cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.6953183
title: Availability of electricity, its sources and related management plans in Brazilian Amazon’s extractive communities

description: This dataset consists of a situational description of Brazilian Amazon's extractive communities regarding availability of energy sources. Data are reported for 50 protected 'extractive reserve' areas, their geographical extents, management plans, population details and sources of/access to energy.<br>The related study examines the access to energy sources for populations within extractive communities in the Brazilian Amazon and reports on the related presence or absence of management plan documentation. Management plans published by the relevant Federal or State authority are used to generate data on mode of access for communities, energy supply level and energy source. Where management plans are not available these values are not present.<br>The dataset consists of two files holding the raw extracted data and a detailed description of the methodology used to generate the data. These are in .xlsx Excel spreadsheet and .docx Word document formats, accessible via MS Office or open office applications.<br><b>SITUATIONAL_ENERGY_PANORAMA_OF_AMAZONS_EXTRACTIVE_RESERVES.xlsx - </b>each row of the data table represents a named extractive reserve area, with the following variables reported for each: <b>Municipality</b><b>State: </b>abbreviated two-letter code for Brazilian State<b>Date of Creation:</b> of the protected area itself<b>Management Plan: </b>the existence of a management plan covering this area - 'Yes' or 'No'<br><b>Administrative Sphere:</b> State or Federal<b>Area in Hectares: </b>territorial extent of the extractive reserve <br><b>Beneficiary Families:</b> living within the protected area<b>Average No. of Hectares by Family:</b> calculated as mean<b>Access: </b>mode of access available to communities - 'fluvial', 'aerial' or 'terrestrial' (multivariate)<b>Energy Supply</b>: single value describing level of supply: 'deficient', 'nonexistent' or 'no information'<b>Energy Source:</b> relevant source accessible to the community - 'fossil', 'mainly fossil', 'public network', 'nonexistent' or 'no information' (multivariate)<br><b>Methodology_Description.docx - </b>a detailed descriptive document outlining the data collection process from two official digital databases of the Brazilian government and one digital repository of a public interest civil society organization: (i) National Registry of Conservation Units of the Brazilian Ministry of the Environment; (ii) Dynamic Panel and Management Plans of the Chico Mendes Institute for Biodiversity Conservation (ICMBio), an independent agency responsible for managing protected areas in Brazil at the federal level; and (iii) Social and Environmental Institute (ISA) respectively.<br><br>See the references linked below for these data sources.
DOI: 10.6084/m9.figshare.6953183.v1
title: Availability of electricity, its sources and related management plans in Brazilian Amazon’s extractive communities

description: This dataset consists of a situational description of Brazilian Amazon's extractive communities regarding availability of energy sources. Data are reported for 50 protected 'extractive reserve' areas, their geographical extents, management plans, population details and sources of/access to energy.<br>The related study examines the access to energy sources for populations within extractive communities in the Brazilian Amazon and reports on the related presence or absence of management plan documentation. Management plans published by the relevant Federal or State authority are used to generate data on mode of access for communities, energy supply level and energy source. Where management plans are not available these values are not present.<br>The dataset consists of two files holding the raw extracted data and a detailed description of the methodology used to generate the data. These are in .xlsx Excel spreadsheet and .docx Word document formats, accessible via MS Office or open office applications.<br><b>SITUATIONAL_ENERGY_PANORAMA_OF_AMAZONS_EXTRACTIVE_RESERVES.xlsx - </b>each row of the data table represents a named extractive reserve area, with the following variables reported for each: <b>Municipality</b><b>State: </b>abbreviated two-letter code for Brazilian State<b>Date of Creation:</b> of the protected area itself<b>Management Plan: </b>the existence of a management plan covering this area - 'Yes' or 'No'<br><b>Administrative Sphere:</b> State or Federal<b>Area in Hectares: </b>territorial extent of the extractive reserve <br><b>Beneficiary Families:</b> living within the protected area<b>Average No. of Hectares by Family:</b> calculated as mean<b>Access: </b>mode of access available to communities - 'fluvial', 'aerial' or 'terrestrial' (multivariate)<b>Energy Supply</b>: single value describing level of supply: 'deficient', 'nonexistent' or 'no information'<b>Energy Source:</b> relevant source accessible to the community - 'fossil', 'mainly fossil', 'public network', 'nonexistent' or 'no information' (multivariate)<br><b>Methodology_Description.docx - </b>a detailed descriptive document outlining the data collection process from two official digital databases of the Brazilian government and one digital repository of a public interest civil society organization: (i) National Registry of Conservation Units of the Brazilian Ministry of the Environment; (ii) Dynamic Panel and Management Plans of the Chico Mendes Institute for Biodiversity Conservation (ICMBio), an independent agency responsible for managing protected areas in Brazil at the federal level; and (iii) Social and Environmental Institute (ISA) respectively.<br><br>See the references linked below for these data sources.
DOI: 10.6084/m9.figshare.6953144
title: Metabolite BridgeDb ID Mapping Database (20180809)

