cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5518/1523
title: Datasets associated with the paper: 'Assessing the energy and socio-macroeconomic impacts of the EV transition: A UK case study 2020-2050'

description: This file contains the following MARCO-UK model input and results data used in the following paper: Nieto, J., Brockway, Sakai, M., and Barrett J. “Assessing the energy and socio-macroeconomic impacts of the EV transition: A UK case study 2020-2050.” Applied Energy, 2024 available at https://doi.org/10.1016/j.apenergy.2024.123367
DOI: 10.5281/zenodo.18377769
title: LINF_320008600

description: DMIP22; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.18377770
title: LINF_320008600

description: DMIP22; Leishmania infantum (strain JPCM5)
DOI: 10.17617/3.duutoc
title: Experimental infections with a honeybee virus induce fitness costs in ant colonies

description: Acute Bee Paralysis Virus (ABPV) is a key driver of honey bee colony losses that has been increasingly reported in non-bee hosts. Ants have long been hypothesised to act as viral reservoirs, but most evidence comes from field surveys, and experimental tests are still scarce. Here, we combined survival and transmission assays, viral load quantification, and viral replication assays following experimental inoculations to test whether the clonal raider ant Ooceraea biroi can harbour and transmit ABPV within its colonies. ABPV-injected immatures and adults showed delayed development and elevated mortality, respectively. Viral replication assays suggested these fitness costs were caused by host responses (e.g., immunopathology) rather than viral replication. Viral particles persisted for days in inoculated ants and high viral loads were detected in untreated colony members after 1–3 days of cohabitation with ABPV-injected pupae or adults, indicating rapid viral spread. These results show that ants can acquire ABPV, incur fitness costs, and pass the virus within their colonies, suggesting that they may act as incidental viral reservoirs. By maintaining and disseminating honey bee viruses, even without supporting replication, ants may contribute to their environmental persistence and spillover across species.
DOI: 10.5281/zenodo.18173363
title: Data for the article "Precipitation reduction overrides edaphic controls on nitrous oxide emissions along a soil carbon, texture and pH gradient in a cereal field"

description: This dataset corresponds to the article 'Precipitation reduction overrides edaphic controls on nitrous oxide emissions along a soil carbon, texture and pH gradient in a cereal field' authored by Sigrid Trier Kjær and Peter Dörsch. The data comes from a field experiment, where we measured nitrous oxide (N2O) along a hillslope with a natural gradient in soil organic carbon, texture and pH. Along the hillslope we installed rainout shelters, that were buildt to exclude 49 % of the precipitation. The first letter in the Plot ID indicates plot position (from A to H) and the second indicates whether it was a control plot (K) or rainout shelter plot (R).  The dataset includes edaphic parameters measured along the gradient, N2O emissions measured in the snow-free period when the rainout shelters were installed, monthly extracable nitrate (NO3-) and ammonium (NH4+), barley yield and logger data (soil logggers at 5 cm depth) with volumetric water content and soil temperature (celsius).
DOI: 10.5281/zenodo.18173364
title: Data for the article "Precipitation reduction overrides edaphic controls on nitrous oxide emissions along a soil carbon, texture and pH gradient in a cereal field"

description: This dataset corresponds to the article 'Precipitation reduction overrides edaphic controls on nitrous oxide emissions along a soil carbon, texture and pH gradient in a cereal field' authored by Sigrid Trier Kjær and Peter Dörsch. The data comes from a field experiment, where we measured nitrous oxide (N2O) along a hillslope with a natural gradient in soil organic carbon, texture and pH. Along the hillslope we installed rainout shelters, that were buildt to exclude 49 % of the precipitation. The first letter in the Plot ID indicates plot position (from A to H) and the second indicates whether it was a control plot (K) or rainout shelter plot (R).  The dataset includes edaphic parameters measured along the gradient, N2O emissions measured in the snow-free period when the rainout shelters were installed, monthly extracable nitrate (NO3-) and ammonium (NH4+), barley yield and logger data (soil logggers at 5 cm depth) with volumetric water content and soil temperature (celsius).
DOI: 10.5519/qd.0ruej329
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (59082 records)
DOI: 10.5281/zenodo.18377491
title: Prevalence data for plots to accompany onchocerciasis morbidity modelling publication

description: This dataset supports plotting prevalence data points and associated 95% confidence intervals the following paper (Figures 3 - 9):

