cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48610/8b3af41
title: Whole-genome marker profiles for durum wheat intro-selection lines

description: A total of 11 durum intro-selection lines in the DBA Aurora background were genotyped using Diversity Arrays Technology platform. The folder contains SNP data as single row and two row formats as well as Silico DArT markers in a binary format. Markers were aligned to the durum wheat Svevo reference genome v2. The ‘metadat’ file describes all information relevant to genotyping of the intro-selection lines. The intro-selection lines were co-analysed with previous durum wheat genotyping orders submitted by The University of Queensland (e.g. a nest-association mapping population), which generated a full set of marker results regardless whether they were polymorphic in the intro-selection panel.
DOI: 10.48610/8b7ae22
title: AustLit - sample collection

description: Sample collection contributed to the Language Data Commons of Australia from AustLit provides full-text access to select samples of out of copyright poetry, fiction and criticism ranging from 1795 to the 1930s. The collection includes literature intended for popular audiences as well as literature intended for audiences concerned with literary quality or the establishment of a national canon. The bibliographical information associated with these records enables researchers to investigate the relationships between texts and particular publishers or to track the first publication of each text in newspapers, magazines or journals. The collection includes bibliographic and production information for works of fiction and poetry, writing for the theatre, biographical and travel writing, writing for film and television, criticism and reviews and the people and organisations who produce them.
DOI: 10.15468/dl.dfd92a
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((102.451 4.196163, 102.38055 4.190593, 102.311837 4.174021, 102.246557 4.146856, 102.186317 4.109767, 102.132604 4.063668, 102.086739 4.009698, 102.049852 3.949185, 102.022849 3.88362, 102.006394 3.81462, 102.000888 3.743884, 102.006465 3.673153, 102.022985 3.604169, 102.050039 3.53863, 102.086959 3.478149, 102.132835 3.424213, 102.186537 3.37815, 102.246744 3.341093, 102.311973 3.313952, 102.380621 3.297397, 102.451 3.291832, 102.521379 3.297397, 102.590027 3.313952, 102.655256 3.341093, 102.715463 3.37815, 102.769165 3.424213, 102.815041 3.478149, 102.851961 3.53863, 102.879015 3.604169, 102.895535 3.673153, 102.901112 3.743884, 102.895606 3.81462, 102.879151 3.88362, 102.852148 3.949185, 102.815261 4.009698, 102.769396 4.063668, 102.715683 4.109767, 102.655443 4.146856, 102.590163 4.174021, 102.52145 4.190593, 102.451 4.196163))"},{"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 19 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001340-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48610/8bdd0c8
title: EDA explorer data: application of neuroscience to improve student learning outcomes through interest driven learning

description: The datasets included here consists of survey data and physiological sensors data collected from the study. There are also observational data used to triangulate with the physiological sensors data. This study employed a mixed methods approach to incorporate a STEM interest survey together with physiological data in the form of electrodermal (EDA) data. The data collected were used to analyse and assess interest development within the participants, as well as other factors such as motivation, engagement and affect.
DOI: 10.48610/8c236f2
title: Pink Elephants Project

description: EEG data, analysis scripts and experimental code for Pink Elephants Project.  Files include:
DOI: 10.48610/8c2c84e
title: Dataset for the cryptomarket paper

description: This dataset contains data on 3 online cryptomarkets, -Agartha -Cryptonia -Tochka
DOI: 10.48610/8c41f39
title: Datasets supporting the adoption of multifunctional cover crops related to soil water and nitrogen in water-limited environments

description: Datasets for the understanding of the place of multifunctional cover crops as an alternative paradigm to conventional fallow in water-limited environments. The data contains crop resource quantity (biomass, N uptake, fixed shoot N, and water use), resource quality (C/N ratio, lignin, acid detergent fibre, and N concentration), and soil status (plant available water and mineral N) at field scale.  This data supports the framework of the continuous development of alternative innovative cropping systems that have the potential to increase and maintain crop yield while minimizing the adverse effects of fallowing in the context of sustainable intensification.
DOI: 10.5281/zenodo.17958530
title: Data supporting: Contrasting Northern and Southern East Asian Temperature Responses to Orbital Forcing during the Late Eocene

description: This dataset contains the data supporting the analyses and conclusions of the manuscript“Contrasting Northern and Southern East Asian Temperature Responses to Orbital Forcing during the Late Eocene”.

The dataset includes the following components:





Seasonal climate data for boreal summer (JJA) and boreal winter (DJF), stored as NetCDF files, which are used to analyze seasonal contrasts in climate responses.




Annual mean climate variables, derived from the corresponding seasonal and monthly outputs.




