cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7681876
title: Road Condition Image Dataset

description: Datasets containing 2D images from roads. The images were either rendered from 3D lidar point clouds or captured by a camera on a so called Mobile Mapping vehicle. For more information on how to use this dataset, visit the following repository: https://github.com/Snagnar/CompetitiveReconstructionNetworks
DOI: 10.5281/zenodo.7681875
title: Road Condition Image Dataset

description: Datasets containing 2D images from roads. The images were either rendered from 3D lidar point clouds or captured by a camera on a so called Mobile Mapping vehicle. For more information on how to use this dataset, visit the following repository: https://github.com/Snagnar/CompetitiveReconstructionNetworks
DOI: 10.5281/zenodo.7681808
title: Kerr Total Transmission Mode Mathematica Data: l=2--8

description: These tar.gz files contain the original Mathematica data files for the gravitational total transmission modes of the Kerr geometry for n=0 (Normal) and n=1 & 2 (Asymptotic) sequences for l=2--8. These data sets were constructed using the methods outlines in Cook & Zalutskiy, <em>Phys. Rev. D</em> <strong>90</strong> (2014) pp. 124021 (DOI: https://doi.org/10.1103/PhysRevD.90.124021) and Cook & Lu, <em>Phys. Rev. D</em> <strong>107</strong> (2023) pp. 044043 (DOI: https://doi.org/10.1103/PhysRevD.107.044043).
DOI: 10.5281/zenodo.7681807
title: Kerr Total Transmission Mode Mathematica Data: l=2--8

description: These tar.gz files contain the original Mathematica data files for the gravitational total transmission modes of the Kerr geometry for n=0 (Normal) and n=1 & 2 (Asymptotic) sequences for l=2--8. These data sets were constructed using the methods outlines in Cook & Zalutskiy, <em>Phys. Rev. D</em> <strong>90</strong> (2014) pp. 124021 (DOI: https://doi.org/10.1103/PhysRevD.90.124021) and Cook & Lu, <em>Phys. Rev. D</em> <strong>107</strong> (2023) pp. 044043 (DOI: https://doi.org/10.1103/PhysRevD.107.044043).
DOI: 10.6084/m9.figshare.20163834
title: Participatory Mapping: A Systematic Review and Open Science Framework for Future Research

description: Participatory mapping emerged from a need for more inclusive methods of collecting spatial data with the intention of democratizing the decision-making process. It encompasses a range of methods including mental mapping, sketch mapping, and participatory geographic information systems. There has been a rapid increase in uptake of participatory mapping, but the multidisciplinary nature of the field has resulted in a lack of consistency in the conducting and reporting of research, limiting further development. In this article, we argue that an open science approach is required to enable the field to advance, increasing transparency and replicability in how participatory mapping research is both conducted and reported. This argument is supported by the first large-scale systematic review of the field, which identifies specific areas within participatory mapping that would benefit from an open science approach. Four questions are used to explore the sample: (1) How are different participatory mapping methods being used and reported? (2) What information is given on the data collected through participatory mapping? (3) How are participant demographics being recorded? (4) Who is conducting the research, and where is it being published? From a total of 578 academic research articles, we analyzed a stratified sample of 117. The review reveals a significant lack of reporting on key details in the data collection process, restricting the transparency, replicability, and transferability of participatory mapping research and demonstrating the urgent need for an open science approach. Recommendations are then drawn from the results to guide the design of future participatory mapping research.
DOI: 10.6084/m9.figshare.20163834.v1
title: Participatory Mapping: A Systematic Review and Open Science Framework for Future Research

description: Participatory mapping emerged from a need for more inclusive methods of collecting spatial data with the intention of democratizing the decision-making process. It encompasses a range of methods including mental mapping, sketch mapping, and participatory geographic information systems. There has been a rapid increase in uptake of participatory mapping, but the multidisciplinary nature of the field has resulted in a lack of consistency in the conducting and reporting of research, limiting further development. In this article, we argue that an open science approach is required to enable the field to advance, increasing transparency and replicability in how participatory mapping research is both conducted and reported. This argument is supported by the first large-scale systematic review of the field, which identifies specific areas within participatory mapping that would benefit from an open science approach. Four questions are used to explore the sample: (1) How are different participatory mapping methods being used and reported? (2) What information is given on the data collected through participatory mapping? (3) How are participant demographics being recorded? (4) Who is conducting the research, and where is it being published? From a total of 578 academic research articles, we analyzed a stratified sample of 117. The review reveals a significant lack of reporting on key details in the data collection process, restricting the transparency, replicability, and transferability of participatory mapping research and demonstrating the urgent need for an open science approach. Recommendations are then drawn from the results to guide the design of future participatory mapping research.
DOI: 10.34740/kaggle/dsv/5075623
title: Mahanagar Yatayat Dataset
DOI: 10.5519/qd.58vstncd
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (33 records)
DOI: 10.6084/m9.figshare.21548724
title: Supplementary Materials: Gene expression dynamics in hibernator adrenal gland

