cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14217505
title: JGR-Oceans article 2024JC021722: High-Latitude Cabbeling Observations by the Air-Sea Interaction Profiler

description: Data for JGR article 2024JC021722. Data is in matlab format with a set of structs where each struct is named: deployx_mmddHHMM, where x is the deployment number (4-6) and mmddHHMM is the time of the profile.

Struct contains:

temp: temperature in degC

sal: salinity in PSU

dens: density in kg/m3

pres: pressure in dbar
DOI: 10.5281/zenodo.14217506
title: JGR-Oceans article 2024JC021722: High-Latitude Cabbeling Observations by the Air-Sea Interaction Profiler

description: Data for JGR article 2024JC021722. Data is in matlab format with a set of structs where each struct is named: deployx_mmddHHMM, where x is the deployment number (4-6) and mmddHHMM is the time of the profile.

Struct contains:

temp: temperature in degC

sal: salinity in PSU

dens: density in kg/m3

pres: pressure in dbar
DOI: 10.15468/dl.zke4cq
title: Occurrence Download

description: A dataset containing 5991 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Alectoris graeca (Meisner, 1804), Aquila chrysaetos (Linnaeus, 1758), Anthus campestris (Linnaeus, 1758), Bubo bubo (Linnaeus, 1758), Burhinus oedicnemus (Linnaeus, 1758), Calandrella brachydactyla (Leisler, 1814), Caprimulgus europaeus Linnaeus, 1758, Circaetus gallicus (Gmelin, 1788), Circus cyaneus (Linnaeus, 1766), Falco columbarius Linnaeus, 1758, Falco peregrinus Tunstall, 1771, Hippolais olivetorum (Strickland, 1837), Lanius minor Gmelin, 1788, Melanocorypha calandra (Linnaeus, 1766))",
 "Country is Croatia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5991 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011129-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m8t8qu
title: Occurrence Download

description: A dataset containing 4342171 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Material sample)",
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2000-2024"
 ]
}

 The dataset includes 4342171 records from 167 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011112-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jgta29
title: Occurrence Download

description: A dataset containing 3163 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Phoebe formosana (Hayata) Hayata, Decaisnea insignis (Griff.) Hook.fil. & Thomson, Ginkgo biloba L.)",
 "Geometry POLYGON((72.2 56, 137.5 56, 137.5 0, 72.2 0, 72.2 56))",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null",
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 }
 ]
}

 The dataset includes 3163 records from 81 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011130-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7zhh3h
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Seriola carpenteri Mather, 1971",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011132-241126133413365/datasets/export for details.
DOI: 10.5281/zenodo.7749599
title: Data from: Trends in butterfly populations in UK gardens – new evidence from citizen  science monitoring

description: This data package describes the annual abundance indices and trend estimates for 22 butterfly species in UK gardens for the period 2007-2020.

These data form the basis of the results presented in: Plummer, K.E., Dadam, D., Brereton, T., Dennis, E.B., Massimino, D., Risely, K. et al. (2023) Trends in butterfly populations in UK gardens—New evidence from citizen science monitoring. Insect Conservation and Diversity, 1– 13. Available from: https://doi.org/10.1111/icad.12645

Please refer to the paper for an explanation of the underlying BTO Garden BirdWatch (GBW) data and modelling protocols used to produce the datasets included here.

We would also greatly appreciate if you could fill out this very short form to tell us how you intend to use these data. Thanks in advance!
DOI: 10.5281/zenodo.7749600
title: Data from: Trends in butterfly populations in UK gardens – new evidence from citizen  science monitoring

description: This data package describes the annual abundance indices and trend estimates for 22 butterfly species in UK gardens for the period 2007-2020.

These data form the basis of the results presented in: Plummer, K.E., Dadam, D., Brereton, T., Dennis, E.B., Massimino, D., Risely, K. et al. (2023) Trends in butterfly populations in UK gardens—New evidence from citizen science monitoring. Insect Conservation and Diversity, 1– 13. Available from: https://doi.org/10.1111/icad.12645

Please refer to the paper for an explanation of the underlying BTO Garden BirdWatch (GBW) data and modelling protocols used to produce the datasets included here.

