cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7991497
title: DAACI-VoDAn Dataset

description: DAACI-VoDAn is a dataset for vocal detection that comprises manually-generated vocal activity annotations for 706 full-length music tracks, as well as their associated metadata including song title, artist, and YouTube URL. This data repository contains the metadata file and the annotations, all as CSV files. The music tracks in DAACI-VoDAn were selected to cover a broad variety w.r.t. genre, era, and instrumentation, and are intended to be representative of the music universe available through commercial catalogues and streaming services. <strong>Citing, license, and using the dataset</strong> This dataset is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License and it is intended to be used only for research purposes. DAACI-VoDAn is presented in the following paper, accepted for publication at EUSIPCO (European Signal Processing Conference) 2023. Please, cite this paper when using the dataset: Helena Cuesta, Nadine Kroher, Aggelos Pikrakis, Stojan Djordjevic. <strong>DAACI-VoDAn: Improving Vocal Detection with New Data and Methods</strong>. To appear in Proceedings of the European Signal Processing Conference (EUSIPCO). 2023. <strong>Annotation format</strong> Vocal regions are provided as CSV files with a unique track ID as filename (matching the IDs specified in the metadata file). They were created manually by a team of four annotators following a peer-reviewing strategy, i.e., each annotation was created by one annotator and reviewed by another. Each row in the CSV files represents a vocal region given by the tuple [start_time, duration] in seconds.<br> <br> The task of annotating vocal segments deals with numerous ambiguous scenarios. To provide consistent and extendable annotations, we defined an annotation strategy in the form of a set of rules: We defined a minimum inter-region distance of 200 ms. Two vocal regions which are at least 200 ms apart are annotated as separate regions. Shorter vocal rests are not considered and a single long region is annotated instead. We annotated heavily processed (e.g., chorus or distortion effects) singing voice sections as vocals, as long as they are still recognisable as originating from a human voice. We extended vocal sections to include reverb and delay “tails" as long as they are audible. Similarly, we considered inhalations or “breathing" sounds, e.g., at the beginning of a vocal phrase, as part of vocal section. We considered segments containing speech as vocal sections but excluded whistling. Vocal ensembles and background vocals are also labelled as vocals.
DOI: 10.6084/m9.figshare.23391566
title: Controlled Seeding of Contact Cracks in Testpieces for the Evaluation and Validation of Non-Destructive Testing Systems (Data for F1000.xlsx)

description: Source data for graphs
DOI: 10.6084/m9.figshare.23391566.v1
title: Controlled Seeding of Contact Cracks in Testpieces for the Evaluation and Validation of Non-Destructive Testing Systems (Data for F1000.xlsx)

description: Source data for graphs
DOI: 10.25835/gggv8xg7
title: An Interview Study on the Use, Comprehension, and Perceived Risks of Unsafe Rust

description: The replication package for the paper: “I wouldn’t want my unsafe code to run my pacemaker”: An Interview Study on the Use, Comprehension, and Perceived Risks of Unsafe Rust
DOI: 10.6084/m9.figshare.23377769.v1
title: data_with_covariates_v9_12.csv

description: These items are essential data and scripts for the project "Wild megafauna shape ecosystem properties and promote spatial heterogeneity globally"
DOI: 10.6084/m9.figshare.22335229
title: Raw data used in the analysis of Browett et al.

description: Raw data for use with the R Analysis Script for the article "Prior exposure of microbial communities to seawater reduces resilience but increases compositional and functional resistance to flooding events." by Lewis C. Browett, Sharon Ruiz-Lopez, Hannah L. Mossman, Andrew P. Dean & Damian W. Rivett
DOI: 10.6084/m9.figshare.22335229.v1
title: Raw data used in the analysis of Browett et al.

