cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7516685
title: Protein network analysis links the NSL complex to Parkinson's disease via mitochondrial & nuclear biology

description: This online depository corresponds to manuscript : <em>Protein network analysis links the NSL complex to Parkinson’s disease via mitochondrial & nuclear biology.</em> <strong>Authors: </strong><em>Katie Kelly, Patrick A. Lewis, Helene Plun-Favreau, Claudia Manzoni</em> Whilst the majority (~90-95%) of PD cases are sporadic, much of our understanding of the pathophysiological basis of disease can be traced back to the study of rare, monogenic forms of disease. However, in the past decade, the availability of Genome-Wide Association Studies (GWAS) has facilitated a shift in focus, toward identifying common risk variants conferring an increased risk of developing PD across the population. A recently developed mitophagy screening assay of GWAS candidates, has functionally implicated the non-specific lethal (NSL) complex, a chromatin remodeler, in the regulation of PINK1-mitophagy. Here, a bioinformatics approach has been taken to investigate the interactome of the NSL complex, to unpick its relevance to PD progression. The mitochondrial interactome of the NSL complex has been built, mining 3 separate repositories: PINOT, HIPPIE and MIST, for curated, literature-derived protein-protein interaction (PPI) data. A multi-layered approach has been taken to; i) build the ‘mitochondrial’ NSL interactome, applying PD gene-set enrichment analysis to explore the relevance of the NSL mitochondrial interactome to PD and, ii) build the PD-oriented NSL interactome, using functional enrichment, to uncover biological pathways underpinning the NSL /PD association.
DOI: 10.5281/zenodo.7516686
title: Protein network analysis links the NSL complex to Parkinson's disease and mitochondrial biology

description: This online depository corresponds to manuscript : <em>Protein network analysis links the NSL complex to Parkinson’s disease and mitochondrial biology. </em> <strong>Authors: </strong><em>Katie Kelly, Patrick A. Lewis, Helene Plun-Favreau, Claudia Manzoni</em> Whilst the majority (~90-95%) of PD cases are sporadic, much of our understanding of the pathophysiological basis of disease can be traced back to the study of rare, monogenic forms of disease. However, in the past decade, the availability of Genome-Wide Association Studies (GWAS) has facilitated a shift in focus, toward identifying common risk variants conferring an increased risk of developing PD across the population. A recently developed mitophagy screening assay of GWAS candidates, has functionally implicated the non-specific lethal (NSL) complex, a chromatin remodeler, in the regulation of PINK1-mitophagy. Here, a bioinformatics approach has been taken to investigate the interactome of the NSL complex, to unpick its relevance to PD progression. The mitochondrial interactome of the NSL complex has been built, mining 3 separate repositories: PINOT, HIPPIE and MIST, for curated, literature-derived protein-protein interaction (PPI) data. A multi-layered approach has been taken to; i) build the ‘mitochondrial’ NSL interactome, applying PD gene-set enrichment analysis to explore the relevance of the NSL mitochondrial interactome to PD and, ii) build the PD-oriented NSL interactome, using functional enrichment, to uncover biological pathways underpinning the NSL /PD association.
DOI: 10.5281/zenodo.7868483
title: Protein network analysis links the NSL complex to Parkinson's disease via mitochondrial & nuclear biology

description: This online depository corresponds to manuscript : <em>Protein network analysis links the NSL complex to Parkinson’s disease via mitochondrial & nuclear biology.</em> <strong>Authors: </strong><em>Katie Kelly, Patrick A. Lewis, Helene Plun-Favreau, Claudia Manzoni</em> Whilst the majority (~90-95%) of PD cases are sporadic, much of our understanding of the pathophysiological basis of disease can be traced back to the study of rare, monogenic forms of disease. However, in the past decade, the availability of Genome-Wide Association Studies (GWAS) has facilitated a shift in focus, toward identifying common risk variants conferring an increased risk of developing PD across the population. A recently developed mitophagy screening assay of GWAS candidates, has functionally implicated the non-specific lethal (NSL) complex, a chromatin remodeler, in the regulation of PINK1-mitophagy. Here, a bioinformatics approach has been taken to investigate the interactome of the NSL complex, to unpick its relevance to PD progression. The mitochondrial interactome of the NSL complex has been built, mining 3 separate repositories: PINOT, HIPPIE and MIST, for curated, literature-derived protein-protein interaction (PPI) data. A multi-layered approach has been taken to; i) build the ‘mitochondrial’ NSL interactome, applying PD gene-set enrichment analysis to explore the relevance of the NSL mitochondrial interactome to PD and, ii) build the PD-oriented NSL interactome, using functional enrichment, to uncover biological pathways underpinning the NSL /PD association.
DOI: 10.5281/zenodo.7849102
title: FEP benchmark TNKS2 FEP+ fmp and trajectory files