Top-of-atmosphere (TOA) insolation (SOLIN) data, provided as multi-year mean values, representing orbital-scale variations in incoming solar radiation.




Statistical significance test results, including outputs from significance testing applied to the analyzed climate variables.



All files are provided in NetCDF format unless otherwise stated.These data were generated and processed using numerical climate model simulations and standard post-processing procedures.Detailed variable definitions and processing steps are described in the associated manuscript.

The dataset is deposited in Zenodo to ensure transparency and reproducibility and will be made publicly available upon publication of the manuscript.
DOI: 10.5281/zenodo.17958529
title: Data supporting: Contrasting Northern and Southern East Asian Temperature Responses to Orbital Forcing during the Late Eocene

description: This dataset contains the data supporting the analyses and conclusions of the manuscript“Contrasting Northern and Southern East Asian Temperature Responses to Orbital Forcing during the Late Eocene”.

The dataset includes the following components:





Seasonal climate data for boreal summer (JJA) and boreal winter (DJF), stored as NetCDF files, which are used to analyze seasonal contrasts in climate responses.




Annual mean climate variables, derived from the corresponding seasonal and monthly outputs.




Top-of-atmosphere (TOA) insolation (SOLIN) data, provided as multi-year mean values, representing orbital-scale variations in incoming solar radiation.




Statistical significance test results, including outputs from significance testing applied to the analyzed climate variables.



All files are provided in NetCDF format unless otherwise stated.These data were generated and processed using numerical climate model simulations and standard post-processing procedures.Detailed variable definitions and processing steps are described in the associated manuscript.

The dataset is deposited in Zenodo to ensure transparency and reproducibility and will be made publicly available upon publication of the manuscript.
DOI: 10.6084/m9.figshare.30024679
title: <b>Protocol pipeline for Retrospective image analysis for long-term demography using Google Earth imagery</b>

description: Ecosystems are rapidly degrading. Widely used approaches to monitor ecosystems to manage them effectively are both expensive and time consuming. The recent proliferation of publicly available imagery from satellites, <b>Google Earth</b>, and citizen-science platforms holds the promise to revolutionising ecological monitoring and optimising their efficiency. However, the potential of these platforms to <b>detect species</b> and <b>track their population dynamics</b> remains under-explored. We introduce a fast, inexpensive method for retrospective image analysis combining current ground-truth data with historical RGB imagery from Google Earth to extract long-term demographic data. We apply this method to three case studies involving <b>two major Mediterranean invasive plant taxa</b> with contrasting growth forms.This dataset contains the <b>step-by-step protocol</b> to perform retrospective image analysis using Google Earth Imagery, including writen protocols, videotutorials and the data. A <b>ReadMe </b>is found in the folder explaining all folder's contents, whereas a <b>WatchMe </b>has been recorded to perform an analogous function in the Youtube playlist including all videotutorials: https://www.youtube.com/playlist?list=PL_LKE-yTi9kBXfw_qDdJCQ3Sxu2fjGvDDOur pipeline opens new avenues for <b>cost-effective, large-scale demographic monitoring by retrospectively harnessing open-access imagery</b>. While demonstrated here with invasive plants, we discuss the broad applicability of our approach across taxa and ecosystems. The use of retrospective image analysis for long-term demography with Google Earth imagery has the potential to expedite conservation decisions, support effective restoration, and enable robust ecological forecasting in the Anthropocene.The repository contains 4 folders (Data, Code, Protocols and Videos), acompaigned by a<b><i> ReadMe.txt</i></b> file with further details about the contents.NOTE (September 2025): In order to preserve the identity of the authors during peer review, currently videos can only be found on the youtube playlist: https://youtube.com/playlist?list=PL_LKE-yTi9kBXfw_qDdJCQ3Sxu2fjGvDD&si=Mb5jILoIDxcVsWFW, where the videos do not contain the initial presentation seconds where institutions are presented. After manuscript acceptance, full videos will be uploaded here and updated in Youtube.
DOI: 10.6084/m9.figshare.30024679.v2
title: <b>Protocol pipeline for Retrospective image analysis for long-term demography using Google Earth imagery</b>