description: One pdf file containing three Supplementary figures with legends exported from powerpoint seven Supplementary Tables: Table S1: Animal metadata, excel file with two tabs Table S2: Sequencing library QC, excel file 1 tab Table S3: rlog counts, gzipped tsv file Table S4: batch-corrected rlog counts, gzipped tsv file Table S5: DE genes (q&lt;0.001) from all states and the 7 sequential pairs, excel file with 8 tabs Table S6: Top-expressed genes and top cell specific genes, excel file with 2 tabs Table S7: Gene enrichment analysis output from DAVID and STRING, excel file with 6 tabs
DOI: 10.6084/m9.figshare.21548724.v1
title: Supplementary Materials: Gene expression dynamics in hibernator adrenal gland

description: One pdf file containing three Supplementary figures with legends exported from powerpoint seven Supplementary Tables: Table S1: Animal metadata, excel file with two tabs Table S2: Sequencing library QC, excel file 1 tab Table S3: rlog counts, gzipped tsv file Table S4: batch-corrected rlog counts, gzipped tsv file Table S5: DE genes (q&lt;0.001) from all states and the 7 sequential pairs, excel file with 8 tabs Table S6: Top-expressed genes and top cell specific genes, excel file with 2 tabs Table S7: Gene enrichment analysis output from DAVID and STRING, excel file with 6 tabs
DOI: 10.5281/zenodo.7682043
title: Data in the western boundary current of the South China Sea

description: This is the data that are used for reproducing the results of the paper by Shang et al. (2023) entitled "Observed Frontal Submesoscale Processes below the Mixed Layer in the Western Boundary Current of the South China Sea".
DOI: 10.5281/zenodo.7682042
title: Data in the western boundary current of the South China Sea

description: This is the data that are used for reproducing the results of the paper by Shang et al. (2023) entitled "Observed Frontal Submesoscale Processes below the Mixed Layer in the Western Boundary Current of the South China Sea".
DOI: 10.5519/qd.0awn4wj0
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2700 records)
DOI: 10.25345/c5cv4c22g
title: MassIVE MSV000091371 - Plasma degradome association with severe and moderate alcoholic hepatitis
title: The low molecular weight proteome (aka peptidome or degradome) was isolated by TCA precipitation, neutralization, and HLB concentration desalting. iRT peptides were added to samples prior to isolation and used as internal calibration and RT controls to monitor LCMS performance. High resolution MS1 (FTMS@240,000) data were collected prior to top20 DDA dependent data (ITMS). Data were analyzed using PeaksX Studio for de novo peptide sequence and PTM assignments. TIC normalized data were exported into excel files used for statistical modeling using TIC area normalized data for peptide groups based on overlapping sequences.
DOI: 10.5281/zenodo.7682100
title: Collective Decision-Making and Change Detection with Bayesian Robots in Dynamic Environments

description: The following folder structure holds all research data of my conducted experiments(h5-logfiles and plots). The Python-Script "show_h5.py" can be used to read out the logfile in h5-format (<em>$python3 show_h5.py expample_logfilename.h5</em>). However, this shouldn't be necessary because all plots are already generated. To find the results you want to see, this is a small guide through the structure: First the trials are divided into the respective methods (PELT, DBB, DBBCPD). In the folders you find the experiments for the specific method. In the folder of PELT you find the results for the different feedback types and their combinations. The id for each feedback is noted in parentheses (e.g. XX_(id)_feedback_description). Feedback combinations have their ids added up (e.g. XX_(id1+...+idn)_feedback_description). In the folder to each feedback type the different test trials can be found. This means varying environment difficulties and parameter settings. In the name of the folders this information can be found (e.g. XX_method_environmentdifficulty_parametersetting). All experiments follow the same procedure as long as it is stated otherwise. Each trial consists of 20 individual runs with a duration of 6000 seconds. At half time (3000 s) a change to the opposite fill ratio occurs (fill ratio of 1.0 defines a completely white and one of 0.0 a completely black environment). <strong>Environment difficulty</strong> 0901 --&gt; easy environment, fill ratio changed from 0.9 to 0.1 0703 --&gt; easy environment, fill ratio changed from 0.7 to 0.3 0604 --&gt; easy environment, fill ratio changed from 0.6 to 0.4 055045 --&gt; easy environment, fill ratio changed from 0.55 to 0.45 <strong>Parameter Setting</strong> The setting is in the name of the folder composed of: feedbackID: intervalLength amountNeighbors 3c:50s3n --&gt; feedback 3c with a 50s interval and 3 neighbors In these folders all plots of the respective runs can be found showing a Boxplot of all 20 runs and for each run the swarm belief, the decision distribution and the reset histogram (before/after the change)
DOI: 10.5281/zenodo.7682099
title: Collective Decision-Making and Change Detection with Bayesian Robots in Dynamic Environments