Onchocerciasis-associated skin and ocular disease and the impact of ivermectin treatment: a modelling study

Matthew A. Dixon1†*, Aditya Ramani1,2†, Martin Walker1,2, Jacob N. Stapley1, Michele E. Murdoch3, Ian E. Murdoch4, Gladys A. Ozoh5, Jonathan F. Mosser6,7, Maria-Gloria Basáñez1*

1MRC Centre for Global Infectious Disease Analysis and London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, 90 Wood Lane, London, W12 0BZ, UK

2Department of Pathobiology and Population Sciences, Royal Veterinary College, Hawkshead Lane, Hatfield, Hertfordshire, AL9 7TA, UK

3Department of Dermatology, West Herts Teaching Hospitals NHS Trust, Watford, Herts., UK

4International Centre for Eye Health, Institute of Ophthalmology, London, UK

5Dermatology Division, University of Nigeria Teaching Hospital, Ituku Ozala Enugu State, Nigeria

6Institute for Health Metrics and Evaluation, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle WA 98195, USA

7Department of Global Health, School of Public Health, and Seattle Children’s Hospital, University of Washington, Seattle WA, USA

†Contributed equally

Communications Medicine
DOI: 10.34894/ydxp6z
title: The Influence of State Anxiety on Fear Discrimination and Extinction in Females

description: Formal theories have linked pathological anxiety to a failure in fear response inhibition. Previously, we showed that aberrant response inhibition is not restricted to anxiety patients, but can also be observed in anxiety-prone adults. However, less is known about the influence of currently experienced levels of anxiety on inhibitory learning. The topic is highly important as state anxiety has a debilitating effect on cognition, emotion, and physiology and is linked to several anxiety disorders. In the present study, healthy female volunteers performed a fear conditioning task, after being informed that they will have to perform the Trier Social Stress Test task (n D 25; experimental group) or a control task (n D 25; control group) upon completion of the conditioning task. The results showed that higher levels of state anxiety corresponded with a reduced discrimination between a stimulus (CSC) typically followed by an aversive event and a stimulus (CS􀀀) that is never followed by an aversive event both during the acquisition and the extinction phase. No effect of state anxiety on the skin conductance response associated with CSC and CS􀀀 was found. Additionally, higher levels of state anxiety coincided with more negative valence ratings of the CSs. The results suggest that increased stress-induced state anxiety might lead to stimulus generalization during fear acquisition, thereby impairing associative learning.
description: License changed after request from the contributor
DOI: 10.34894/llnxjy
title: Turning negative memories around: Contingency versus devaluation techniques

description: Background and objectives: It is assumed that fear responses can be altered by changing the contingency between a conditioned stimulus (CS) and an unconditioned stimulus (US), or by devaluing the present mental representation of the US. The aim of the present study was to compare the efficacy of contingency- and devaluation-based intervention techniques on the diminishment in – and return of fear. We hypothesized that extinction (EXT, contingency-based) would outperform devaluation-based techniques regarding contingency measures, but that devaluation-based techniques would be most effective in reducing the mental representation of the US. Additionally, we expected that incorporations of the US during devaluation would result in less reinstatement of the US averseness.
Methods: Healthy participants received a fear conditioning paradigm followed by one of three interventions:
extinction (EXT, contingency-based), imagery rescripting (ImRs, devaluation-based) or eye movement desensitization and reprocessing (EMDR, devaluation-based). A reinstatement procedure and test followed the next day.
Results: EXT was indeed most successful in diminishing contingency-based US expectancies and skin conductance responses (SCRs), but all interventions were equally successful in reducing the averseness of the mental US representation. After reinstatement EXT showed lowest expectancies and SCRs; no differences were observed between the conditions concerning the mental US representation.
Limitations: A partial reinforcement schedule was used, resulting in a vast amount of contingency unaware participants. Additionally, a non-clinical sample was used, which may limit the generalizability to clinical populations.
Conclusion: EXT is most effective in reducing conditioned fear responses.
description: License changed after request from the contributor
DOI: 10.34894/exi8ij
title: Gildepenningen database