description: BridgeDb ID mapping database for metabolites, using HMDB 4.0 (Release of 08 July 2018), ChEBI 166 (Release of 01 August 2018), and Wikidata (09 august 2018) as data sources.<br>Several updated mappings, with almost 500 added mapping for Wikidata!<br><i>This work was funded by ELIXIR, the research infrastructure for life-science data.</i><br>
DOI: 10.6084/m9.figshare.6953135
title: Worse than useless: traditional ERP baseline correction reduces power through self-contradiction

description: Poster presented at SNL 2018.<br>Baseline correction plays an important role in past and current methodological debates in ERP research (e.g. the Tanner v. Maess debate in Journal of Neuroscience Methods), serving as a potential alternative to strong high-pass filtering. However, the very assumptions that underlie traditional baseline also undermine it, making it statistically unnecessary and undesirable. In particular, the assumption that the electrophysiological activity of the baseline interval does not differ systematically between conditions implies by definition that the baseline interval is essentially a by-channel noisy reference. The noise from the baseline interval is then projected into the target interval, thereby reducing power. Moreover, as a reference, the baseline interval can bias topographies, especially if the no-systematic-difference assumption is violated. This reference nonetheless serves to address non-experimental recording factors (electrode drift, differences in environmental electrical noise), but there are better methods for controlling for theses environmental issues. Instead of assuming a fixed baseline correction, whether trial-by-trial or at the level of single-subject averages, we can instead include the baseline interval as a statistical predictor, similar to other GLM-based deconvolution approaches (e.g. removal of eye-artifacts, Dimigen et al. 2011; rERP, Smith & Kutas 2014). The baseline interval can then interact with, i.e. allow its influence to be weighted by topographical and experimental factors. This controls for topographical biases, addresses electrode drift in block designs, does not require the no-systematic-difference assumption, and allows the data to determine how much baseline correction is actually needed. Additionally, both full traditional baseline correction and no baseline correction are included as special cases. The lack of the no-systematic-difference assumption also allows for this method to be applied more naturalistic settings, which have recently begun to gain ground in M/EEG research (cf. Alday et al. 2017, Broderick et al. 2017, Brodbeck et al. 2018). In addition to this theoretical argument, we show the effectiveness of this method by reanalysis of previous ERP studies on language. We find that the empirically determined baseline correction is often much less than the traditional correction. Using semi-parametric simulations from mixed-effects models fit to these data, we further show that the trade-off in power between additional model complexity and the noisiness of the dependent variable is worth the improved fit to the data.
DOI: 10.6084/m9.figshare.6953144.v1
title: Metabolite BridgeDb ID Mapping Database (20180809)

description: BridgeDb ID mapping database for metabolites, using HMDB 4.0 (Release of 08 July 2018), ChEBI 166 (Release of 01 August 2018), and Wikidata (09 august 2018) as data sources.<br>Several updated mappings, with almost 500 added mapping for Wikidata!<br><i>This work was funded by ELIXIR, the research infrastructure for life-science data.</i><br>
DOI: 10.6084/m9.figshare.6953126
title: Northern elephant seal blubber transcriptome assembly (repeated ACTH challenge)