description: The following folder structure holds all research data of my conducted experiments(h5-logfiles and plots). The Python-Script "show_h5.py" can be used to read out the logfile in h5-format (<em>$python3 show_h5.py expample_logfilename.h5</em>). However, this shouldn't be necessary because all plots are already generated. To find the results you want to see, this is a small guide through the structure: First the trials are divided into the respective methods (PELT, DBB, DBBCPD). In the folders you find the experiments for the specific method. In the folder of PELT you find the results for the different feedback types and their combinations. The id for each feedback is noted in parentheses (e.g. XX_(id)_feedback_description). Feedback combinations have their ids added up (e.g. XX_(id1+...+idn)_feedback_description). In the folder to each feedback type the different test trials can be found. This means varying environment difficulties and parameter settings. In the name of the folders this information can be found (e.g. XX_method_environmentdifficulty_parametersetting). All experiments follow the same procedure as long as it is stated otherwise. Each trial consists of 20 individual runs with a duration of 6000 seconds. At half time (3000 s) a change to the opposite fill ratio occurs (fill ratio of 1.0 defines a completely white and one of 0.0 a completely black environment). <strong>Environment difficulty</strong> 0901 --&gt; easy environment, fill ratio changed from 0.9 to 0.1 0703 --&gt; easy environment, fill ratio changed from 0.7 to 0.3 0604 --&gt; easy environment, fill ratio changed from 0.6 to 0.4 055045 --&gt; easy environment, fill ratio changed from 0.55 to 0.45 <strong>Parameter Setting</strong> The setting is in the name of the folder composed of: feedbackID: intervalLength amountNeighbors 3c:50s3n --&gt; feedback 3c with a 50s interval and 3 neighbors In these folders all plots of the respective runs can be found showing a Boxplot of all 20 runs and for each run the swarm belief, the decision distribution and the reset histogram (before/after the change)
DOI: 10.6084/m9.figshare.21313650
title: Road mortality data for Ecuadorian wildlife

description: Data for publication Medrano-Vizcaíno P., Brito-Zapata, D., Rueda, A., Jarrín, P., Aguilar, J., Acosta, N., and González-Suárez M. 2022. First national assessment of wildlife mortality in Ecuador: an effort from citizens and academia to collect roadkill data at country scale. <br> We include two .csv files that contain 333 roadkill rates for 242 Ecuadorian species (Appendix1_roadkillrates_systematic studies2023.csv), and 5010 roadkill records (4244 georeferenced) from 392 Ecuadorian species (Appendix2_Roadkill records Ecuador_2023). <br>
DOI: 10.5281/zenodo.7590681
title: Dataset on characterization of multilayer films waste

description: Dataset correponding to the public devliverable D2.1 available on Zenodo.
DOI: 10.5281/zenodo.7590680
title: Dataset on characterization of multilayer films waste

description: Dataset correponding to the public devliverable D2.1 available on Zenodo.
DOI: 10.5281/zenodo.7571283
title: Dataset: BacRefSeq and Mini-BacRefSeq

description: Please see README.md
DOI: 10.5281/zenodo.7571307
title: Dataset: BacRefSeq and Mini-BacRefSeq

description: Please see README.md
DOI: 10.5281/zenodo.7571282
title: Dataset: BacRefSeq and Mini-BacRefSeq

description: Please see README.md
DOI: 10.5281/zenodo.7681330
title: Dataset: BacRefSeq and Mini-BacRefSeq

description: Please see README.md
DOI: 10.5281/zenodo.6546712
title: van Hijfte GBM dataset 2022/A

description: Dataset containing raw single nucleus-RNA sequencing counts from one glioblastoma. MGMT status: methylated EGFR status: unstable IDH status: wildtype Sex: male Age at resection: 53
DOI: 10.5281/zenodo.6546711
title: van Hijfte GBM dataset 2022/A

description: Dataset containing raw single nucleus-RNA sequencing counts from one glioblastoma. MGMT status: methylated EGFR status: unstable IDH status: wildtype Sex: male Age at resection: 53
DOI: 10.5281/zenodo.7682148
title: data: Early visual deprivation disrupts the mental representation of numbers in visually impaired children.