description: Full FEP+ fmp file and related trajectory files for TNKS2 FEP+ calculations related to dataset https://github.com/MCompChem/fep-benchmark
DOI: 10.5281/zenodo.7849101
title: FEP benchmark TNKS2 FEP+ fmp and trajectory files

description: Full FEP+ fmp file and related trajectory files for TNKS2 FEP+ calculations related to dataset https://github.com/MCompChem/fep-benchmark
DOI: 10.34620/dadosipb/wasooa
title: Systematic Review and Meta-Analysis on Risk Factors for Sporadic Food-Borne Illnesses. Part II: Campylobacteriosis

description: Report on risk factors for sporadic campylobacteriosis prepared on request from the French Agency for Food, Environmental and Occupational Health and Safety (ANSES), as deliverable of the services contract "Systematic Review and Meta-Analysis on Risk Factors for Sporadic Food-Borne Illnesses".
DOI: 10.5281/zenodo.7872660
title: Data from Real-time, model-based magnetic field correction for moving, wearable MEG

description: OP-MEG data used to generate figures from manuscript titled "Real-time, model-based magnetic field correction for moving, wearable MEG". Each zipped folder relates to a different experiment: EnvironmentalNoise relates to the environmental noise experiments, AEF is the auditory evoked response experiment and ExternalCoils relates to the recordings using a set of external coils to produce interference presented in the supplementary material of the paper.
DOI: 10.5281/zenodo.7872659
title: Data from Real-time, model-based magnetic field correction for moving, wearable MEG

description: OP-MEG data used to generate figures from manuscript titled "Real-time, model-based magnetic field correction for moving, wearable MEG". Each zipped folder relates to a different experiment: EnvironmentalNoise relates to the environmental noise experiments, AEF is the auditory evoked response experiment and ExternalCoils relates to the recordings using a set of external coils to produce interference presented in the supplementary material of the paper.
DOI: 10.6084/m9.figshare.22678759.v1
title: Priming effect stimulates carbon release from thawed permafrost

description: This dataset is composed of soil organic carbon density and basal microbial respiration, cumulative priming effect and relative priming effect in thawed permafrost soils from 24 sites on the Tibetan Plateau.
DOI: 10.17608/k6.auckland.22508050
title: Matlab scripts

description: Dataset and Matlab code to accompany the following manuscript: Wilson, L. Constantine, R. Pine, M.K., Farcas, A. Radford, C.A. 2022. Small boat sound diminishes the listening spaces of fishes and crustaceans. <br> Please note that the functions find_closest4_fast.m, linterp.m, linterp2d.m, and extract_rec_value_update.m were provided by Charlotte Findlay and Adrian Farcas. Charlotte Findlay can be contacted at charlotte_findlay@hotmail.co.uk or charlotte.findlay@bio.au.dk. <br> The following required functions are available from the Matlab file exchange: Jonathan Sullivan (2023). Automatic Map Scale Generation (https://www.mathworks.com/matlabcentral/fileexchange/33545-automatic-map-scale-generation), MATLAB Central File Exchange. Retrieved April 28, 2023. <br> Rafael Palacios (2023). deg2utm (https://www.mathworks.com/matlabcentral/fileexchange/10915-deg2utm), MATLAB Central File Exchange. Retrieved April 28, 2023. <br> Rafael Palacios (2023). utm2deg (https://www.mathworks.com/matlabcentral/fileexchange/10914-utm2deg), MATLAB Central File Exchange. Retrieved April 28, 2023. <br> The function PG_DFT.m accompanies the folllowing manuscript: Merchant, N. D., Fristrup, K. M., Johnson, M. P., Tyack, P. L., Witt, M. J., Blondel, P., & Parks, S. E. (2015). Measuring acoustic habitats. <em>Methods in Ecology and Evolution</em>, <em>6</em>, 257–265. <strong>https://doi.org/10.1111/2041-210X.12330</strong> <br>
DOI: 10.17608/k6.auckland.22508050.v3
title: Matlab scripts