We would also greatly appreciate if you could fill out this very short form to tell us how you intend to use these data. Thanks in advance!
DOI: 10.15468/dl.k6vs59
title: Occurrence Download

description: A dataset containing 139 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Crambe cordifolia Steven)"
 ]
}

 The dataset includes 139 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011131-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48579/pro/i9iprd/drqw2h
title: tags_data.tab

description: :unav
DOI: 10.48579/pro/i9iprd
title: Simulated trajectories of tagged dolphin carcasses in the Atlantic waters of the Iberian Peninsula, Northeast Atlantic, between 2007 and 2012

description: In the context of small cetacean bycatch, dead dolphins caught in fishing vessel off the Galician coast were tagged and released to allow identification if they stranded between 2007 and 2012. The trajectories of these dolphins were simulated using the MOTHY (Modèle Océanique de Transport d’HYdrocarbures) model , with relevant environmental variables tested to optimise the model parameters for this area. The aim was to accurately estimate the carcass trajectories of the dolphins. The dataset includes information on the tagged dolphins and the coordinates of the simulated trajectories with the best parameter combinations identified for the model.
DOI: 10.48579/pro/i9iprd/btt8go
title: trajectory_simulated.tab

description: :unav
DOI: 10.48579/pro/i9iprd/nnlygr
title: readme.txt

description: :unav
DOI: 10.17632/jmb45j9p2v
title: Fostering sustainable finance investments: Understanding investor motivations for impact funds from the perspective of the push-pull-mooring framework

description: Private investors’ investments are essential for achieving sustainability goals within sustainability transformation. Drawing on migration theory, this study applies the push-pull-mooring framework and structural equation modeling to contribute to a better understanding of motivations guiding potential investors toward sustainable financial products that pursue sustainability-related objectives alongside return on investment (impact funds). 
Our findings reveal pull (i.e., lifestyle compatibility and alternative attractiveness) and mooring (i.e., consciousness for sustainable consumption) factors as influential drivers of the intention to invest in impact funds. Conversely, push factors (i.e., perceived environmental threats) may not always trigger transformative actions. Therefore, different investment profiles, emerging from intergenerational differences and prior experiences, necessitate tailored strategies for different investor target groups as well as further communication and education on sustainability issues by asset managers to encourage a broader impact fund acceptance.
Our study emphasizes the growing importance of integrating sustainability strategies into asset management, driven by evolving regulatory frameworks and shifting investor preferences. Future research in sustainable finance should refine the push-pull-mooring framework, address endogeneity issues through longitudinal behavioral data, and incorporate additional control variables for a more comprehensive understanding. Overall, this study provides a foundation for future research and managerial strategies to foster sustainable finance investing.
DOI: 10.17632/jmb45j9p2v.1
title: Fostering sustainable finance investments: Understanding investor motivations for impact funds from the perspective of the push-pull-mooring framework

description: Private investors’ investments are essential for achieving sustainability goals within sustainability transformation. Drawing on migration theory, this study applies the push-pull-mooring framework and structural equation modeling to contribute to a better understanding of motivations guiding potential investors toward sustainable financial products that pursue sustainability-related objectives alongside return on investment (impact funds). 
Our findings reveal pull (i.e., lifestyle compatibility and alternative attractiveness) and mooring (i.e., consciousness for sustainable consumption) factors as influential drivers of the intention to invest in impact funds. Conversely, push factors (i.e., perceived environmental threats) may not always trigger transformative actions. Therefore, different investment profiles, emerging from intergenerational differences and prior experiences, necessitate tailored strategies for different investor target groups as well as further communication and education on sustainability issues by asset managers to encourage a broader impact fund acceptance.
Our study emphasizes the growing importance of integrating sustainability strategies into asset management, driven by evolving regulatory frameworks and shifting investor preferences. Future research in sustainable finance should refine the push-pull-mooring framework, address endogeneity issues through longitudinal behavioral data, and incorporate additional control variables for a more comprehensive understanding. Overall, this study provides a foundation for future research and managerial strategies to foster sustainable finance investing.
DOI: 10.5281/zenodo.56453
title: Data from: Investigating the impact of street lighting changes on garden moth communities

description: This data package accompanies:Plummer et al (2016). Investigating the impact of street lighting changes on garden moth communities. Journal of Urban Ecology. DOI 10.1093/jue/juw004

It contains a copy of the two derived datasets used to complete the analyses presented in the paper.  File details:

1.  ReadMe.txt:  Includes a description of the variables included in each dataset.

2.  Plummer_JUrbanEcol_2016_BACI_dataset.csv:  A .csv file including two years (2011 & 2013) of macro-moth community data (abundance, richness, diversity) for 18 garden locations in Birmingham, UK. Data are summarised per garden and year, together with data for proximity to street lamp replacement.

