cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25824/redu/95tspq
title: Three-dimensional structural models of the wild-type and mutated proteins from variants identified in patients diagnosed with Ménière's Disease in Brazil

description: The dataset is composed of three-dimensional structural models of wild-type and mutated proteins that correspond to genetic variants identified in Brazilian patients diagnosed with Ménière's Disease. This work was conducted within the thesis "Identification of the molecular basis of Meniere's Disease" as a collaborative effort between the Laboratory of Human Molecular Genetics at CBMEG/UNICAMP and the Meniere's Disease Neuroscience Laboratory at the Kolling Institute, University of Sydney. Subsequent to the identification of genetic variants, the subsequent analytical step consisted of modelling the three-dimensional structure of the selected proteins using bioinformatic software to elucidate the potential structural impact of these variants on the translated proteins. As the genetic material was extracted from blood samples, the modelling is not tissue-specific and was performed using the canonical protein sequences obtained from the UniProt database. The selection between different structural predictions for each model was based on the higher average scores obtained through established quality control validations.
DOI: 10.13140/rg.2.2.33482.22723
title: The Örenäs Research Group: the 'Causes of Delayed Cancer Diagnosis' study
DOI: 10.6084/m9.figshare.31150078
title: Translating predictive distributions into informative priors

description: “When complex Bayesian models exhibit implausible behaviour, one solution is to assemble available information into an informative prior. Challenges arise as prior information is often only available for the observable quantity, or some model-derived marginal quantity, rather than directly pertaining to the (usually latent) parameters in our model. We propose a method for translating available prior information, in the form of an elicited distribution for the observable or model-derived marginal quantity, into an informative joint prior. Our approach proceeds given a parametric class of prior distributions with as yet undetermined hyperparameters, and minimises the difference between the supplied elicited distribution and corresponding prior predictive distribution. We employ a global, multi-stage Bayesian optimisation procedure to locate optimal values for the hyperparameters. Three examples illustrate our approach: a cure-fraction survival model, where censoring implies that the observable quantity is <i>a priori</i> a mixed discrete/continuous quantity; a setting in which prior information pertains to R2 – a model-derived quantity; and a nonlinear regression model.”
DOI: 10.6084/m9.figshare.31150078.v1
title: Translating predictive distributions into informative priors

description: “When complex Bayesian models exhibit implausible behaviour, one solution is to assemble available information into an informative prior. Challenges arise as prior information is often only available for the observable quantity, or some model-derived marginal quantity, rather than directly pertaining to the (usually latent) parameters in our model. We propose a method for translating available prior information, in the form of an elicited distribution for the observable or model-derived marginal quantity, into an informative joint prior. Our approach proceeds given a parametric class of prior distributions with as yet undetermined hyperparameters, and minimises the difference between the supplied elicited distribution and corresponding prior predictive distribution. We employ a global, multi-stage Bayesian optimisation procedure to locate optimal values for the hyperparameters. Three examples illustrate our approach: a cure-fraction survival model, where censoring implies that the observable quantity is <i>a priori</i> a mixed discrete/continuous quantity; a setting in which prior information pertains to R2 – a model-derived quantity; and a nonlinear regression model.”
DOI: 10.5281/zenodo.18326661
title: CosMx Spatial Transcriptomics of Colorectal Cancer Liver Metastases - Raw dataset TMA4

description: Data files for a CosMx ST run with a 1000-plex probeset. This dataset was used for the Geyer et al manuscript as part of a collaboration.

The slide contains a TMA (tissue microarray) with 2 mm cores of FFPE material of patients with colorectal cancer liver metastasis, taken at the tumour-liver interface. An annotated image (png) of the TMA stained by HE is included

Besides the raw files (starting with ErasmusTMA), an R data file (RDS) is included, containing a Spatial Experiment object as used in the manuscript. An explainer file (TMA4classic_spe_colnames.csv) is added as well.

Please contact Dr. Tauriello for more information about access or collaboration.
DOI: 10.5281/zenodo.18326662
title: CosMx Spatial Transcriptomics of Colorectal Cancer Liver Metastases - Raw dataset TMA4

description: Data files for a CosMx ST run with a 1000-plex probeset. This dataset was used for the Geyer et al manuscript as part of a collaboration.