description: Raw data for use with the R Analysis Script for the article "Prior exposure of microbial communities to seawater reduces resilience but increases compositional and functional resistance to flooding events." by Lewis C. Browett, Sharon Ruiz-Lopez, Hannah L. Mossman, Andrew P. Dean & Damian W. Rivett
DOI: 10.6084/m9.figshare.23377769.v2
title: data_with_covariates_v9_12.csv

description: These items are essential data and scripts for the project "Wild megafauna shape ecosystem properties and promote spatial heterogeneity globally"
DOI: 10.5281/zenodo.8017577
title: Dataset for "Exposure and environmental engagement: A pilot integrating wearable sensors, air quality and citizen science"

description: The dataset contains anonymised readings of 7 citizens taking air quality measurements using PlumeLabs Flow 2 monitor. Data is for Falmouth/Penryn, and Bristol and it was collected between January 26, 2022 and March 9, 2022. CSV file: latitude: unit degrees, positive values indicate North hemisphere. longitude, unit degrees, positive values indicate East. AQI: PlumeLabs' Air Quality Index. site: A refers to Falmouth/Penryn(UK), B refers to Bristol (UK). count: auxiliary variable that indicates that the record was comprised of a single reading. Jupyter notebook: The air quality analysis was conducted with Python 3.9.16 alongside numpy 1.24.3, pandas 2.0.2, matplotlib 3.7.1, and cartopy 0.21.1 (background tiles by OpenStreetMaps).
description: Funding was provided by the GW4 Crucible and the authors would like to thank the funding application reviewers and the administrators of the programme for their support.
DOI: 10.5281/zenodo.8017578
title: Dataset for "Exposure and environmental engagement: A pilot integrating wearable sensors, air quality and citizen science"

description: The dataset contains anonymised readings of 7 citizens taking air quality measurements using PlumeLabs Flow 2 monitor. Data is for Falmouth/Penryn, and Bristol and it was collected between January 26, 2022 and March 9, 2022. CSV file: latitude: unit degrees, positive values indicate North hemisphere. longitude, unit degrees, positive values indicate East. AQI: PlumeLabs' Air Quality Index. site: A refers to Falmouth/Penryn(UK), B refers to Bristol (UK). count: auxiliary variable that indicates that the record was comprised of a single reading. Jupyter notebook: The air quality analysis was conducted with Python 3.9.16 alongside numpy 1.24.3, pandas 2.0.2, matplotlib 3.7.1, and cartopy 0.21.1 (background tiles by OpenStreetMaps).
description: Funding was provided by the GW4 Crucible and the authors would like to thank the funding application reviewers and the administrators of the programme for their support.
DOI: 10.5281/zenodo.6902763
title: Data from: Will Current Protected Areas Harbour Refugia for Threatened Arctic Vegetation Types until 2050? A First Assessment

description: We present predictions of Arctic vegetation for 2050 based on a combination of climate models (namely, EC-Earth3-Veg, IPSL-CM6A-LR, and MRI-ESM2-0), emission scenarios (names, SSP126 and SSP585) and tree dispersal rate scenarios (unrestricted, 20km and 5km) based on the methods of Pearson et al. (2013) and the new raster version of the Circumpolar Arctic Vegetation Map (CAVM) (Raynolds et al. 2019). We additionally present a dataset summarising total areas for each vegetation type in the CAVM and the forecasted models based on the computation of zonal histograms in ArcGIS (zonal_histogram_results.csv), for the total Arctic as well as only within protected areas, defined by the Map of Arctic Protected Areas (CAFF and PAME 2017). We also present a potential map of refugia for what we deem the realistic model (IPSL, SSP585, 20 km tree dispersal) as a raster file. Refugia were identified as regions where the vegetation remained the same between the CAVM and the predictions. Additionally, we present a map of model agreement, showing the degree to which other models agree with the vegetation classification for our refugia. All predictions named according to the tree dispersal rate, climate model, and emissions scenario, preceded by the term "pred". For example: "pred_unres_mri_585" represents the unrestricted tree dispersal, MRI-ESM-0 climate model, and SSP585 scenario-based prediction. The MRI-ESM-0 x SSP585 combination had gaps in data which results in a lack of predictions in some areas; this affects 3 models. Further details and all code associated with these datasets are found here.
description: We thank University of Zurich's URPP GCB for the funding which made this project possible.
description: {"references": ["Pearson, R., Phillips, S., Loranty, M. et al. Shifts in Arctic vegetation and associated feedbacks under climate change. Nature Clim Change 3, 673\u2013677 (2013). https://doi.org/10.1038/nclimate1858", "Raynolds, Martha K., Donald A. Walker, Andrew Balser, Christian Bay, Mitch Campbell, Mikhail M. Cherosov, Fred J.A. Dani\u00ebls, et al. 2019. \"A Raster Version of the Circumpolar Arctic Vegetation Map (CAVM).\" Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2019.111297.", "CAFF, and PAME. 2017. Arctic Protected Areas: Indicator Report, 2017: Conservation of Arctic Flora and Fauna and Protection of the Arctic Marine Environment. Akureyri, Iceland."]}
DOI: 10.5281/zenodo.6902764
title: Data from: Will Current Protected Areas Harbour Refugia for Threatened Arctic Vegetation Types until 2050? A First Assessment