description: Ecosystems are rapidly degrading. Widely used approaches to monitor ecosystems to manage them effectively are both expensive and time consuming. The recent proliferation of publicly available imagery from satellites, <b>Google Earth</b>, and citizen-science platforms holds the promise to revolutionising ecological monitoring and optimising their efficiency. However, the potential of these platforms to <b>detect species</b> and <b>track their population dynamics</b> remains under-explored. We introduce a fast, inexpensive method for retrospective image analysis combining current ground-truth data with historical RGB imagery from Google Earth to extract long-term demographic data. We apply this method to three case studies involving <b>two major Mediterranean invasive plant taxa</b> with contrasting growth forms.This dataset contains the <b>step-by-step protocol</b> to perform retrospective image analysis using Google Earth Imagery, including writen protocols, videotutorials and the data. A <b>ReadMe </b>is found in the folder explaining all folder's contents, whereas a <b>WatchMe </b>has been recorded to perform an analogous function in the Youtube playlist including all videotutorials: https://www.youtube.com/playlist?list=PL_LKE-yTi9kBXfw_qDdJCQ3Sxu2fjGvDDOur pipeline opens new avenues for <b>cost-effective, large-scale demographic monitoring by retrospectively harnessing open-access imagery</b>. While demonstrated here with invasive plants, we discuss the broad applicability of our approach across taxa and ecosystems. The use of retrospective image analysis for long-term demography with Google Earth imagery has the potential to expedite conservation decisions, support effective restoration, and enable robust ecological forecasting in the Anthropocene.The repository contains 4 folders (Data, Code, Protocols and Videos), acompaigned by a<b><i> ReadMe.txt</i></b> file with further details about the contents.NOTE (September 2025): In order to preserve the identity of the authors during peer review, currently videos can only be found on the youtube playlist: https://youtube.com/playlist?list=PL_LKE-yTi9kBXfw_qDdJCQ3Sxu2fjGvDD&si=Mb5jILoIDxcVsWFW, where the videos do not contain the initial presentation seconds where institutions are presented. After manuscript acceptance, full videos will be uploaded here and updated in Youtube.
DOI: 10.48610/8dba1c2
title: Supplementary files: Calcite U–Pb dating of altered ancient oceanic crust in the North Pamir, Central Asia

description: Supplementary files: Calcite U–Pb dating of altered ancient oceanic crust in the North Pamir, Central Asia
DOI: 10.6084/m9.figshare.30024679.v1
title: <b>Protocol pipeline for Retrospective image analysis for long-term demography using Google Earth imagery</b>

description: Ecosystems are rapidly degrading. Widely used approaches to monitor ecosystems to manage them effectively are both expensive and time consuming. The recent proliferation of publicly available imagery from satellites, <b>Google Earth</b>, and citizen-science platforms holds the promise to revolutionising ecological monitoring and optimising their efficiency. However, the potential of these platforms to <b>detect species</b> and <b>track their population dynamics</b> remains under-explored. We introduce a fast, inexpensive method for retrospective image analysis combining current ground-truth data with historical RGB imagery from Google Earth to extract long-term demographic data. We apply this method to three case studies involving <b>two major Mediterranean invasive plant taxa</b> with contrasting growth forms. This dataset contains the <b>step-by-step protocol</b> to perform retrospective image analysis using Google Earth Imagery, including writen protocols, videotutorials and the data. A <b>ReadMe </b>is found in the folder explaining all folder's contents, whereas a <b>WatchMe </b>has been recorded to perform an analogous function in the Youtube playlist including all videotutorials: https://www.youtube.com/playlist?list=PL_LKE-yTi9kBXfw_qDdJCQ3Sxu2fjGvDD Our pipeline opens new avenues for <b>cost-effective, large-scale demographic monitoring by retrospectively harnessing open-access imagery</b>. While demonstrated here with invasive plants, we discuss the broad applicability of our approach across taxa and ecosystems. The use of retrospective image analysis for long-term demography with Google Earth imagery has the potential to expedite conservation decisions, support effective restoration, and enable robust ecological forecasting in the Anthropocene.The repository contains 4 folders (Data, Code, Protocols and Videos), acompaigned by a<b><i> ReadMe.txt</i></b> file with further details about the contents.
DOI: 10.15468/dl.wwg3pr
title: Occurrence Download

description: A dataset containing 1377 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Equus conversidens"
 ]
}

 The dataset includes 1377 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001341-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48610/8e39846
title: The high corrosion-resistance of ultra-high purity Mg-Ge alloy and its discharge performance as anode for Mg-air battery

description: The dataset for the published paper
DOI: 10.21227/7t6a-9r59
title: "A Comparative Dataset of Commercial Portable Brain Monitoring Devices for Research-Oriented Acquisition (2025)"

description: "This dataset presents 98 wireless brain monitoring devices (EEG and fNIRS) currently available on the global market. A scientific characterization was performed by systematically searching the literature following PRISMA guidelines and registered in the OSF. The search was conducted across five databases (IEEE, Scopus, PubMed, Web of Science, and Embase) to identify the applications of the devices tracked in the market. The searches were conducted between January and August 2025. A total of 5,069 records were screened; 3,164 were unique entries. Among these, 2,100 studies explicitly reported the use of brain devices in clinical trials, dataset development, or technology evaluations, with an overall inclusion rate of 66.37% after screening. Research adoption was quantified by the number of DOI-indexed publications referencing each device. The compiled dataset provides an evidence-based framework to guide neuroscientists, engineers, and clinicians in selecting the appropriate device for their specific research needs."
DOI: 10.48610/8e4a7f3
title: Dataset for: UV exposure causes energy trade-offs leading to increased chytrid fungus susceptibility in green tree frog larvae.