The slide contains a TMA (tissue microarray) with 2 mm cores of FFPE material of patients with colorectal cancer liver metastasis, taken at the tumour-liver interface. An annotated image (png) of the TMA stained by HE is included

Besides the raw files (starting with ErasmusTMA), an R data file (RDS) is included, containing a Spatial Experiment object as used in the manuscript. An explainer file (TMA4classic_spe_colnames.csv) is added as well.

Please contact Dr. Tauriello for more information about access or collaboration.
DOI: 10.60520/ieda/114311
title: Nutrient data from shallow ice samples on Qaanaaq Ice Gap and nearby Greenland ice sheet

description: Nutrient data for surface ice samples from the Qaanaaq ice cap(approximate sampling location: N 77° 30.092', W 69° 09.691') and a nearby GrIS site (approximate sampling location: N 77° 48.259', W 68° 21.083') . Samples collected consisted of surface ice scrapings and or shallow ice cores, of which three depths were collected for analysis. Ice scrapings were collected using a field conditioned ice axe to scrape the top layer of weathered ice in a ~2 x 2 m square. From the ice cores, three primary 5 cm pucks were analysed: 1) The top 5 cm; 2) The underlying 5 cm (5 – 10 cm depth); 3) The last 5 cm at the base of the ~80-90 cm depth core. Nutrients analysed included: Soluble Reactive Phosphorous (SRP), Nitrite (NO2-) and Total Oxidised Nitrogen (TON; NO2-+ NO3-), ammonium (NH4+) and total dissolved phosphorous (TDP) and total dissolved nitrogen (TDN).
DOI: 10.5281/zenodo.5113706
title: Outer Port of Punta Langosteira (Spain) ship movement dataset: 2015 - 2020

description: This dataset contains the movements of 45 ships recorded in the Outer Port of Punta Langosteira (A Coruña, Spain) from 2015 until 2020.
description: The research that allowed creating this dataset was funded by the Spanish Ministry of Economy, Industry, and Competitiveness (MEIC), R&D National Plan, within the project BIA2017-86738-R, the FPI predoctoral grant from the Spanish Ministry of Science, Innovation, and Universities (PRE2018-083777) and the Spanish Ministry of Science and Innovation, Retos Call, within the project PID2020-112794RB-I00.
DOI: 10.5281/zenodo.5113707
title: Outer Port of Punta Langosteira (Spain) ship movement dataset: 2015 - 2020

description: This dataset contains the movements of 45 ships recorded in the Outer Port of Punta Langosteira (A Coruña, Spain) from 2015 until 2020.
description: The research that allowed creating this dataset was funded by the Spanish Ministry of Economy, Industry, and Competitiveness (MEIC), R&D National Plan, within the project BIA2017-86738-R, the FPI predoctoral grant from the Spanish Ministry of Science, Innovation, and Universities (PRE2018-083777) and the Spanish Ministry of Science and Innovation, Retos Call, within the project PID2020-112794RB-I00.
DOI: 10.5281/zenodo.17951744
title: De novo transcriptome assembly of Alzatea verticillata

description: This Zenodo deposit contains the key data products generated during the de novo transcriptome analyses of Alzatea verticillata. We provide the final filtered transcriptome assembly together with the main derived outputs used for technical validation and downstream analyses (e.g., expression matrices, functional annotation).

All files were produced using the bioinformatic pipeline archived on Zenodo via the linked GitHub repository (see “Related identifiers”).
DOI: 10.5281/zenodo.17951745
title: De novo transcriptome assembly of Alzatea verticillata

description: This Zenodo deposit contains the key data products generated during the de novo transcriptome analyses of Alzatea verticillata. We provide the final filtered transcriptome assembly together with the main derived outputs used for technical validation and downstream analyses (e.g., expression matrices, functional annotation).