description: Juvenile northern elephant seals received once-daily injections of synthetic adrenocorticotropic hormone (ACTH) for four consecutive days. Blubber samples were collected from study animals immediately prior to and four hours after the first and fourth ACTH administrations. RNA was isolated from the inner, metabolically active half (closest to muscle) of each blubber sample from four study animals. cDNA libraries were sequenced as 100-bp paired end reads on the Illumina HiSeq 4000 (mean 27 million reads/sample) at UC Berkeley. <br> A single reference transcriptome was assembled de novo using Trinity v2.4.0 with default settings (file "Trinity.CSM.all.fasta") and XSEDE computational resources (PSC Bridges Large). The assembly contains 1,216,779 contigs, of which 370,178 were annotated by blastx using the UniProt database (e-value threshold 0.001; file "Trinity.CSM.all.annotation.m8"). 92% of sequencing reads could be mapped back to the assembly and it contains 85.8% complete vertebrate BUSCOs. Mean contig length was 670 bp.<br>The raw assembly was filtered using TransDecoder; the filtered assembly contains 266,916 in silico predicted proteins (file "Trinity.CSM.all.fasta.transdecoder.pep").
DOI: 10.6084/m9.figshare.6953135.v1
title: Worse than useless: traditional ERP baseline correction reduces power through self-contradiction

description: Poster presented at SNL 2018.<br>Baseline correction plays an important role in past and current methodological debates in ERP research (e.g. the Tanner v. Maess debate in Journal of Neuroscience Methods), serving as a potential alternative to strong high-pass filtering. However, the very assumptions that underlie traditional baseline also undermine it, making it statistically unnecessary and undesirable. In particular, the assumption that the electrophysiological activity of the baseline interval does not differ systematically between conditions implies by definition that the baseline interval is essentially a by-channel noisy reference. The noise from the baseline interval is then projected into the target interval, thereby reducing power. Moreover, as a reference, the baseline interval can bias topographies, especially if the no-systematic-difference assumption is violated. This reference nonetheless serves to address non-experimental recording factors (electrode drift, differences in environmental electrical noise), but there are better methods for controlling for theses environmental issues. Instead of assuming a fixed baseline correction, whether trial-by-trial or at the level of single-subject averages, we can instead include the baseline interval as a statistical predictor, similar to other GLM-based deconvolution approaches (e.g. removal of eye-artifacts, Dimigen et al. 2011; rERP, Smith & Kutas 2014). The baseline interval can then interact with, i.e. allow its influence to be weighted by topographical and experimental factors. This controls for topographical biases, addresses electrode drift in block designs, does not require the no-systematic-difference assumption, and allows the data to determine how much baseline correction is actually needed. Additionally, both full traditional baseline correction and no baseline correction are included as special cases. The lack of the no-systematic-difference assumption also allows for this method to be applied more naturalistic settings, which have recently begun to gain ground in M/EEG research (cf. Alday et al. 2017, Broderick et al. 2017, Brodbeck et al. 2018). In addition to this theoretical argument, we show the effectiveness of this method by reanalysis of previous ERP studies on language. We find that the empirically determined baseline correction is often much less than the traditional correction. Using semi-parametric simulations from mixed-effects models fit to these data, we further show that the trade-off in power between additional model complexity and the noisiness of the dependent variable is worth the improved fit to the data.
DOI: 10.6084/m9.figshare.6953123
title: Supplemental File 3 - hrpT3SS effector database with additional genes

description: Manually curated T3SS database, originally downloaded from http://www.pseudomonas-syringae.org/
DOI: 10.6084/m9.figshare.6953126.v1
title: Northern elephant seal blubber transcriptome assembly (repeated ACTH challenge)

description: Juvenile northern elephant seals received once-daily injections of synthetic adrenocorticotropic hormone (ACTH) for four consecutive days. Blubber samples were collected from study animals immediately prior to and four hours after the first and fourth ACTH administrations. RNA was isolated from the inner, metabolically active half (closest to muscle) of each blubber sample from four study animals. cDNA libraries were sequenced as 100-bp paired end reads on the Illumina HiSeq 4000 (mean 27 million reads/sample) at UC Berkeley. <br> A single reference transcriptome was assembled de novo using Trinity v2.4.0 with default settings (file "Trinity.CSM.all.fasta") and XSEDE computational resources (PSC Bridges Large). The assembly contains 1,216,779 contigs, of which 370,178 were annotated by blastx using the UniProt database (e-value threshold 0.001; file "Trinity.CSM.all.annotation.m8"). 92% of sequencing reads could be mapped back to the assembly and it contains 85.8% complete vertebrate BUSCOs. Mean contig length was 670 bp.<br>The raw assembly was filtered using TransDecoder; the filtered assembly contains 266,916 in silico predicted proteins (file "Trinity.CSM.all.fasta.transdecoder.pep").
DOI: 10.6084/m9.figshare.6953123.v1
title: Supplemental File 3 - hrpT3SS effector database with additional genes