description: This record contains data used in the analyses described in 'UV exposure causes energy trade-offs leading to increased chytrid fungus susceptibility in green tree frog larvae' i.e. tadpole metabolic rates, growth and development rates, and pathogen infection data.
DOI: 10.15468/dl.k589mx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((111.192 0.532185, 111.121734 0.526617, 111.053198 0.5100519999999999, 110.988081 0.482898, 110.927986 0.445823, 110.874392 0.39974, 110.828619 0.345784, 110.791794 0.285284, 110.764823 0.219729, 110.748371 0.150734, 110.742842 0.079998, 110.748372 0.009261, 110.764826 -0.059734, 110.791798 -0.125288, 110.828623 -0.185787, 110.874396 -0.239742, 110.92799 -0.285825, 110.988085 -0.322899, 111.053201 -0.350053, 111.121736 -0.366617, 111.192 -0.372185, 111.262264 -0.366617, 111.330799 -0.350053, 111.395915 -0.322899, 111.45601 -0.285825, 111.509604 -0.239742, 111.555377 -0.185787, 111.592202 -0.125288, 111.619174 -0.059734, 111.635628 0.009261, 111.641158 0.079998, 111.635629 0.150734, 111.619177 0.219729, 111.592206 0.285284, 111.555381 0.345784, 111.509608 0.39974, 111.456014 0.445823, 111.395919 0.482898, 111.330802 0.5100519999999999, 111.262266 0.526617, 111.192 0.532185))"},{"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 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001342-260120142942310/datasets/export for details.
DOI: 10.48610/8e4bf7a
title: Case studies of estimating climate projections of flooding hazard using NARClim 1.5 for Gwydir River, Macquarie River, Murrumbidgee River, Wagga Wagga Settlement and Illawarra Catchment.

description: A fully copy of the code used and outcomes obtained for Gwydir River, Macquarie River, Murrumbidgee River, Wagga Wagga Settlement and Illawarra Catchment case studies. This collection contains scripts used to develop, constrain, execute and post-process case studies (including files created during this case study required to run them) and outcomes. It does not, however, contain the NARCliM 1.5 netCDF files, totalling 1.4 TB, which can be obtained from NSW state government or the MIKE FLOOD and TUFLOW models, which can be obtained from NSW state government or relevant local governments. There is enough information available to recreate hydrologic and hydraulic models, execute and post-process them. The electronic collection contains the following main folders: BinAll, containing software developed by DHI for accessing MIKE FLOOD *.dfs? files from MATLAB; flooding, containing MATLAB intermediate files (data) and flood hazard projections (hyd); processing, containing MATLAB scripts and functions used in developing models, executing them, and post-process their outputs; and util_lisflood, containing FORTRAN programs.For bulk download the data files, read README file and windowns_powershell_download_all_ps1 file.
DOI: 10.48610/8e4d43f
title: Reproducible development environment in R containing CASTER, Gradient Forest Bootstrapping, and all supporting R packages and system libraries

description: Making computational analyses reproducible is challenging, as both hardware and software are constantly being updated. Newer versions of hardware or software may produce different results, making the research hard to validate or build on. While hardware changes are difficult to control for, software changes are controllable, but are often very difficult to configure in a normal academic setup. The investigator may not be able to change the operating system, and may not be able to install the software versions used in the original investigation. Administrators, concerned about proliferating versions of software on a system, may refuse to install specific versions. The nix package manager can be used by an unprivileged user to create a software stack that controls software and dependencies. This dataset provides code instructing the nix package manager to create an R environment that matches the environment used during the analyse of the project "Novel methods for developing large-scale, data-driven, biologically informed bioregionalisations" License: GNU General Public License V3
DOI: 10.48610/8f73a27
title: BeUpstanding_Early_Adopters_2017_2019

description: This dataset reports on data from those who signed up to the online BeUpstanding program prior to the start of the National Implementation Trial and were reported in the Early Adopters paper in BMC Public Health.
DOI: 10.48610/8f7b70e
title: Data for: Highly defended nudibranchs’ escape’ to visually distinct background habitats

description: Raw colour pattern data for each viewing distance.