description: We present predictions of Arctic vegetation for 2050 based on a combination of climate models (namely, EC-Earth3-Veg, IPSL-CM6A-LR, and MRI-ESM2-0), emission scenarios (names, SSP126 and SSP585) and tree dispersal rate scenarios (unrestricted, 20km and 5km) based on the methods of Pearson et al. (2013) and the new raster version of the Circumpolar Arctic Vegetation Map (CAVM) (Raynolds et al. 2019). We additionally present a dataset summarising total areas for each vegetation type in the CAVM and the forecasted models based on the computation of zonal histograms in ArcGIS (zonal_histogram_results.csv), for the total Arctic as well as only within protected areas, defined by the Map of Arctic Protected Areas (CAFF and PAME 2017). We also present a potential map of refugia for what we deem the realistic model (IPSL, SSP585, 20 km tree dispersal) as a raster file. Refugia were identified as regions where the vegetation remained the same between the CAVM and the predictions. Additionally, we present a map of model agreement, showing the degree to which other models agree with the vegetation classification for our refugia. All predictions named according to the tree dispersal rate, climate model, and emissions scenario, preceded by the term "pred". For example: "pred_unres_mri_585" represents the unrestricted tree dispersal, MRI-ESM-0 climate model, and SSP585 scenario-based prediction. The MRI-ESM-0 x SSP585 combination had gaps in data which results in a lack of predictions in some areas; this affects 3 models. Further details and all code associated with these datasets are found here.
description: We thank University of Zurich's URPP GCB for the funding which made this project possible.
description: {"references": ["Pearson, R., Phillips, S., Loranty, M. et al. Shifts in Arctic vegetation and associated feedbacks under climate change. Nature Clim Change 3, 673\u2013677 (2013). https://doi.org/10.1038/nclimate1858", "Raynolds, Martha K., Donald A. Walker, Andrew Balser, Christian Bay, Mitch Campbell, Mikhail M. Cherosov, Fred J.A. Dani\u00ebls, et al. 2019. \"A Raster Version of the Circumpolar Arctic Vegetation Map (CAVM).\" Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2019.111297.", "CAFF, and PAME. 2017. Arctic Protected Areas: Indicator Report, 2017: Conservation of Arctic Flora and Fauna and Protection of the Arctic Marine Environment. Akureyri, Iceland."]}
DOI: 10.6084/m9.figshare.23357372
title: RosaMariaGutierrezRios_citasA_B.pdf

description: Lista de citas tipo A y tipo B en scopus <br>
DOI: 10.6084/m9.figshare.23357372.v2
title: RosaMariaGutierrezRios_citasA_B.pdf

description: Lista de citas tipo A y tipo B en scopus <br>
DOI: 10.6084/m9.figshare.23357372.v1
title: RosaMariaGutierrezRios_citasA_B.pdf

description: Lista de citas tipo A y tipo B en scopus <br>
DOI: 10.4121/980484ec-5187-4f73-9e57-2e0dcd1330cc.v1
title: Code and Data for "Availability and utilisation of airspace structure in a U-space traffic management system"

