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701 results for “factor analysis”
Analysis Products: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency
<p>This record contains analysis products for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen <em>et al</em>. Please refer to the READMEs in the directories, which are summarized below.</p> <p>The record contains the following files:<br> <br> `clusters.tsv`: <strong> </strong>contains the cluster id, name and colour of clusters in the paper</p> <p><strong>scATAC.zip</strong></p> <p>Analysis products for the single-cell ATAC-seq data. Contains:</p> <p>- `cells.tsv`: list of barcodes that pass QC. Columns include:<br> - `barcode`<br> - `sample`: (time point)<br> - `umap1`<br> - `umap2`<br> - `cluster`<br> - `dpt_pseudotime_fibr_root`: pseudotime values treating a fibroblast cell as root<br> - `dpt_pseudotime_xOSK_root`: pseudotime values treating xOSK cell as root<br> - `peaks.bed`: list of peaks of 500bp across all cell states. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA.<br> - `features.tsv`: 50 dimensional representation of each cell <br> - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed` </p> <p><strong>scATAC_clusters.zip</strong></p> <p>Analysis products corresponding to cluster pseudo-bulks of the single-cell ATAC-seq data. </p> <p>- `clusters.tsv`: contains the cluster id, name and colour used in the paper<br> - `peaks`: contains `overlap_reproducibilty/overlap.optimal_peak` peaks called using ENCODE bulk ATAC-seq pipeline in the narrowPeak format.<br> - `fragments`: contains per cluster fragment files </p> <p><strong>scATAC_scRNA_integration.zip</strong></p> <p>Analysis products from the integration of scATAC with scRNA. Contains:</p> <p>- `peak_gene_links_fdr1e-4.tsv`: file with peak gene links passing FDR 1e-4. For analyses in the paper, we filter to peaks with absolute correlation >0.45.<br> - `harmony.cca.30.feat.tsv`: 30 dimensional co-embedding for scATAC and scRNA cells obtained by CCA followed by applying Harmony over assay type.<br> - `harmony.cca.metadata.tsv`: UMAP coordinates for scATAC and scRNA cells derived from the Harmony CCA embedding. First column contains barcode.</p> <p><strong>scRNA.zip</strong></p> <p>Analysis products for the single-cell RNA-seq data. Contains:</p> <p>- `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca), knn graphs, all associated metadata. Note that barcode suffix (1-9 corresponds to samples D0, D2, ..., D14, iPSC)<br> - `genes.txt`: list of all genes<br> - `cells.tsv`: list of barcodes that pass QC across samples. Contains:<br> - `barcode_sample`: barcode with index of sample (1-9 corresponding to D0, D2, ..., D14, iPSC) <br> - `sample`: sample name (D0, D2, .., D14, iPSC)<br> - `umap1`<br> - `umap2`<br> - `nCount_RNA`<br> - `nFeature_RNA`<br> - `cluster`<br> - `percent.mt`: percent of mitochondrial transcripts in cell<br> - `percent.oskm`: percent of OSKM transcripts in cell<br> - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt` <br> - `pca.tsv`: first 50 PC of each cell<br> - `oskm_endo_sendai.tsv`: estimated raw counts (cts, may not be integers) and log(1+ tp10k) normalized expression (norm) for endogenous and exogenous (Sendai derived) counts of POU5F1 (OCT4), SOX2, KLF4 and MYC genes. Rows are consistent with `seurat.rds` and `cells.tsv`</p> <p><strong>multiome.zip</strong></p> <p><em>multiome/snATAC:</em></p> <p>These files are derived from the integration of nuclei from multiome (D1M and D2M), with cells from day 2 of scATAC-seq (labeled D2). </p> <p>- `cells.tsv`: This is the list of nuclei barcodes that pass QC from multiome AND also cell barcodes from D2 of scATAC-seq. Includes:<br> - `barcode`<br> - `umap1`: These are the coordinates used for the figures involving multiome in the paper.<br> - `umap2`: ^^^ <br> - `sample`: D1M and D2M correspond to multiome, D2 corresponds to day 2 of scATAC-seq<br> - `cluster`: For multiome barcodes, these are labels transfered from scATAC-seq. For D2 scATAC-seq, it is the original cluster labels. <br> - `peaks.bed`: This is the same file as scATAC/peaks.bed. List of peaks of 500bp. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA.<br> - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed`.<br> - `features.no.harmony.50d.tsv`: 50 dimensional representation of each cell prior to running Harmony (to correct for batch effect between D2 scATAC and D1M,D2M snMultiome). Rows correspond to cells from `cells.tsv`.<br> - `features.harmony.10d.tsv`: 10 dimensional representation of each cell after running Harmony. Rows correspond to cells from `cells.tsv`.</p> <p><em>multiome/snRNA:</em></p> <p>- `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca),associated metadata. Note that barcode suffix (1,2 corresponds to samples D1M, D2M). Please use the UMAP/features from snATAC/ for consistency.<br> - `genes.txt`: list of all genes (this is different from the list in scRNA analysis)<br> - `cells.tsv`: list of barcodes that pass QC across samples. Contains:<br> - `barcode_sample`: barcode with index of sample (1,2 corresponding to D1M, D2M respectively) <br> - `sample`: sample name (D1M, D2M)<br> - `nCount_RNA`<br> - `nFeature_RNA`<br> - `percent.oskm`: percent of OSKM genes in cell<br> - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt` </p>