description: This study deals with the craft guilds in the 17th and 18th centuries in the Northern Netherlands and their internal organization. Dr. Teulings is drilling a unique material source for his study, namely the still existing corpus of guild tokens.
DOI: 10.5518/1522
title: Data associated with "Power stabilization of a terahertz-frequency quantum-cascade laser using a photonic-integrated modulator"

description: This dataset is associated with the manuscript "Power stabilization of a terahertz-frequency quantum-cascade laser using a photonic-integrated modulator". We demonstrate a technique to stabilize the emission from a ~3.5 THz quantum-cascade laser against long-term power drifts, using a recently developed Photonic Integrated Circuit (PIC) structure formed by coupling a racetrack resonator with a ridge waveguide. Successful locking over >600 s was achieved using a proportional–integral control loop. A technique for integrating the PIC with a precision micromachined rectangular metallic waveguide module has also been demonstrated.
DOI: 10.5281/zenodo.14062626
title: Housing gap database

description: Detailed data on the new mesure of housing affordability, i.e. "housing gap" described in the article Measuring the housing gap: a novel approach to assessing housing affordability.

The definition of categories used in the datafile:



 Group A is a subsample of households that (1) use dwellings that are owner-occupied or rented at market prices and (2) are neither overcrowded nor overburdened by housing costs.

Group B is a subsample of households that: (1) use dwellings that are owner-occupied or rented at market prices; (2) are either overcrowded or overburdened by housing costs or both; and (3) have incomes that fulfil either the housing credit affordability, the private rental affordability criterion, or both.

Group C is a subsample of households that: (1) use dwellings that are owner-occupied or rented at market prices; (2) are either overcrowded or overburdened by housing costs or both; and (3) have incomes that fulfil neither the housing credit affordability nor the private rental affordability criterion.

Group D is a subsample of households that: (1) use dwellings that are rented for free or at below-market prices; (2) are either overcrowded or overburdened by housing costs or both; and (3) have incomes that fulfil neither the housing credit affordability nor the private rental affordability criterion.

Group E is a subsample of households that: (1) use dwellings that are rented for free or at below-market prices; (2) are neither overcrowded nor overburdened by housing costs; and (3) have incomes that fulfil neither the housing credit affordability nor the private rental affordability criterion.

Group F is a subsample of households that: (1) use dwellings that are rented for free or at below-market prices; and (2) have incomes that fulfil either the housing credit affordability, the private rental affordability criterion, or both.

Group G consists of households that couldn’t be classified elsewhere due to missing data.


The housing gap was calculated as the sum of groups B, C, and D. Other data dimensions' are described in the label file. The definitions, if not denoted otherwise, are in-line with the ones used by Eurostat.

Please cite as: Czerniak, A., Kroszka, J. (2026). Measuring the housing gap: a novel approach to assessing housing affordability. Housing studies. doi.org/10.1080/02673037.2025.2608615

This project was financed by the National Science Centre (NCN) grant no. 2021/43/D/HS4/01649.
DOI: 10.5281/zenodo.14062625
title: Housing gap database

description: Detailed data on the new mesure of housing affordability, i.e. "housing gap" described in the article Measuring the housing gap: a novel approach to assessing housing affordability.

The definition of categories used in the datafile:



 Group A is a subsample of households that (1) use dwellings that are owner-occupied or rented at market prices and (2) are neither overcrowded nor overburdened by housing costs.

Group B is a subsample of households that: (1) use dwellings that are owner-occupied or rented at market prices; (2) are either overcrowded or overburdened by housing costs or both; and (3) have incomes that fulfil either the housing credit affordability, the private rental affordability criterion, or both.

Group C is a subsample of households that: (1) use dwellings that are owner-occupied or rented at market prices; (2) are either overcrowded or overburdened by housing costs or both; and (3) have incomes that fulfil neither the housing credit affordability nor the private rental affordability criterion.