description: Manually curated T3SS database, originally downloaded from http://www.pseudomonas-syringae.org/
DOI: 10.6084/m9.figshare.6953117
title: Supplemental File 1 - Figure 2a core genome alignment

description: This is the core genome alignment visualized in SplitsTree to construct Figure 2a
DOI: 10.6084/m9.figshare.6953114
title: Supplemental Table 2 - COG results

description: COG categories of core and rare genes in the E. tracheiphila rare and core pangenome
DOI: 10.6084/m9.figshare.6953114.v1
title: Supplemental Table 2 - COG results

description: COG categories of core and rare genes in the E. tracheiphila rare and core pangenome
DOI: 10.6084/m9.figshare.6951608
title: Improving the semantic quality of conceptual models through text mining. A proof of concept

description: Python code generated in the context of the dissertation 'Improving the semantic quality of conceptual models through text mining. A proof of concept' (Postgraduate studies Big Data & Analytics for Business and Management, KU Leuven Faculty of Economics and Business, 2018)
DOI: 10.6084/m9.figshare.6951608.v1
title: Improving the semantic quality of conceptual models through text mining. A proof of concept

description: Python code generated in the context of the dissertation 'Improving the semantic quality of conceptual models through text mining. A proof of concept' (Postgraduate studies Big Data & Analytics for Business and Management, KU Leuven Faculty of Economics and Business, 2018)
DOI: 10.6084/m9.figshare.6950144
title: Robust experimental data

description: Experimental data (running time and evaluations) ofmultiple robust shortest path algorithms.<br>
DOI: 10.6084/m9.figshare.6950144.v1
title: Robust experimental data

description: Experimental data (running time and evaluations) ofmultiple robust shortest path algorithms.<br>
DOI: 10.6084/m9.figshare.6950126
title: Robust network

description: A robust network based on OSM data, “(c) OpenStreetMap contributors”, see<br>https://www.openstreetmap.org/copyright<br><br>Intended to conduct experiments with robust shortest path algorithms.<br>Syntax is as follows:<br> "num_vertices" "num_arcs""vertex_1_id" "vertex_1_x" "vertex_1_y""vertex_2_id" "vertex_2_x" "vertex_2_y"..."arc_1_id" "arc_1_source" "arc_1_target" "arc_1_cost" "arc_1_deviation""arc_2_id" "arc_2_source" "arc_2_target" "arc_2_cost" "arc_2_deviation"...<br><br>
DOI: 10.6084/m9.figshare.6950141
title: Earth Analytics Bootcamp | Homework 2 Teaching Subset

description: This is a teaching data subset that contains data for monthly snowfall (inches) for Boulder, CO. Data included: <br>1. Monthly snowfall (inches) between 2007 and 2017 (snow-2007-to-2017.csv). File is organized with 11 rows/records (one for each year between 2007 and 2017) and 12 columns (one for each month from January through December). <br> <br>2. Monthly snowfall (inches) between 2007 and 2017 with month and season names (snow-2007-to-2017-months-seasons.csv). File contains headers for rows/columns as needed.<br><br> Source: NOAA.<br>
DOI: 10.6084/m9.figshare.6950126.v1
title: Robust network

description: A robust network based on OSM data, “(c) OpenStreetMap contributors”, see<br>https://www.openstreetmap.org/copyright<br><br>Intended to conduct experiments with robust shortest path algorithms.<br>Syntax is as follows:<br> "num_vertices" "num_arcs""vertex_1_id" "vertex_1_x" "vertex_1_y""vertex_2_id" "vertex_2_x" "vertex_2_y"..."arc_1_id" "arc_1_source" "arc_1_target" "arc_1_cost" "arc_1_deviation""arc_2_id" "arc_2_source" "arc_2_target" "arc_2_cost" "arc_2_deviation"...<br><br>
DOI: 10.6084/m9.figshare.6950141.v1
title: Earth Analytics Bootcamp | Homework 2 Teaching Subset