Geospatial Analysis of Road Conditions and Hazardous Factors in Communities on Continuous vs. Sporadic Permafrost in Greenland
<p>Road conditions and hazardous factors were surveyed in two permafrost-affected communities of West Greenland, Ilulissat (underlain by continuous ice-rich permafrost) and Sisimiut (underlain by sporadic permafrost). Pavement damages, repairs, embankment structural elements, artificial drainage systems, water accumulations and preferential snow ploughing deposits were notably mapped and georeferenced in a geographic information system to form high-resolution spatial databases. In total, respectively 66 and 76 \% of the paved road networks of Ilulissat and Sisimiut were surveyed. Manual in-situ mapping took place in September 2020 and September 2021 in Ilulissat, while Global Navigation Satellite System (GNSS) equipment was used to map road conditions in Sisimiut in September 2020. The severity of the pavement damages was assessed according to the ASTM D 6433–07, Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys, by ASTM International (2008). The drainage conditions were characterized following the recommendations in Cold Regions Pavement Engineering, by Doré, G. and Zubeck, H. K. (2009).</p> <p>This dataset comprises the geospatial layers of the road damage and hazard inventories created for the settlements of Ilulissat and Sisimiut. Each settlement’s inventory is provided in a ZIP-folder, containing the geospatial layers as geopackages and sorted following a thematic structure. Further information about each layer and its attributes can be found in the metadata PDF document.</p>
Plant‐eating carnivores: Multispecies analysis on factors influencing the frequency of plant occurrence in obligate carnivores
Open the record for dataset details and reuse information.
Meta-analysis and critical review of trophic discrimination factors (Δ13C and Δ15N): importance of tissue, trophic level, and diet source
Open the record for dataset details and reuse information.
Data from: A meta-analysis of factors influencing the strength of mate choice copying in animals
Davies et al., (2020) All data and R code <p>Mate-choice copying is a form of social learning in which an individual's choice of mate is influenced by the apparent choices of other individuals of the same sex, and has been observed in more than 20 species across a broad taxonomic range. Though fitness benefits of copying have proven difficult to measure, theory suggests that copying should not be beneficial for all species or contexts. However, the factors influencing the evolution and expression of copying have proven difficult to resolve. We systematically searched the literature for studies of mate-choice copying in non-human animals, and then performed a phylogenetically-controlled meta-analysis to explore which factors influence the expression of copying across species. Across 58 published studies in 23 species, we find strong evidence that animals copy the mate choice of others. The strength of copying was significantly influenced by taxonomic group, however sample size limitations mean it is difficult to draw firm conclusions regarding copying in mammals and arthropods. The strength of copying was also influenced by experimental design: copying was stronger when choosers were tested before and after witnessing a conspecific's mate choice, compared to when choosers with social information were compared to choosers without. Importantly, we did not detect any difference in the strength of copying between males and females, or in relation to the rate of multiple mating. Our search also highlights that more empirical work is needed to investigate copying in a broader range of species, especially those with differing mating systems and levels of reproductive investment.</p>
Data from: Beta diversity patterns of bats in the Atlantic Forest: how does the scale of analysis affect the importance of spatial and environmental factors?
