patents

description: ETS DID and biotechnology patents
DOI: 10.15468/dl.ddu6ak
title: Occurrence Download

description: A dataset containing 127 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "PublishingOrg is f891104a-681f-48df-b6a1-68fd1938b44f",
 "TaxonKey is Vaccinium L."
 ]
}

 The dataset includes 127 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013369-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vsnzb8
title: Occurrence Download

description: A dataset containing 394 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "MediaType is Audio",
 "TaxonKey is Pseudochorthippus parallelus (Zetterstedt, 1821)"
 ]
}

 The dataset includes 394 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013370-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/n8k6ss8hcg.1
title: Special economic zones upgrading and carbon emission reduction firm level data

description: In calculating the carbon emissions of listed companies, we use fossil energy consumption, electricity consumption, and heat consumption manually collected from the corporate social responsibility reports (CSR reports), sustainable development reports, and environmental reports disclosed annually by 3,352 listed Chinese companies from 2003 to 2021. We have manually sorted out the list and time of the upgrading of SEZs in 272 cities. The relevant information about the upgrading of SEZs comes from the China Development Zone Audit Announcement (2006 edition) released by the NDRC and other departments . This set of data records the details of state- and provincial-level SEZs that have been officially recognized after a large-scale rectification in 2003, including the names, types, and approval authorities of SEZs. The financial information of the listed companies involved in the control variables is all from the China Stock Market & Accounting Research Database (CSMAR Database).
DOI: 10.17632/np79pt4zdc.1
title: Etesetch

description: Hipótesis de Investigación
La investigación parte de la hipótesis de que los videos interactivos con subtítulos automáticos pueden mejorar significativamente la comprensión y retención de información en comparación con los videos tradicionales. Dado el creciente uso de videos en la educación en línea, la investigación busca explorar si la integración de tecnologías interactivas y de subtitulado dinámico aumenta el compromiso del usuario y la efectividad del aprendizaje.
DOI: 10.17632/yxjs9rrs8k.1
title: A model for evaluating the safety climate within a military aviation detachment during a long-term mission in hostile environments

description: This data base it is about a model designed to evaluate the safety climate within a military aviation detachment during a long-term mission in hostile environments. This model was tested during Romania's peacekeeping mission in Mali, Africa, in 2019-2020, where over 120 soldiers, including pilots and technicians, participated under UN auspices.

Objective
The goal of this research was to identify the key factors contributing to a high safety climate and to ensure the detachment operated safely in a dangerous environment. The mission's success, with zero accidents, highlights the importance of understanding and improving the safety climate.

Methodology
To evaluate the safety climate, a survey was distributed to 120 detachment members. The survey measured both organizational factors—such as task clarity and performance evaluation—and individual factors—like team spirit, trust in leaders, and motivation. We used a 10-point Likert scale for responses, and the results were analyzed using correlation and network analysis.

Key Findings
The results showed strong positive relationships between the safety climate and factors like team spirit, task clarity, and motivation. For example, task clarity had a correlation of r = 0.71, and team spirit showed r = 0.78. Interestingly, the impact of working conditions on the safety climate was insignificant.

Conclusion
This study demonstrates that clear tasks, team spirit, and motivation are crucial for maintaining a safe operational environment, especially in hostile situations. Future research could explore how safety climate evolves over time during long-term missions, but this model provides a valuable tool for improving safety outcomes in military aviation detachments.
DOI: 10.17632/yxjs9rrs8k.2
title: A model for evaluating the safety climate within a military aviation detachment during a long-term mission in hostile environments

description: This data base it is about a model designed to evaluate the safety climate within a military aviation detachment during a long-term mission in hostile environments. This model was tested during Romania's peacekeeping mission in Mali, Africa, in 2019-2020, where over 120 soldiers, including pilots and technicians, participated under UN auspices.

Objective
The goal of this research was to identify the key factors contributing to a high safety climate and to ensure the detachment operated safely in a dangerous environment. The mission's success, with zero accidents, highlights the importance of understanding and improving the safety climate.

Methodology
To evaluate the safety climate, a survey was distributed to 120 detachment members. The survey measured both organizational factors—such as task clarity and performance evaluation—and individual factors—like team spirit, trust in leaders, and motivation. We used a 10-point Likert scale for responses, and the results were analyzed using correlation and network analysis.

Key Findings
The results showed strong positive relationships between the safety climate and factors like team spirit, task clarity, and motivation. For example, task clarity had a correlation of r = 0.71, and team spirit showed r = 0.78. Interestingly, the impact of working conditions on the safety climate was insignificant.