description: Dataset and Matlab code to accompany the following manuscript: Wilson, L. Constantine, R. Pine, M.K., Farcas, A. Radford, C.A. 2022. Small boat sound diminishes the listening spaces of fishes and crustaceans. <br> Please note that the functions find_closest4_fast.m, linterp.m, linterp2d.m, and extract_rec_value_update.m were provided by Charlotte Findlay and Adrian Farcas. Charlotte Findlay can be contacted at charlotte_findlay@hotmail.co.uk or charlotte.findlay@bio.au.dk. <br> The following required functions are available from the Matlab file exchange: Jonathan Sullivan (2023). Automatic Map Scale Generation (https://www.mathworks.com/matlabcentral/fileexchange/33545-automatic-map-scale-generation), MATLAB Central File Exchange. Retrieved April 28, 2023. <br> Rafael Palacios (2023). deg2utm (https://www.mathworks.com/matlabcentral/fileexchange/10915-deg2utm), MATLAB Central File Exchange. Retrieved April 28, 2023. <br> Rafael Palacios (2023). utm2deg (https://www.mathworks.com/matlabcentral/fileexchange/10914-utm2deg), MATLAB Central File Exchange. Retrieved April 28, 2023. <br> The function PG_DFT.m accompanies the folllowing manuscript: Merchant, N. D., Fristrup, K. M., Johnson, M. P., Tyack, P. L., Witt, M. J., Blondel, P., & Parks, S. E. (2015). Measuring acoustic habitats. <em>Methods in Ecology and Evolution</em>, <em>6</em>, 257–265. <strong>https://doi.org/10.1111/2041-210X.12330</strong> <br>
DOI: 10.17608/k6.auckland.22508050.v2
title: Matlab scripts

description: Dataset and Matlab code to accompany the following manuscript: Wilson, L. Constantine, R. Pine, M.K., Farcas, A. Radford, C.A. 2022. Small boat sound diminishes the listening spaces of fishes and crustaceans.
DOI: 10.6084/m9.figshare.22678759.v4
title: Priming effect stimulates carbon release from thawed permafrost

description: This dataset is composed of two datasets, Dataset1 includes geographic location, soil layer thickness, soil organic carbon, bulk density, soil organic carbon density, basal mirobial respiration, cumulative priming effect and relative priming effect across 24 sampling sites on the Tibetan Plateau. Dataset2 includes CO2 emission rate and δ13C values across 24 sampling sites during the 60-day incubation. At each site, both CO2 emission rate and δ13C values of three parallel samples were measured for 9 times on days 1, 2, 4, 9, 15, 22, 30, 45, and 60.
DOI: 10.5281/zenodo.6374012
title: Zegami user manual for data exploration: "Systematic analysis of YFP gene traps reveals common discordance between mRNA and protein across the nervous system"

description: The explosion in biological data generation challenges the available technologies and methodologies for data interrogation. Moreover, highly rich and complex datasets together with diverse linked data are difficult to explore when provided in flat files. Here we provide a way to filter and analyse in a systematic way a dataset with more than 18 thousand data points using Zegami, a solution for interactive data visualisation and exploration. The primary data we use are derived from a systematic analysis of 200 YFP gene traps reveals common discordance between mRNA and protein across the nervous system which is submitted elsewhere. This manual provides the raw image data together with annotations and associated data and explains how to use Zegami for exploring all these data types together by providing specific examples. We also provide the open source python code used to annotate the figures.
DOI: 10.5281/zenodo.6379926
title: Zegami user manual for data exploration: "Systematic analysis of YFP gene traps reveals common discordance between mRNA and protein across the nervous system"

description: The explosion in biological data generation challenges the available technologies and methodologies for data interrogation. Moreover, highly rich and complex datasets together with diverse linked data are difficult to explore when provided in flat files. Here we provide a way to filter and analyse in a systematic way a dataset with more than 18 thousand data points using Zegami (link), a solution for interactive data visualisation and exploration. The primary data we use are derived from a systematic analysis of 200 YFP gene traps reveals common discordance between mRNA and protein across the nervous system which is submitted elsewhere. This manual provides the raw image data together with annotations and associated data and explains how to use Zegami for exploring all these data types together by providing specific examples. We also provide the open source python code (github link) used to annotate the figures.
DOI: 10.5281/zenodo.6374011
title: Multi-Dimensional Data Viewer (MDV) user manual for data exploration: "Systematic analysis of YFP traps reveals common discordance between mRNA and protein across the nervous system"