Group D is a subsample of households that: (1) use dwellings that are rented for free or at below-market prices; (2) are either overcrowded or overburdened by housing costs or both; and (3) have incomes that fulfil neither the housing credit affordability nor the private rental affordability criterion.

Group E is a subsample of households that: (1) use dwellings that are rented for free or at below-market prices; (2) are neither overcrowded nor overburdened by housing costs; and (3) have incomes that fulfil neither the housing credit affordability nor the private rental affordability criterion.

Group F is a subsample of households that: (1) use dwellings that are rented for free or at below-market prices; and (2) have incomes that fulfil either the housing credit affordability, the private rental affordability criterion, or both.

Group G consists of households that couldn’t be classified elsewhere due to missing data.


The housing gap was calculated as the sum of groups B, C, and D. Other data dimensions' are described in the label file. The definitions, if not denoted otherwise, are in-line with the ones used by Eurostat.

Please cite as: Czerniak, A., Kroszka, J. (2026). Measuring the housing gap: a novel approach to assessing housing affordability. Housing studies. doi.org/10.1080/02673037.2025.2608615

This project was financed by the National Science Centre (NCN) grant no. 2021/43/D/HS4/01649.
DOI: 10.6084/m9.figshare.31150387
title: SHARM Brain Dataset

description: Details of this dataset are in the following publication:E. A. Rashed, M. al-Shatouri, I. Laakso, S. Kodera and A. Hirata, "SHARM: Segmented Head Anatomical Reference Models," Biomedical Signal Processing and Control, vol. 104, pp. 107481, 2025Full access is available from here:https://erashed.weebly.com/publications.html
DOI: 10.6084/m9.figshare.31150387.v1
title: SHARM Brain Dataset

description: Details of this dataset are in the following publication:E. A. Rashed, M. al-Shatouri, I. Laakso, S. Kodera and A. Hirata, "SHARM: Segmented Head Anatomical Reference Models," Biomedical Signal Processing and Control, vol. 104, pp. 107481, 2025Full access is available from here:https://erashed.weebly.com/publications.html
DOI: 10.5281/zenodo.15736089
title: Data and code release of Paying Attention to Other Animal Detections Improves Camera Trap Classification Models

description: Data and code release of: Paying Attention to Other Animal Detections Improves Camera Trap Classification Models

 

Preprint is available on bioRxiv : https://www.biorxiv.org/content/10.1101/2025.07.15.664849

 

Code is available on GitHub : https://github.com/gdussert/MCA_Classifier and also archived in MCA_Classifier-code.zip

 

This repository contains:

 

* models:

   * crop_classifier.pt

   * mca_classifier.pt

* data:

   * serengeti_train: training set of Snapshot Serengeti

   * serengeti_test: test set of Snapshot Serengeti

   * serengeti_toy: toy dataset to quickly test all the scripts, it contains only the pictures of Snapshot Serengeti camera trap location C12

   * safari2024: whole Snapshot Safari 2024 Expansion dataset

   * crop_images.zip: crop images of Snapshot Serengeti camera trap location C12

   * speciesnet.zip: SpeciesNet predictions for all images, as well as the script used to compute the accuracy reported in the manuscript. 

* code:

  * MCA_Classifier-code.zip

 

Please cite our paper for the models: 

 

@misc{dussert2025attention,    author = {Dussert, Gaspard and Dray, Stéphane and Chamaillé-Jammes, Simon and Miele, Vincent},    title = {Paying Attention to Other Animal Detections Improves Camera Trap Classification Models},    year = {2025},    doi = {10.1101/2025.07.15.664849},    publisher = {bioRxiv},    type = {preprint},}

 

Please cite this paper for the Serengeti dataset: 

 

@article{swanson2015snapshot,  title={Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna},  author={Swanson, Alexandra and Kosmala, Margaret and Lintott, Chris and Simpson, Robert and Smith, Arfon and Packer, Craig},  journal={Scientific data},  volume={2},  number={1},  pages={1--14},  year={2015},  publisher={Nature Publishing Group}}
DOI: 10.5281/zenodo.18377860
title: Data and code release of Paying Attention to Other Animal Detections Improves Camera Trap Classification Models

description: Data and code release of: Paying Attention to Other Animal Detections Improves Camera Trap Classification Models