All files were produced using the bioinformatic pipeline archived on Zenodo via the linked GitHub repository (see “Related identifiers”).
DOI: 10.5281/zenodo.18376816
title: ACEM 10066 - supplementary files
DOI: 10.5281/zenodo.18376817
title: ACEM 10066 - supplementary files
DOI: 10.5281/zenodo.17961950
title: De novo transcriptome assembly of Cedrela montana

description: This Zenodo deposit contains the key data products generated during the de novo transcriptome analyses of Cedrela montana. We provide the final filtered transcriptome assembly together with the main derived outputs used for technical validation and downstream analyses (e.g., expression matrices, functional annotation).

All files were produced using the bioinformatic pipeline archived on Zenodo via the linked GitHub repository (see “Related identifiers”).
DOI: 10.5281/zenodo.17961951
title: De novo transcriptome assembly of Cedrela montana

description: This Zenodo deposit contains the key data products generated during the de novo transcriptome analyses of Cedrela montana. We provide the final filtered transcriptome assembly together with the main derived outputs used for technical validation and downstream analyses (e.g., expression matrices, functional annotation).

All files were produced using the bioinformatic pipeline archived on Zenodo via the linked GitHub repository (see “Related identifiers”).
DOI: 10.5281/zenodo.18376743
title: Spatial hearing beyond primary interaural cues in humans and deep neural networks

description: Data for the preprint "Robust spatial hearing beyond primary interaural cues in humans over deep neural networks" are deposited here and will be released upon publication.Preprint link:https://www.biorxiv.org/content/10.1101/2025.08.05.668776v1
DOI: 10.5281/zenodo.18376742
title: Spatial hearing beyond primary interaural cues in humans and deep neural networks

description: Data for the preprint "Robust spatial hearing beyond primary interaural cues in humans over deep neural networks" are deposited here and will be released upon publication.Preprint link:https://www.biorxiv.org/content/10.1101/2025.08.05.668776v1
DOI: 10.5281/zenodo.17962375
title: De novo transcriptome assembly of Graffenrieda emarginata

description: This Zenodo deposit contains the key data products generated during the de novo transcriptome analyses of Graffenrieda emarginata. We provide the final filtered transcriptome assembly together with the main derived outputs used for technical validation and downstream analyses (e.g., expression matrices, functional annotation).

All files were produced using the bioinformatic pipeline archived on Zenodo via the linked GitHub repository (see “Related identifiers”).
DOI: 10.5281/zenodo.17962374
title: De novo transcriptome assembly of Graffenrieda emarginata

description: This Zenodo deposit contains the key data products generated during the de novo transcriptome analyses of Graffenrieda emarginata. We provide the final filtered transcriptome assembly together with the main derived outputs used for technical validation and downstream analyses (e.g., expression matrices, functional annotation).

All files were produced using the bioinformatic pipeline archived on Zenodo via the linked GitHub repository (see “Related identifiers”).
DOI: 10.11588/duerer.online.69776fa3ed64c
title: Die Grabtragung, https://d-nb.info/gnd/1196833958
DOI: 10.7910/dvn/mhk18r
title: Recruitment Threat Audit Index (RTAI): An Open-Source Intelligence Risk Scoring Model and Evidence Chain Audit Mechanism for Higher Education Talent Recruitment

description: This dataset is constructed based on the paper ‘Recruitment Threat Audit Index (RTAI): An Open-Source Intelligence Risk Scoring Model and Evidence Chain Audit Mechanism for Higher Education Talent Recruitment’. It aims to translate potential risks in university talent recruitment and academic qualification verification processes into a structured, computable scoring system in a reproducible and auditable manner. The dataset provides discrete scoring rules and weighting parameters for eight core indicators (I1–I8), alongside tiered thresholds (Green/Yellow/Orange/Red) and corresponding governance action recommendations. It is complemented by a ‘Claim–Evidence’ traceability table and links to external OSINT sources, ensuring end-to-end verifiability from conclusions to supporting evidence. This dataset facilitates risk identification, evidence chain auditing, and decision support for academic research and governance practices, while being scalable for broader institutional assessments and automated audit scenarios.
DOI: 10.34740/kaggle/dsv/14626429
title: llms.txt
DOI: 10.5281/zenodo.8369649
title: BIOVALENT: Biodiversity Footprint Database

description: BIOVALENT: Biodiversity Footprint Database interface (with additional food-related biodiversity impact factors) can be found at: https://oscsolutions.cc.jyu.fi/wisdom/biovalent/