description: The datasets contain the raw data of PAE and PE variables.
DOI: 10.5281/zenodo.7682147
title: data: Early visual deprivation disrupts the mental representation of numbers in visually impaired children.

description: The datasets contain the raw data of PAE and PE variables.
DOI: 10.6084/m9.figshare.22151879
title: Lemmatization.py output

description: Lemmatization of metadata recovered from BioSample NCBI.
DOI: 10.6084/m9.figshare.22151879.v1
title: Lemmatization.py output

description: Lemmatization of metadata recovered from BioSample NCBI.
DOI: 10.6084/m9.figshare.22151879.v2
title: Lemmatization.py output

description: Lemmatization of metadata recovered from BioSample NCBI.
DOI: 10.6084/m9.figshare.22151882.v1
title: Search_bioportal.py output

description: Bioportal REST API result for the tool "Search class".
DOI: 10.15129/884b5286-bdb3-4eb9-bc47-08c77caf5f9e
title: A 2018 UK SAM with households disaggregated by income

description: This is a 2018 UK Social Accounting Matrix (SAM) where the households have been disaggregated into quintiles based on the households' gross income. This SAM incorporates the latest 2018 input-output tables published by the Office for National Statistics, with the sectors being aggregated into 34 broader sectors. The households have been disaggregated using information from the Living Cost and Food survey. This SAM is relevant for projects looking to identify not only the economy-wide impacts of shocks to the UK economy, but also explore how households of different income are impacted. Moreover, it allows the exploration of shocks to specific household quintiles and how these could affect the wider economy.
DOI: 10.5281/zenodo.5589277
title: SPoD-Ex Systematic Review Datasets

description: Datasets of the systematic review project "Exercise Interventions in Patients with Mild Cognitive Impairment: A Systematic Review of Long-term Patient-relevant Cognitive and Non-Cognitive Effects". All versions of the search strategy will be uploaded as seperate files continousely throughout search strategy development.
DOI: 10.5281/zenodo.7544896
title: SPoD-Ex Systematic Review Datasets

description: Datasets of the systematic review project "Exercise Interventions in Patients with Mild Cognitive Impairment: A Systematic Review of Long-term Patient-relevant Cognitive and Non-Cognitive Effects". Search strategy for different databases Forest plots without meta-analysis Excluded studies with reasons for exclusion
DOI: 10.5281/zenodo.5589276
title: SPoD-Ex Systematic Review Datasets

description: Datasets of the systematic review project "Exercise Interventions in Patients with Mild Cognitive Impairment: A Systematic Review of Long-term Patient-relevant Cognitive and Non-Cognitive Effects". Search strategy for different databases Forest plots without meta-analysis Excluded studies with reasons for exclusion
DOI: 10.5281/zenodo.7682173
title: SPoD-Ex Systematic Review Datasets

description: Datasets of the systematic review project "Exercise Interventions in Patients with Mild Cognitive Impairment: A Systematic Review of Long-term Patient-relevant Cognitive and Non-Cognitive Effects". Search strategy for different databases Forest plots without meta-analysis Excluded studies with reasons for exclusion
DOI: 10.5281/zenodo.7679377
title: Magnetic braking with MESA evolutionary models in the single star and LMXB regimes (Gossage et al. 2023) - Inlists, source code, history files

description: Associated MESA inlists, source files (run_star_extras.f90 and run_binary_extras.f), and outputs (history files for single star models --but the same for binary star models is available upon request) used in producing models featured in Gossage et al. 2023 - Magnetic braking with MESA evolutionary models in the single star and LMXB regimes). README files are included, and please contact to alert me of any issues.
DOI: 10.5281/zenodo.7679376
title: Magnetic braking with MESA evolutionary models in the single star and LMXB regimes (Gossage et al. 2023) - Inlists, source code, history files