3.  Plummer_JUrbanEcol_2016_light_composition_dataset.csv:  A .csv file including one year (2013) of garden moth community data (abundance, richness, diversity; including macro- and micro-moths) for 18 garden locations in Birmingham, UK. Data are summarised per trapping event, together with associated street lighting and habitat characteristics for each garden.        

We would also greatly appreciate if you could fill out this very short form to tell us how you intend to use these data. Thanks in advance!
DOI: 10.15468/dl.9jr77d
title: Occurrence Download

description: A dataset containing 531909 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Colletidae"
 ]
}

 The dataset includes 531909 records from 859 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011114-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a9gyqa
title: Occurrence Download

description: A dataset containing 206 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lophius litulon (Jordan, 1902)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 206 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011134-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/s95bxkw9s9.1
title: A prolonged crustal storage for mantle xenolith-bearing alkali basaltic lava

description: This is a dataset on mantle xenocrysts, including whole-rock data of the host magma, major and trace element data of olivine xenocrysts, Fe-Mg isotope data, as well as data on olivine phenocrysts and the matrix. The data for olivine xenocrysts are all compositional profiles.
Supplementary Table 1 - Major and trace element of the bulk basanite
Supplementary Table 2 -Microprobe analysis data with olivine (wt.%)
Supplementary Table 3 - LA-MC-ICP-MS analysis data with xenocryst olivine for trace elements (ppm)
Supplementary Table 4 - In-situ Fe-Mg isotope compositions of olivine xenocryst and modeling results
DOI: 10.5281/zenodo.7752011
title: BTO Garden BirdWatch: Weekly butterfly abundance data for modelling trends in UK gardens

description: Dataset used to estimate annual abundance indices and trends for UK butterflies in gardens, covering the period 2007 to 2020.

Data have been collected as part of the British Trust for Ornithology (BTO) Garden BirdWatch (GBW) survey. GBW is a structured, citizen science monitoring programme whereby volunteers record weekly abundances of various bird, invertebrate, mammal, reptile  and amphibian species in (predominantly suburban and rural) gardens. See www.bto.org/gbw for further information about the survey. 

This dataset has been pre-filtered to meet criteria for inclusion in the modelling of butterfly species trends, as described by Plummer et al 2023. Please refer to the 'readme' file for further details.

We would also greatly appreciate if you could fill out this very short form to tell us how you intend to use these data. Thanks in advance!
DOI: 10.5281/zenodo.7752012
title: BTO Garden BirdWatch: Weekly butterfly abundance data for modelling trends in UK gardens

description: Dataset used to estimate annual abundance indices and trends for UK butterflies in gardens, covering the period 2007 to 2020.

Data have been collected as part of the British Trust for Ornithology (BTO) Garden BirdWatch (GBW) survey. GBW is a structured, citizen science monitoring programme whereby volunteers record weekly abundances of various bird, invertebrate, mammal, reptile  and amphibian species in (predominantly suburban and rural) gardens. See www.bto.org/gbw for further information about the survey. 

This dataset has been pre-filtered to meet criteria for inclusion in the modelling of butterfly species trends, as described by Plummer et al 2023. Please refer to the 'readme' file for further details.