description: This contains the code and output logs to run the BlueSky simulator for "Availability and utilisation of airspace structure in a U-space traffic management system". The code provided here is a modified version of the main fork of BlueSky (https://github.com/TUDelft-CNS-ATM/bluesky).<br>The first step is to install the correct environment. Refer to `condaenv.txt` for the list of packages needed to run the simulator.<br>After setting up the environment, we then need to save all of the potential paths of drones in `bluesky/plugins/streets/path_plan_dills`. Note that this takes about 180GB of storage so make sure to have enough available. The paths can be downloaded from https://surfdrive.surf.nl/files/index.php/s/makXrEfPtrtdzaO. There are some example paths saved in this dataset but it will not be possible to run all of the experiment without downloading the paths.<br>The scenarios for sub-experiment 1 are saved in `bluesky/scenario/subexperiment1`.<br>The scenarios for sub-experiment 2 are saved in `bluesky/scenario/subexperiment2`.<br>To run the scenarios we first need to start a bluesky server by running the following code inside `bluesky`:<br>`python BlueSky.py --headless`<br>In another terminal we can start a bluesky client by running:<br>`python BlueSky.py --client`<br>In the bluesky console we can now run each batch scenario by typing and entering:<br>`batch batch_subexperiment_1.scn` or`batch batch_subexperiment_2.scn`<br>The logs of the scenarios are saved in `bluesky/output`.<br>Without the paths, it will not possible to run the simulations. However, this code currently has some paths so that it is possible to run some example scenarios. The zeroth repetition for the low imposed traffic demand case can be run without all of the paths. For example, `bluesky/scenario/subexperiment1/Flight_intention_low_40_0_1to1.scn` and `bluesky/scenario/subexperiment2/Flight_intention_low_40_0_baseline.scn` can be run directly with this dataset.<br>First start bluesky by running:<br>`python BlueSky.py`<br>In the console, type.<br>`ic subexperiment2/Flight_intention_low_40_0_baseline.scn`<br>Please do not hesistate to contact me with any questions.<br>-Andres<br><br>
DOI: 10.26249/fk2/abydfb/hot98u
title: Rappeneau et al. PMID 37165673-SAGE.xlsx

description: SAGE data
DOI: 10.26249/fk2/abydfb/psgb5a
title: Rappeneau et al. PMID 37165673-microarray.xls

description: microarray data
DOI: 10.26249/fk2/abydfb
title: Molecular changes in hippocampal energy metabolism in mice selectively bred for extremes in stress reactivity: Relevance of mitochondrial dysfunction for affective disorders.

description: Affective disorders, such as major depression, are frequently associated with metabolic disturbances involving mitochondria. Although dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis is known to alter energy metabolism, the precise mechanisms linking stress and metabolic disturbances are not sufficiently understood. We used a mouse model of affective disorders to investigate the impact of a genetic predisposition for extremes in stress reactivity on behavioural and metabolic phenotypes as well as energy metabolism. Adult males of three independent mouse lines selectively bred for high, intermediate or low HPA axis reactivity were tested for exploratory and locomotor activity as well as stress-coping behaviour. Additionally, basal and stress-induced plasma corticosterone levels, body weight, food intake and body composition were measured. At the molecular level, the hippocampal transcriptome was analysed using microarray, serial analysis of gene expression and qRT-PCR. Finally, mitochondrial DNA copy number, damages and mitochondrial respiration were assessed. We found clear effects of the differential stress reactivity on the behavioural, morphometric and metabolic measures. Remarkably, the hyperactive behavioural and neuroendocrine stress-coping style of high-reactivity mice was associated with significant changes in the expression of an extended list of genes involved in energy metabolism and several mitochondrial functions. Yet, only minor changes were found in mitochondrial DNA copy number, damages and respiration. Thus, our findings support a prominent role of glucocorticoids in shaping the major endophenotypes of the stress reactivity mouse model and contribute towards understanding the important role of HPA axis dysregulation and changes in energy metabolism in the pathophysiology of affective disorders.
DOI: 10.6084/m9.figshare.23392442
title: Ecoanxiety data