 

Preprint is available on bioRxiv : https://www.biorxiv.org/content/10.1101/2025.07.15.664849

 

Code is available on GitHub : https://github.com/gdussert/MCA_Classifier and also archived in MCA_Classifier-code.zip

 

This repository contains:

 

* models:

   * crop_classifier.pt

   * mca_classifier.pt

* data:

   * serengeti_train: training set of Snapshot Serengeti

   * serengeti_test: test set of Snapshot Serengeti

   * serengeti_toy: toy dataset to quickly test all the scripts, it contains only the pictures of Snapshot Serengeti camera trap location C12

   * safari2024: whole Snapshot Safari 2024 Expansion dataset

   * crop_images.zip: crop images of Snapshot Serengeti camera trap location C12

   * speciesnet.zip: SpeciesNet predictions for all images, as well as the script used to compute the accuracy reported in the manuscript. 

* code:

  * MCA_Classifier-code.zip

 

Please cite our paper for the models: 

 

@misc{dussert2025attention,    author = {Dussert, Gaspard and Dray, Stéphane and Chamaillé-Jammes, Simon and Miele, Vincent},    title = {Paying Attention to Other Animal Detections Improves Camera Trap Classification Models},    year = {2025},    doi = {10.1101/2025.07.15.664849},    publisher = {bioRxiv},    type = {preprint},}

 

Please cite this paper for the Serengeti dataset: 

 

@article{swanson2015snapshot,  title={Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna},  author={Swanson, Alexandra and Kosmala, Margaret and Lintott, Chris and Simpson, Robert and Smith, Arfon and Packer, Craig},  journal={Scientific data},  volume={2},  number={1},  pages={1--14},  year={2015},  publisher={Nature Publishing Group}}
DOI: 10.5281/zenodo.18377559
title: Constructing a Dutch Historical Climate Vocabulary (v1.1) based on GLOBALISE

description: This version represents an intermediate, exploratory stage of development and is not intended for direct application without further manual annotation and refinement. Subsequent work (v1.2+) focuses on manual curation and formalization of the vocabulary.

v1.1 of the Dutch Historical Climate Vocabulary builds on v1 by identifying candidate terms for enrichment. A pre-trained GLOBALISE Word2Vec model (van Wissen, 2024; DOI 10.5281/zenodo.15038313) was evaluated for semantic coherence, and a FastText model was trained to capture additional candidate terms for inclusion. Candidate terms from the FastText Model were incorporated into v1.1 of the vocabulary based on similarity thresholds and other statistical metrics. This version is exploratory and requires further annotation and validation to be operational.

This version was initially shared internally on 2026-01-22 and is deposited here for provenance purposes.
DOI: 10.5281/zenodo.18377558
title: Constructing a Dutch Historical Climate Vocabulary (v1.1) based on GLOBALISE

description: This version represents an intermediate, exploratory stage of development and is not intended for direct application without further manual annotation and refinement. Subsequent work (v1.2+) focuses on manual curation and formalization of the vocabulary.

v1.1 of the Dutch Historical Climate Vocabulary builds on v1 by identifying candidate terms for enrichment. A pre-trained GLOBALISE Word2Vec model (van Wissen, 2024; DOI 10.5281/zenodo.15038313) was evaluated for semantic coherence, and a FastText model was trained to capture additional candidate terms for inclusion. Candidate terms from the FastText Model were incorporated into v1.1 of the vocabulary based on similarity thresholds and other statistical metrics. This version is exploratory and requires further annotation and validation to be operational.

This version was initially shared internally on 2026-01-22 and is deposited here for provenance purposes.
DOI: 10.5281/zenodo.18377879
title: LINF_320008700

description: DMIP24; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.18377880
title: LINF_320008700

description: DMIP24; Leishmania infantum (strain JPCM5)
DOI: 10.25574/28546
title: Chandra X-ray Observatory ObsId 28546

description: PHANGS-Chandra: A Legacy Survey for Detailing the First Billion Years of X-ray Evolution in Galaxies