description: Please also see the latest version of the repository:<br> https://doi.org/10.5281/zenodo.6374011 and<br> our website: https://ilandavis.com/jcb2023-yfp The explosion in the volume of biological imaging data challenges the available technologies for data interrogation and its intersection with related published bioinformatics data sets. Moreover, intersection of highly rich and complex datasets from different sources provided as flat csv files requires advanced informatics skills, which is time consuming and not accessible to all. Here, we provide a “user manual” to our new paradigm for systematically filtering and analysing a dataset with more than 1300 microscopy data figures using Multi-Dimensional Viewer (MDV) -link, a solution for interactive multimodal data visualisation and exploration. The primary data we use are derived from our published systematic analysis of 200 YFP traps reveals common discordance between mRNA and protein across the nervous system (eprint link). This manual provides the raw image data together with the expert annotations of the mRNA and protein distribution as well as associated bioinformatics data. We provide an explanation, with specific examples, of how to use MDV to make the multiple data types interoperable and explore them together. We also provide the open-source python code (github link) used to annotate the figures, which could be adapted to any other kind of data annotation task.
description: {"references": ["Titlow, Joshua S, Maria Kiourlappou, Ana Palanca, Jeffrey Y Lee, Dalia S Gala, Darragh Ennis, Joyce J S Yu, et al. 'Systematic Analysis of YFP Gene Traps Reveals Common Discordance between MRNA and Protein across the Nervous System'. Preprint \u2013 Accepted for publication at JCB, https://doi.org/10.1083/jcb.202205129."]}
DOI: 10.5281/zenodo.7308444
title: Zegami user manual for data exploration: "Systematic analysis of YFP gene traps reveals common discordance between mRNA and protein across the nervous system"

description: The explosion in biological data generation challenges the available technologies and methodologies for data interrogation. Moreover, highly rich and complex datasets together with diverse linked data are difficult to explore when provided in flat files. Here we provide a way to filter and analyse in a systematic way a dataset with more than 18 thousand data points using Zegami (link), a solution for interactive data visualisation and exploration. The primary data we use are derived from a systematic analysis of 200 YFP gene traps reveals common discordance between mRNA and protein across the nervous system which is submitted elsewhere. This manual provides the raw image data together with annotations and associated data and explains how to use Zegami for exploring all these data types together by providing specific examples. We also provide the open source python code (github link) used to annotate the figures.
DOI: 10.5281/zenodo.7738944
title: Multi-Dimensional Data Viewer (MDV) user manual for data exploration: "Systematic analysis of YFP gene traps reveals common discordance between mRNA and protein across the nervous system"

description: The explosion in the volume of biological imaging data challenges the available technologies for data interrogation and its intersection with related published bioinformatics data sets. Moreover, intersection of highly rich and complex datasets from different sources provided as flat csv files requires advanced informatics skills, which is time consuming and not accessible to all. Here, we provide a “user manual” to our new paradigm for systematically filtering and analysing a dataset with more than 1300 microscopy data figures using Multi-Dimensional Viewer (MDV: https://mdv.molbiol.ox.ac.uk), a solution for interactive multimodal data visualisation and exploration. The primary data we use are derived from our published systematic analysis of 200 YFP gene traps reveals common discordance between mRNA and protein across the nervous system (https://doi.org/10.1083/jcb.202205129). This manual provides the raw image data together with the expert annotations of the mRNA and protein distribution as well as associated bioinformatics data. We provide an explanation, with specific examples, of how to use MDV to make the multiple data types interoperable and explore them together. We also provide the open-source python code (github link) used to annotate the figures, which could be adapted to any other kind of data annotation task.
description: {"references": ["Titlow, Joshua S, Maria Kiourlappou, Ana Palanca, Jeffrey Y Lee, Dalia S Gala, Darragh Ennis, Joyce J S Yu, et al. 'Systematic Analysis of YFP Gene Traps Reveals Common Discordance between MRNA and Protein across the Nervous System'. Preprint \u2013 Accepted for publication at JCB, https://doi.org/10.1083/jcb.202205129."]}
DOI: 10.5281/zenodo.7875495
title: Multi-Dimensional Data Viewer (MDV) user manual for data exploration: "Systematic analysis of YFP traps reveals common discordance between mRNA and protein across the nervous system"