description: This is a teaching data subset that contains data for monthly snowfall (inches) for Boulder, CO. Data included: <br>1. Monthly snowfall (inches) between 2007 and 2017 (snow-2007-to-2017.csv). File is organized with 11 rows/records (one for each year between 2007 and 2017) and 12 columns (one for each month from January through December). <br> <br>2. Monthly snowfall (inches) between 2007 and 2017 with month and season names (snow-2007-to-2017-months-seasons.csv). File contains headers for rows/columns as needed.<br><br> Source: NOAA.<br>
DOI: 10.6084/m9.figshare.6950069
title: WI-Radiograms-for-3D

description: This stack of 2D radiograms was collected at the end of water injection. For reconstruction use the Flat field and Dark current images provided. Image Dimensions: 1024×1024×2001 Image Type: 16 bit - unsigned Mode = cone beam Last angle = 360° Pixel size = 0.2 mm Centre of rotation = 511.718 Source object distance = 37 mm Source detector distance = 549.5 mm Direction of rotation = clockwise Vertical centre = 496 Horizontal centre = 512 Tilt = 0° Skew = 0.5° Voxel size = 0.013239 mm
DOI: 10.6084/m9.figshare.6950069.v1
title: WI-Radiograms-for-3D

description: This stack of 2D radiograms was collected at the end of water injection. For reconstruction use the Flat field and Dark current images provided. Image Dimensions: 1024×1024×2001 Image Type: 16 bit - unsigned Mode = cone beam Last angle = 360° Pixel size = 0.2 mm Centre of rotation = 511.718 Source object distance = 37 mm Source detector distance = 549.5 mm Direction of rotation = clockwise Vertical centre = 496 Horizontal centre = 512 Tilt = 0° Skew = 0.5° Voxel size = 0.013239 mm
DOI: 10.6084/m9.figshare.6950030
title: WI-Reconstructed

description: Reconstruction of the image: WI-Radiograms-for-3D. Reconstructed using Octopus (v8.5) software, filtered back projection method. Use the Flat field and Dark current images provided. Image Dimension: 1024×1024×845 Image Type: 8 bit
DOI: 10.6084/m9.figshare.6950030.v1
title: WI-Reconstructed

description: Reconstruction of the image: WI-Radiograms-for-3D. Reconstructed using Octopus (v8.5) software, filtered back projection method. Use the Flat field and Dark current images provided. Image Dimension: 1024×1024×845 Image Type: 8 bit
DOI: 10.6084/m9.figshare.6949988
title: Earthquake in western Iran: renovation kills

description: Earthquake is the most important cause of death from natural disasters in Iran.
DOI: 10.6084/m9.figshare.6949988.v1
title: Earthquake in western Iran: renovation kills

description: Earthquake is the most important cause of death from natural disasters in Iran.
DOI: 10.6084/m9.figshare.6949907
title: Transgenerational effects of parental light environment on progeny competitive performance and lifetime fitness

description: This file contains all of the raw data for the analyses presented in the article "Transgenerational effects of parental light environment on progeny competitive performance and lifetime fitness" by B. H. Baker, S. E. Sultan, M. Lopez-Ichikawa, and R. Waterman<br>
DOI: 10.6084/m9.figshare.6949907.v1
title: Transgenerational effects of parental light environment on progeny competitive performance and lifetime fitness

description: This file contains all of the raw data for the analyses presented in the article "Transgenerational effects of parental light environment on progeny competitive performance and lifetime fitness" by B. H. Baker, S. E. Sultan, M. Lopez-Ichikawa, and R. Waterman<br>
DOI: 10.6084/m9.figshare.6949634
title: NPI-012-Radiograms

description: This is a series of 2D radiograms which was collected during the injection of a nanoparticle suspension (0.12 wt%). Image Dimensions: 1024×1024×5972 Image Type: 16 bit - unsigned