<p>Aim: Environmental and spatial factors are broadly recognized as important predictors of beta diversity patterns. However, the scale at which beta diversity patterns are evaluated will affect the outcoming results. For example, studies at larger scales will usually find spatial processes as the main predictor of beta diversity patterns. In this study we evaluate how beta diversity patterns change when analyses are conducted at different scales by reducing the scale of analysis in a hierarchical manner.</p> <p>Taxon: Chiroptera.</p> <p>Location: Atlantic Forest biome.</p> <p>Methods: Information on the occurrence of 59 bat species were obtained from the Atlantic Bats and Species Link database. We partitioned beta diversity into its two components (nestedness and turnover), and calculated these indexes hierarchically: the biome in its entirety (all ecoregions); between larger regions (north, central and south); and between ecoregions within each region. We performed a Generalized Dissimilarity Model (GDM) to identify and predict the turnover of bat species in the Atlantic Forest based on geo-climatic predictors. We obtained 19 geo-climatic data from AMBDATA, an environmental dataset based on different data sources commonly used in species distribution modeling.</p> <p>Results: We found that turnover was the main component influencing a latitudinal gradient when the biome was analysed in its entirety. However, when the scale of the analysis was reduced, we found that species loss (nestedness component) had a large effect in determining beta diversity dissimilarity. We also found that nestedness was the main pattern explaining beta diversity dissimilarity along a longitudinal gradient.</p> <p>Main conclusions: Beta diversity patterns changed with the scale of analysis, which indicates that bat species composition does not follow the same pattern throughout the Atlantic Forest. This corroborates the importance of analysing beta diversity patterns at different scales in order to understand how environmental dissimilarity across geographic space can influence species distribution patterns.</p>
Influence of polymorphisms in the vascular endothelial growth factor gene on allograft rejection after kidney transplantation: a meta-analysis
<p><span><b><i>Purpose</i></b><i> </i>Reported associations of allograft rejection in kidney transplant patients with <i>VEGF</i> single nucleotide polymorphisms (SNPs) have been inconsistent between studies, which prompted a meta-analysis to obtain more precise estimates.</span></p> <p><span><b><i>Methods</i></b><i> </i>Using the PICO elements, kidney transplant patients (P) were compared by genotype data between rejectors (I) and non-rejectors (C) in order to determine the risk of allograft rejection (O) attributed to the <i>VEGF</i> SNPs. Literature search of four databases yielded seven articles. To calculate risks for allograft rejection, four SNPs were examined. Meta-analysis treatments included outlier and subgroup analyses, the latter was based on ethnicity (Indians/Caucasians) and rejection type (acute/chronic). Multiple comparisons were corrected with the Bonferroni test. </span></p> <p><span><span><b><i>Results</i></b> Five highly significant outcomes (P<sup>a</sup> < 0.01) survived Bonferroni correction, one of which showed reduced risk for the <i>var</i> allele (OR 0.61, 95% CI 0.45-0.82). The remaining four indicated increased risk for the <i>wt</i> allele where the chronic rejection (OR 2.10, 95% CI 1.36-3.24) and Indian (OR 1.44, 95% CI 1.13-1.84) subgroups were accorded susceptibility status. </span></span></p> <p><span><b><i>Conclusions</i></b> Risk associations for renal allograft rejection were increased and reduced on account of the <i>wt</i> and <i>var</i> alleles, respectively. These findings could render the <i>VEGF</i> polymorphisms useful in the clinical genetics of kidney transplantation. </span></p>
Exploratory analysis using machine learning of predictive factors for falls in persons with type 2 diabetes: A Longitudinal Study
<p>The risk of falls in elderly individuals with diabetes was reported to be 1.5 - 3 times higher than in those without diabetes. However, it is not clear what risk factors are strongly related to falls in those with diabetes. In this study, we aimed to investigate the status of falls and to identify important risk factors for falls in persons with type 2 diabetes (T2D) including the non-elderly. Participants were 316 persons with T2D who were admitted to the University of Tsukuba Hospital for treatment of diabetes. They were assessed for medical history, laboratory data and physical capabilities during the hospitalization and were given a questionnaire on falls one year after discharge. Two different statistical models, logistic regression and random forest classifier, were used to investigate important predictors of falls. The response rate to the survey was 72%; of the 226 respondents, there were 129 males and 97 females (median age 62 years). The fall rate during the first year after discharge was 19% and increased with age; fall rates were 17% for those <60 years, 20% for those aged 60 – 69 years and 24% for those ≥70 years. Logistic regression revealed that knee extension strength (β= -0.698, P = 0.002), fasting C-peptide (F-CPR) level (β= 0.492, P = 0.009) and dorsiflexion strength (β= -0.432, P = 0.047) were independent predictors of falls. The random forest classifier placed knee extension strength (covariate importance = 0.304), grip strength (0.234), F-CPR level (0.232) and dorsiflexion strength (0.230) in the top 4 important variables for falls. The rate of falls in persons with T2D was high even in middle age. Lower extremity muscle weakness as well as elevated F-CPR levels and reduced grip strength were shown to be important risk factors for falls in T2D.</p>