The BIOVALENT data files in Zenodo contain global consumption-based, monetary, biodiversity impact factors for 44 countries and five rest of the world regions. The dataset has been compiled by combining information from EXIOBASE and LC-IMPACT databases. In addition, the EXIOBASE database has been analyzed with the pymrio analysis tool to determine the geographical location of the consumption-based biodiversity impacts. The mid-point impact factors from EXIOBASE are based on 2019 data, but the regional analysis with pymrio is based on 2011 data. EXIOBASE version 3.8.2 was used and LC-IMPACT version 1.3. The data is currently non peer-reviewed and under submission. The preprint of the manuscript can be found from: https://doi.org/10.48550/arXiv.2309.14186

Please also cite the preprint when using the database:

El Geneidy, S., Peura, M., Aumanen, V.-M., Baumeister, S., Helimo, U., Vainio, V. & Kotiaho, J. S. (2025). Value-transforming financial, carbon and biodiversity footprint accounting. arXiv:2309.14186 [econ.GN]. https://doi.org/10.48550/arXiv.2309.14186

About the units

The unit used in the database is the biodiversity equivalent (BDe). The biodversity equivalent, as we call it, is more commonly known as the global potentially disappeared fraction of species (global PDF, Verones et al., 2020). Thus, the monetary biodiversity impact factors are presented in the form BDe/€.

Prices are in basic prices and the conversion factors to transform purchaser prices (e.g. financial accounting prices) to basic prices are provided for Finland (and later for all regions), based on EXIOBASE supply and use tables (SUT).

Content of files

BiodiversityFootprintDatabase.xlsx

The biodiversity impact factors, regional abbreviations and basic price conversion factors for Finland.

BiodiversityFootprintDatabase_DetailedData.zip 

The detailed data used to combine EXIOBASE and LC-IMPACT data after the EXIOBASE data was analyzed with the pymrio tool. Contains folders for each driver of biodiversity loss according to the LC-IMPACT classification.

20220406_Exio3stressorcode _2011.py & 20220406_Exio3StressorAggregationCode_2011.py 

The pymrio codes that were used to analyze EXIOBASE and the geographical location of the drivers of biodiversity loss (mid-point indicators).
DOI: 10.5281/zenodo.8369650
title: BIOVALENT: Biodiversity Footprint Database

description: BIOVALENT: Biodiversity Footprint Database interface (with additional food-related biodiversity impact factors) can be found at: https://oscsolutions.cc.jyu.fi/wisdom/biovalent/

The BIOVALENT data files in Zenodo contain global consumption-based, monetary, biodiversity impact factors for 44 countries and five rest of the world regions. The dataset has been compiled by combining information from EXIOBASE and LC-IMPACT databases. In addition, the EXIOBASE database has been analyzed with the pymrio analysis tool to determine the geographical location of the consumption-based biodiversity impacts. The mid-point impact factors from EXIOBASE are based on 2019 data, but the regional analysis with pymrio is based on 2011 data. EXIOBASE version 3.8.2 was used and LC-IMPACT version 1.3. The data is currently non peer-reviewed and under submission. The preprint of the manuscript can be found from: https://doi.org/10.48550/arXiv.2309.14186

Please also cite the preprint when using the database:

El Geneidy, S., Peura, M., Aumanen, V.-M., Baumeister, S., Helimo, U., Vainio, V. & Kotiaho, J. S. (2025). Value-transforming financial, carbon and biodiversity footprint accounting. arXiv:2309.14186 [econ.GN]. https://doi.org/10.48550/arXiv.2309.14186

About the units

The unit used in the database is the biodiversity equivalent (BDe). The biodversity equivalent, as we call it, is more commonly known as the global potentially disappeared fraction of species (global PDF, Verones et al., 2020). Thus, the monetary biodiversity impact factors are presented in the form BDe/€.

Prices are in basic prices and the conversion factors to transform purchaser prices (e.g. financial accounting prices) to basic prices are provided for Finland (and later for all regions), based on EXIOBASE supply and use tables (SUT).