description: Raw data
DOI: 10.6084/m9.figshare.23392442.v1
title: Ecoanxiety data

description: Raw data
DOI: 10.5517/ccdc.csd.cc276ybr
title: CCDC 2062254: Experimental Crystal Structure Determination

description: Related Article: Alexandra M. Antonio, Kyle J. Korman, Meaghan M. Deegan, Garrett A. Taggart, Glenn P. A. Yap, Eric D. Bloch|2022|Inorg.Chem.|61|4609|doi:10.1021/acs.inorgchem.1c03519
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.cc276ydt
title: CCDC 2062256: Experimental Crystal Structure Determination

description: Related Article: Alexandra M. Antonio, Kyle J. Korman, Meaghan M. Deegan, Garrett A. Taggart, Glenn P. A. Yap, Eric D. Bloch|2022|Inorg.Chem.|61|4609|doi:10.1021/acs.inorgchem.1c03519
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.cc276ygw
title: CCDC 2062258: Experimental Crystal Structure Determination

description: Related Article: Alexandra M. Antonio, Kyle J. Korman, Meaghan M. Deegan, Garrett A. Taggart, Glenn P. A. Yap, Eric D. Bloch|2022|Inorg.Chem.|61|4609|doi:10.1021/acs.inorgchem.1c03519
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.cc276yhx
title: CCDC 2062259: Experimental Crystal Structure Determination

description: Related Article: Alexandra M. Antonio, Kyle J. Korman, Meaghan M. Deegan, Garrett A. Taggart, Glenn P. A. Yap, Eric D. Bloch|2022|Inorg.Chem.|61|4609|doi:10.1021/acs.inorgchem.1c03519
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.cc2c4l15
title: CCDC 2179146: Experimental Crystal Structure Determination

description: Related Article: Xi-Xi Wu, Yonghui He, Xiu-Xiu Qiao, Tao Ma, Chang-Peng Zou, Ganpeng Li, Xiao-Jing Zhao|2023|J.Org.Chem.|88|6599|doi:10.1021/acs.joc.2c02765
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.cc2cn5bk
title: CCDC 2194128: Experimental Crystal Structure Determination

description: Related Article: Benjamin E. Rennie, Jeffrey S. Price, David J. H. Emslie, Robert H. Morris|2023|Inorg.Chem.|62|8123|doi:10.1021/acs.inorgchem.2c04432
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.cc2cz5q8
title: CCDC 2203750: Experimental Crystal Structure Determination

description: Related Article: Lin-jie Yang, Wenye Xuan, David Webster, Lethy Krishnan Jagadamma, Teng Li, David N. Miller, David B. Cordes, Alexandra M. Z. Slawin, Graham A. Turnbull, Ifor D. W. Samuel, Hsin-Yi Tiffany Chen, Philip Lightfoot, Matthew S. Dyer, Julia L. Payne|2023|Chem.Mater.|35|3801|doi:10.1021/acs.chemmater.2c03125
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.cc2cz5l4
title: CCDC 2203746: Experimental Crystal Structure Determination

description: Related Article: Lin-jie Yang, Wenye Xuan, David Webster, Lethy Krishnan Jagadamma, Teng Li, David N. Miller, David B. Cordes, Alexandra M. Z. Slawin, Graham A. Turnbull, Ifor D. W. Samuel, Hsin-Yi Tiffany Chen, Philip Lightfoot, Matthew S. Dyer, Julia L. Payne|2023|Chem.Mater.|35|3801|doi:10.1021/acs.chemmater.2c03125
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.cc2cz5m5
title: CCDC 2203747: Experimental Crystal Structure Determination

description: Related Article: Lin-jie Yang, Wenye Xuan, David Webster, Lethy Krishnan Jagadamma, Teng Li, David N. Miller, David B. Cordes, Alexandra M. Z. Slawin, Graham A. Turnbull, Ifor D. W. Samuel, Hsin-Yi Tiffany Chen, Philip Lightfoot, Matthew S. Dyer, Julia L. Payne|2023|Chem.Mater.|35|3801|doi:10.1021/acs.chemmater.2c03125
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.