Unravelling potential virulence factor candidates in Xanthomonas citri. subsp. citri by secretome analysis
<p>Citrus canker is a major disease affecting citrus production in Brazil. Its mainly caused by <em>Xanthomonas citri </em>subsp. <em>citri</em> strain 306 pathotype A (Xac). We analysed the differential expression of proteins secreted by wild type Xac and an asymptomatic mutant for <em>hrpB4</em> (<em>ΔhrpB4</em>) grown in Nutrient Broth (NB) and a medium mimicking growth conditions in the plant (XAM1). This allowed the identification of 55 secreted proteins, of which 37 were secreted by both strains when cultured in XAM1. In this secreted protein repertoire, the following stand out: Virk, Polyphosphate-selective porin, Cellulase, Endoglucanase, Histone-like protein, Ribosomal proteins, five hypothetical proteins expressed only in the wild type strain, Lytic murein transglycosylase, Lipoprotein, Leucyl-tRNA synthetase, Co-chaperonin, Toluene tolerance, C-type cytochrome biogenesis membrane protein, Aminopeptidase and two hypothetical proteins expressed only in the <em>ΔhrpB4 </em>mutant. Furthermore, Peptidoglycan-associated outer membrane protein, Regulator of pathogenicity factor, Outer membrane proteins, Endopolygalacturonase, Chorismate mutase, Peptidyl-prolyl cis-trans isomerase and seven hypothetical proteins were detected in both strains, suggesting that there was no relationship with the secretion mediated by the type III secretory system, which is not functional in the mutant strain. Also worth mentioning is the Elongation factor Tu (EF-Tu), expressed only the wild type strain, and Type IV pilus assembly protein, Flagellin (FliC) and Flagellar hook-associated protein, identified in the wild-type strain secretome when grown only in NB. Noteworthy, that FliC, EF-Tu are classically characterized as PAMPs (Pathogen-associated molecular patterns), responsible for a PAMP-triggered immunity response. Therefore, our results highlight proteins potentially involved with the virulence. Overall, we conclude that the use of secretome data is a valuable approach that may bring more knowledge of the biology of this important plant pathogen, which ultimately can lead to the establishment of new strategies to combat citrus canker.</p>
Molecular dynamics trajectories, GROMACS input files, and analysis code from "Rational optimization of a transcription factor activation domain inhibitor" by Basu et. al, Nature Structural & Molecular Biology, 2023
<p>Molecular dynamics trajectories, GROMACS input files, and analysis code from "Rational optimization of a transcription factor activation domain inhibitor" by Basu et. al, Nature Structural & Molecular Biology, 2023</p> <p> </p> <p> </p>
Meta-analysis of the effects of abiotic factors on plant microbes
<p>The abiotic environment exerts strong effects on plant-associated microbes, shaping their interactions with plants and resulting ecosystem processes. However, these abiotic effects on plant-microbe interactions are often highly specific and contingent on the abiotic driver or microbial group, requiring synthesis work describing general patterns and from this generate hypotheses and guide mechanistic work. To address this, we conducted a meta-analysis of the effects of climate change-related abiotic factors, namely warming, drought, and eCO<sub>2</sub>, on plant-associated microbes distinguishing by microbial taxonomic or biological group (bacteria, fungi or virus) and the plant part where microbes are found or associated with (phyllosphere or rhizosphere). We found abiotic driver-specific patterns, whereby drought significantly reduced microbial abundance, whereas warming and eCO<sub>2</sub> had no significant effects. In addition, these abiotic effects were contingent on the microbial taxonomic group, with fungi being negatively affected by drought but positively affected by warming (eCO<sub>2</sub> enrichment had no effect), whereas bacteria and viruses were not significantly affected by any factor. Likewise, rhizopheric microbes were negatively affected by drought but positively affected by warming (eCO<sub>2</sub> enrichment had no effect), whereas phyllospheric microbes were not significantly affected by any factor. Collectively, these findings point to important implications for global change research by highlighting contrasting effects of climate change-related abiotic drivers on plant-associated microbes and the contingency of such effects on microbe life histories and the nature of their interactions with plants.</p>