Conclusion
This study demonstrates that clear tasks, team spirit, and motivation are crucial for maintaining a safe operational environment, especially in hostile situations. Future research could explore how safety climate evolves over time during long-term missions, but this model provides a valuable tool for improving safety outcomes in military aviation detachments.
DOI: 10.17632/yxjs9rrs8k.3
title: A model for evaluating the safety climate within a military aviation detachment during a long-term mission in hostile environments

description: This data base it is about a model designed to evaluate the safety climate within a military aviation detachment during a long-term mission in hostile environments. This model was tested during Romania's peacekeeping mission in Mali, Africa, in 2019-2020, where over 120 soldiers, including pilots and technicians, participated under UN auspices.

Objective
The goal of this research was to identify the key factors contributing to a high safety climate and to ensure the detachment operated safely in a dangerous environment. The mission's success, with zero accidents, highlights the importance of understanding and improving the safety climate.

Methodology
To evaluate the safety climate, a survey was distributed to 120 detachment members. The survey measured both organizational factors—such as task clarity and performance evaluation—and individual factors—like team spirit, trust in leaders, and motivation. We used a 10-point Likert scale for responses, and the results were analyzed using correlation and network analysis.

Key Findings
The results showed strong positive relationships between the safety climate and factors like team spirit, task clarity, and motivation. For example, task clarity had a correlation of r = 0.71, and team spirit showed r = 0.78. Interestingly, the impact of working conditions on the safety climate was insignificant.

Conclusion
This study demonstrates that clear tasks, team spirit, and motivation are crucial for maintaining a safe operational environment, especially in hostile situations. Future research could explore how safety climate evolves over time during long-term missions, but this model provides a valuable tool for improving safety outcomes in military aviation detachments.
DOI: 10.15468/dl.bnqjjp
title: Occurrence Download

description: A dataset containing 204 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Boophis picturatus Glaw, Vences, Andreone & Vallan, 2001"
 ]
}

 The dataset includes 204 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013371-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/c3cfw72d4n.1
title: A Python Code for Famispacing Estimation

description: Famispacing estimation, or family spacing estimation, is a quantitative method that estimates an individual's conformity or disconformity to an accepted minimum expectation (spacing interval scheme) within a population. It quantifies the similarity or dissimilarity between the observed pattern of biological children's ages and the expected pattern. Within the framework of Kabirian-based optinalysis, famispacing is conceptualized as the isoreflective pairing between the observed and expected patterns of biological children's ages.
The methodological processes in famispacing comprise two distinct phases:
1) Preprocessing phase: This involves applying preprocessing operations and transformations, such as parameter distillation, theoretical ordering, and shift transformations with absolutely no data centering. It also encompasses tasks like conceptual age pattern generation and optimizations within the established optinalytic construction. These optimizations include selecting an efficient pairing style, central normalization, and establishing an isoreflective pair between the two preprocessed data. The data are the observed and expected patterns of biological children's ages. 
2) Optinalytic model calculation phase: This phase is focused on computing estimates (such as the Kabirian coefficient of conformity (similarity), the probability of conformity (similarity), and the disconformity (dissimilarity)) based on Kabirian-based isomorphic optinalysis models.
DOI: 10.17632/c3cfw72d4n.2
title: A Python Code for Famispacing Estimation

description: Famispacing estimation, or family spacing estimation, is a quantitative method that estimates an individual's conformity or disconformity to an accepted minimum expectation (spacing interval scheme) within a population. It quantifies the similarity or dissimilarity between the observed pattern of biological children's ages and the expected pattern. Within the framework of Kabirian-based optinalysis, famispacing is conceptualized as the isoreflective pairing between the observed and expected patterns of biological children's ages.
The methodological processes in famispacing comprise two distinct phases:
1) Preprocessing phase: This involves applying preprocessing operations and transformations, such as parameter distillation, theoretical ordering, and shift transformations with absolutely no data centering. It also encompasses tasks like conceptual age pattern generation and optimizations within the established optinalytic construction. These optimizations include selecting an efficient pairing style, central normalization, and establishing an isoreflective pair between the two preprocessed data. The data are the observed and expected patterns of biological children's ages. 
2) Optinalytic model calculation phase: This phase is focused on computing estimates (such as the Kabirian coefficient of conformity (similarity), the probability of conformity (similarity), and the disconformity (dissimilarity)) based on Kabirian-based isomorphic optinalysis models.