We would also greatly appreciate if you could fill out this very short form to tell us how you intend to use these data. Thanks in advance!
DOI: 10.15468/dl.ycrdk6
title: Occurrence Download

description: A dataset containing 42407 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Crambe maritima L.)"
 ]
}

 The dataset includes 42407 records from 217 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011135-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/sx4m6tdwb7.1
title: Hydrological Dataset for Ganges Basin

description: This dataset contains high-resolution hydrological and sediment transport data collected from the Ganges River Basin between 2000 and 2022. Key variables include water level, discharge, sediment concentration, precipitation, and temperature. The data is sourced from multiple monitoring stations, including Bokulpur Ghat, Hardinge Bridge, and Rajbari, and has undergone rigorous validation by the Bangladesh Water Development Board (BWDB), ensuring its reliability. The dataset is well-suited for hydrodynamic modelling, Bayesian analysis, and sustainable river basin management studies.
DOI: 10.17632/g5fcztszg4.1
title: Environmental_Hydrological_Data

description: This dataset, titled “Environmental_Hydrological_Data”, provides comprehensive hydrological and environmental observations collected over 100,000 hourly intervals. It is designed for use in hydrodynamic modeling, water resource management, and environmental analysis. The dataset includes variables essential for studying river dynamics, sediment transport, and climate impacts on aquatic systems.

Key Features:

 1. Temporal Coverage: The dataset spans from 2000 to 2022, providing 100,000 hourly records.
 2. Geographical Context: Data collected from multiple stations, representing diverse hydrological conditions.
 3. Variables:
 • Hydrological Data: Water levels (m), discharge rates (m³/s), and flow velocity (m/s).
 • Sediment Analysis: Sediment concentration (mg/L), dissolved oxygen (mg/L), and turbidity (NTU).
 • Environmental Data: Precipitation (mm), temperature (°C), pH levels, and nutrient concentrations (Total Nitrogen and Total Phosphorus).
 4. Use Cases:
 • Hydrodynamic and sediment transport modeling.
 • Climate impact assessments on river systems.
 • Sustainable water resource management.
 • Analysis of environmental quality and ecosystem health.

Applications:

This dataset is ideal for researchers, engineers, and policymakers involved in:
 • Riverine ecosystem modeling.
 • Flood risk assessment.
 • Water quality and pollution control.
 • Decision-making for sustainable environmental and hydrological management.

Format:

The dataset is stored in Excel format (.xlsx) for easy access and compatibility with analytical tools like Python, R, MATLAB, and Excel.
DOI: 10.17632/sd4ryht7sm.1
title: Canal_Structures

description: The dataset “Canal_Structures.xlsx” provides detailed structural and operational characteristics of canal segments within an irrigation network. It is designed for researchers, engineers, and policymakers involved in water resource management, irrigation planning, and hydrodynamic modelling.


Key Features

 1. Dataset Scope:
 • Includes data on canal lengths, cross-sectional dimensions, irrigation areas, and operational details for each canal segment.
 • Covers primary and secondary canals, along with associated turnouts and off-take regulators.
 2. Variables Included:
 • Structural Information:
 • Canal ID, Length (km), Bed Width (m), Top Width (m), Bed Level (mPWD), and Side Slope.
 • Irrigation Details:
 • Command Area (ha), Irrigable Area (ha), and Number of Turnouts.
 • Operational Features:
 • Offtake regulator dimensions, chainage (km), invert levels, and design discharge (m³/s).
 • Water Levels:
 • Full Supply Level (mPWD), Crest Level (mPWD), and Crest Width (m).
 3. Geographical Context:
 • Data pertains to canal networks typically used for irrigation and flood control systems.



This dataset is valuable for:
 • Irrigation Network Analysis:
 • Evaluation of canal capacities and structural efficiency.
 • Water Resource Management:
 • Optimization of water delivery and irrigation coverage.
 • Hydrodynamic Modeling:
 • Integration into simulation models for predicting water flow and sediment transport.
 • Engineering Design:
 • Assessment of existing canal structures for maintenance and upgrading.
DOI: 10.15468/dl.zbsa8x
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lophiomus litulon Jordan, 1902",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011136-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/pj2ph9jbj5.1
title: Raw data: Introducing Rigidity into the GFP chromophore via a Boron Bridge: new insights and application in two-photon imaging.

description: The dataset contains the raw data for the microscopy measurements for the scientific paper in the title. 
The folders are named according to the figures in the manuscript and supplementary information.
DOI: 10.17632/pj2ph9jbj5.2
title: Raw data: Introducing Rigidity into the GFP chromophore via a Boron Bridge: new insights and application in two-photon imaging.

description: The dataset contains the raw data for the microscopy measurements for the scientific paper in the title. 
The folders are named according to the figures in the manuscript and supplementary information.