description: Associated MESA (r11701) inlists, source files (run_star_extras.f90 and run_binary_extras.f), and outputs (history files for single star models --but the same for binary star models is available upon request) used in producing models featured in Gossage et al. 2023 - Magnetic braking with MESA evolutionary models in the single star and LMXB regimes). README files are included, and please contact to alert me of any issues.
DOI: 10.5281/zenodo.7679543
title: Magnetic braking with MESA evolutionary models in the single star and LMXB regimes (Gossage et al. 2023) - Inlists, source code, history files

description: Associated MESA (r11701) inlists, source files (run_star_extras.f90 and run_binary_extras.f), and outputs (history files for single star models --but the same for binary star models is available upon request) used in producing models featured in Gossage et al. 2023 - Magnetic braking with MESA evolutionary models in the single star and LMXB regimes). README files are included, and please contact to alert me of any issues.
DOI: 10.5281/zenodo.7682151
title: Magnetic braking with MESA evolutionary models in the single star and LMXB regimes (Gossage et al. 2023) - Inlists, source code, history files

description: Associated MESA (r11701) inlists, source files (run_star_extras.f90 and run_binary_extras.f), and outputs (history files for single star models --but the same for binary star models is available upon request) used in producing models featured in Gossage et al. 2023 - Magnetic braking with MESA evolutionary models in the single star and LMXB regimes). README files are included, and please contact to alert me of any issues.
DOI: 10.26036/cnp0004064
title: miRNA data of testicular tissue of adult and juvenile crabs in Eriocheir sinensis

description: miRNA data of testicular tissue of adult and juvenile crabs in Eriocheir sinensis
DOI: 10.21227/88e1-ej71
title: Supplementary material to Improving the teaching of real-world software practices by means of course integration

description: Supplementary material to the article "Improving the teaching of real-world software practices by means of course integration"
DOI: 10.6084/m9.figshare.22060922
title: EID2_organism_interactions_raw_2022.csv

description: EID2_organism_interactions_raw_2022.csv contains organism-organism (cargo-carrier) interactions extracted from the Enhanced Infectious Diseases Database - EID2 (https://eid2.liverpool.ac.uk/). For full description of EID2 please refer to: https://www.nature.com/articles/sdata20154. All interactions im this file are supported by one genetic sequence or 4 or more publicaitons and is structured as follows: 1. cargo: name of cargo organism (species, subspecies or lower rank). 2. cargoID: EID2 ID of the cargo 3. cargoTaxID: NCBI taxid for the cargo 4. cargoRank: the rank of the cargo 5. cargoCategory: the taxonomic classification of the cargo (e.g., bacteria, virus, etc.) 6. carrier: name of carrier organism (species, subspecies or lower rank) 7. carrierID: EID2 ID of the carrier 8. carrierTaxID: NCBI taxid for the carrier 9. carrierRank: the rank of the carrier 10. carrierCategory: the taxonomic classification of the carrier. 11. sequencesNo: Total number of nucleotide sequences supporting the interaction. 12. papersNo: Total number of publications supporting the interaction.
DOI: 10.6084/m9.figshare.22060922.v1
title: EID2_organism_interactions_raw_2022.csv

description: EID2_organism_interactions_raw_2022.csv contains organism-organism (cargo-carrier) interactions extracted from the Enhanced Infectious Diseases Database - EID2 (https://eid2.liverpool.ac.uk/). For full description of EID2 please refer to: https://www.nature.com/articles/sdata20154. All interactions im this file are supported by one genetic sequence or 4 or more publicaitons and is structured as follows: 1. cargo: name of cargo organism (species, subspecies or lower rank). 2. cargoID: EID2 ID of the cargo 3. cargoTaxID: NCBI taxid for the cargo 4. cargoRank: the rank of the cargo 5. cargoCategory: the taxonomic classification of the cargo (e.g., bacteria, virus, etc.) 6. carrier: name of carrier organism (species, subspecies or lower rank) 7. carrierID: EID2 ID of the carrier 8. carrierTaxID: NCBI taxid for the carrier 9. carrierRank: the rank of the carrier 10. carrierCategory: the taxonomic classification of the carrier. 11. sequencesNo: Total number of nucleotide sequences supporting the interaction. 12. papersNo: Total number of publications supporting the interaction.
DOI: 10.25345/c5833n79h
title: MassIVE MSV000091372 - Example Waters FastDDA data set
title: Example FastDDA raw data collected on a Waters Synapt XS. Negative Mode. No promises on the calibration and accuracy; just putting it up for testing purposes.