description: Please also see the latest version of the repository:<br> https://doi.org/10.5281/zenodo.6374011 and<br> our website: https://ilandavis.com/jcb2023-yfp The explosion in the volume of biological imaging data challenges the available technologies for data interrogation and its intersection with related published bioinformatics data sets. Moreover, intersection of highly rich and complex datasets from different sources provided as flat csv files requires advanced informatics skills, which is time consuming and not accessible to all. Here, we provide a “user manual” to our new paradigm for systematically filtering and analysing a dataset with more than 1300 microscopy data figures using Multi-Dimensional Viewer (MDV) -link, a solution for interactive multimodal data visualisation and exploration. The primary data we use are derived from our published systematic analysis of 200 YFP traps reveals common discordance between mRNA and protein across the nervous system (eprint link). This manual provides the raw image data together with the expert annotations of the mRNA and protein distribution as well as associated bioinformatics data. We provide an explanation, with specific examples, of how to use MDV to make the multiple data types interoperable and explore them together. We also provide the open-source python code (github link) used to annotate the figures, which could be adapted to any other kind of data annotation task.
description: {"references": ["Titlow, Joshua S, Maria Kiourlappou, Ana Palanca, Jeffrey Y Lee, Dalia S Gala, Darragh Ennis, Joyce J S Yu, et al. 'Systematic Analysis of YFP Gene Traps Reveals Common Discordance between MRNA and Protein across the Nervous System'. Preprint \u2013 Accepted for publication at JCB, https://doi.org/10.1083/jcb.202205129."]}
DOI: 10.5281/zenodo.7875469
title: Pushing the boundaries of VCD spectroscopy in natural product chemistry

description: Database of VCD and IR spectra for a set of monoterpenes, experimental mixtures and essential oils used in the article `Pushing the boundaries of VCD spectroscopy in natural product chemistry'.
DOI: 10.5281/zenodo.7875468
title: Pushing the boundaries of VCD spectroscopy in natural product chemistry

description: Database of VCD and IR spectra for a set of monoterpenes, experimental mixtures and essential oils used in the article `Pushing the boundaries of VCD spectroscopy in natural product chemistry'.
DOI: 10.5281/zenodo.7870976
title: CHclim25 - relative sunshine duration (Srel)

description: Relative sunshine duration are derived from daily 1 km resolution gridded dataset from the Swiss federal agency for meteorology and climate (MeteoSwiss) using a bilinear downscaling to 25 m resolution. Monthly and yearly individual year and current average (1981-2010) layers can be downloaded from separate zip files. No Future layers could be calculated since no scenario is available from the CH2018 initiative. The layer files are stored in compressed GeoTIFF format with the “deflate” algorithm with option “predictor2” from the GDAL. This format has a high compression ratio but allows direct import in most GIS softwares. All the maps are projected in the Swiss coordinate system CH 1903+ LV95 (epsg:2056) with a resolution of 25x25m using the extent of the digital height model DHM25 of the Swiss office for topography (swisstopo).
DOI: 10.5281/zenodo.7870977
title: CHclim25 - relative sunshine duration (Srel)

description: Relative sunshine duration are derived from daily 1 km resolution gridded dataset from the Swiss federal agency for meteorology and climate (MeteoSwiss) using a bilinear downscaling to 25 m resolution. Monthly and yearly individual year and current average (1981-2010) layers can be downloaded from separate zip files. No Future layers could be calculated since no scenario is available from the CH2018 initiative. The layer files are stored in compressed GeoTIFF format with the “deflate” algorithm with option “predictor2” from the GDAL. This format has a high compression ratio but allows direct import in most GIS softwares. All the maps are projected in the Swiss coordinate system CH 1903+ LV95 (epsg:2056) with a resolution of 25x25m using the extent of the digital height model DHM25 of the Swiss office for topography (swisstopo).
DOI: 10.6084/m9.figshare.20137811
title: Raw data

description: The data included here are the raw respirometry data, body mass data, gill surface area data (post-processing of gill surface microscopy images), ventricle mass data, hematocrit data, and enzyme activity data (post collection from spectrophotometry assays) analyzed in the manuscript "<strong>Gill surface area allometry does not constrain the body mass scaling of maximum oxygen uptake rate in the tidepool sculpin, </strong><em><strong>Oligocottus maculosus</strong></em>"