Content of files

BiodiversityFootprintDatabase.xlsx

The biodiversity impact factors, regional abbreviations and basic price conversion factors for Finland.

BiodiversityFootprintDatabase_DetailedData.zip 

The detailed data used to combine EXIOBASE and LC-IMPACT data after the EXIOBASE data was analyzed with the pymrio tool. Contains folders for each driver of biodiversity loss according to the LC-IMPACT classification.

20220406_Exio3stressorcode _2011.py & 20220406_Exio3StressorAggregationCode_2011.py 

The pymrio codes that were used to analyze EXIOBASE and the geographical location of the drivers of biodiversity loss (mid-point indicators).
DOI: 10.5281/zenodo.17670798
title: Supplementary materials, data and code for the article "Humans can learn bimodal priors in complex sensorimotor behaviour"

description: This repository contains supplementary material (Extended_Data_Tables, Explicit_Knowledge_Test) and a zip folder with data and code (bt-bimodal_prior_integration_vr_tennis-main.zip) for the article "Humans can learn bimodal priors in complex sensorimotor behaviour". 

The article is published in Proceedings of the Royal Society B: Biological Sciences (DOI: 10.1098/rspb)

Same content is available on https://github.com/ispw-unibe-ch/bt-bimodal_prior_integration_vr_tennis
DOI: 10.5281/zenodo.18376601
title: Supplementary materials, data and code for the article "Humans can learn bimodal priors in complex sensorimotor behaviour"

description: This repository contains supplementary material (Extended_Data_Tables, Explicit_Knowledge_Test) and a zip folder with data and code (bt-bimodal_prior_integration_vr_tennis-main.zip) for the article "Humans can learn bimodal priors in complex sensorimotor behaviour". 

The article is published in Proceedings of the Royal Society B: Biological Sciences (DOI: 10.1098/rspb)

Same content is available on https://github.com/ispw-unibe-ch/bt-bimodal_prior_integration_vr_tennis
DOI: 10.5517/ccdc.csd.cc13073g
title: CCDC 1013114: Experimental Crystal Structure Determination

description: Related Article: Adrian Zając, Remigiusz Żurawiński, Lesław Sieroń, Józef Drabowicz |2025|ChemPlusChem|90|e202400614|doi:10.1002/cplu.202400614
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc130q94
title: CCDC 1013585: Experimental Crystal Structure Determination

description: Related Article: Wei Luo, Yongping Ma, Meijing Wang, Jiaojiao Wang, Chengqiang Wu, Huihong Yang|2025|J.Mol.Struct.|1336|142140|doi:10.1016/j.molstruc.2025.142140
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc138r7c
title: CCDC 1021302: Experimental Crystal Structure Determination

description: Related Article: Wei Luo, Yongping Ma, Meijing Wang, Jiaojiao Wang, Chengqiang Wu, Huihong Yang|2025|J.Mol.Struct.|1336|142140|doi:10.1016/j.molstruc.2025.142140
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.ccvndvq
title: CCDC 794184: Experimental Crystal Structure Determination

description: Related Article: Caroline Delehedde, Marcel Culcasi, Emilie Ricquebourg, Mathieu Cassien, Didier Siri, Bruno Blaive, Sylvia Pietri, Sophie Thétiot-Laurent|2022|Molecules|27|4506|doi:10.3390/molecules27144506
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.ccvnb60
title: CCDC 794102: Experimental Crystal Structure Determination

description: Related Article: Caroline Delehedde, Marcel Culcasi, Emilie Ricquebourg, Mathieu Cassien, Didier Siri, Bruno Blaive, Sylvia Pietri, Sophie Thétiot-Laurent|2022|Molecules|27|4506|doi:10.3390/molecules27144506
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.ccvnd0w
title: CCDC 794158: Experimental Crystal Structure Determination

description: Related Article: Caroline Delehedde, Marcel Culcasi, Emilie Ricquebourg, Mathieu Cassien, Didier Siri, Bruno Blaive, Sylvia Pietri, Sophie Thétiot-Laurent|2022|Molecules|27|4506|doi:10.3390/molecules27144506
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.