Identification and expression analysis of transcription factors in the Carallia brachiata genome
<p>Rhizophoraceae has 2 terrestrial genera and 4 marine genera. The intertidal zone in which marine mangroves are located is known for its low oxygen and high salinity. Marine and terrestrial genera have evolved distinct adaptive characteristics, among which viviparous reproduction is the most unique. To investigate the genetic foundations difference underlying the adaptive mechanisms of marine–terrestrial genera, we selected two species from Rhizophoraceae. <em>Kandelia obovata</em> is marine and viviparous, and <em>Carallia brachiata</em> is terrestrial and non-viviparous.<em> </em>We compared their transcriptome of 8 tissues (root, stem, leaf, flower, ovule, fruit, seed, embryo) and found that the mature reproductive organs (fruit, seed, embryo) of <em>K. obovata </em>did not reduce metabolic activity compared to <em>C. brachiata</em>. The reproductive organs of <em>K. obovata</em> were regulated by the same gene set as vegetative organs. This contrasted with <em>C. brachiata</em>. Eight kinds of hormone transduction genes were up-regulated in the seed of <em>K. obovata</em>. Finally, and most importantly, the transcriptional factors AP2 and ARF families were significantly more expressed in the reproductive organs of <em>K. obovata</em> than in those of <em>C. brachiata</em>. At the same time, the ERF family was more expressed in its roots. The findings suggested that the hormone transduction may contribute to viviparous initiation. Transcriptional factors were quite crucial for mangroves' adaptation to wetlands.</p>
Data on soil variables (with plot IDs) and grassland species traits used for the analysis of grassland vegetation data by Pillar, V.D. (2024) Trait divergence in plant community assembly is generated by environmental factor interactions. Journal of Vegetation Science, 35, e13259. Available from: https://doi.org/10.1111/jvs.13259
<p>File <a href="../api/records/10983049/draft/files/Plot_IDs_990ua.txt/content" target="_blank" rel="noopener noreferrer">Plot_IDs_990ua.txt</a> contains the IDs of the 1-m2 plots used for the analysis of grassland vegetation data by Pillar, V.D. (2024) Trait divergence in plant community assembly is generated by environmental factor interactions. The plot data are stored in the sPlot database (PPBio South Brazilian Grassland Database).</p> <p>File <a href="../api/records/10983049/draft/files/E_990ua_21SoilVar.txt/content" target="_blank" rel="noopener noreferrer">E_990ua_21SoilVar.txt</a> contains data on soil variables evaluated in the 250 m transects, but here expanded to the 990 1-m2 plots (each transect was sampled using 10 1-m2 pots).</p> <p>File <a href="../api/records/10983049/draft/files/B_769spp_4t.txt/content" target="_blank" rel="noopener noreferrer">B_769spp_4t.txt</a> is the species trait database collected in the framework of several research projects in the Quantitative Ecology Lab (EcoQua) and Grassland Vegetation Studies Lab (LevCamp) of Universidade Federal do Rio Grande do Sul (UFRGS). Data gaps were filled by compiled from the TRY database and data imputation.</p> <p> </p> <p> </p>
Adolescents' mental health and maladaptive behaviors before the Covid-19 pandemic and one-year after: analysis of trajectories over time and associated factors
<p>The database reports data about psychopathological indexes in a sample of adolescent students (N=153) assessed before Covid-19 pandemic (T0, November 2019-January 2020) and one year after (T1, April-May 2021).</p>
Global analysis of environmental and socioeconomic factors associated with human burden of environmentally mediated pathogens
<p>This repository contains four datasets that support repeatability of the analyses in the Sokolow et al. paper published in <em>Lancet Planetary Health</em>. Descriptions of the four datasets are included in the metadata document. This study found that 80% of pathogen species known to infect humans are environmentally mediated, causing about 40% of contemporary infectious-disease burden (global loss of 130 million years of healthy life annually). More than 91% of this environmentally-mediated disease burden occurs in tropical countries, and the poorest countries carry the highest burdens across all latitudes. There were weak associations between disease burden and biodiversity or agricultural land use at the global scale. In contrast, the proportion of people with rural poor livelihoods in a country was a strong proximate indicator of environmentally mediated infectious disease burden there. Political stability and wealth were associated with improved sanitation, better health care, and lower proportions of rural poverty, indirectly resulting in lower burdens of environmentally mediated infections."</p>
Validating Instructional Practice Scale (IPS) for University Instructors: Confirmatory Factor Analysis
<p>.csv dataset for the study "validating instructional practice scale for instructors in some selected Ethiopian public universities: confirmatory factor analysis"</p>
Fig. 4 in The correlations between certain features of the journal Neotropical Ichthyology and its impact factor: a comparative analysis at the thematic and national levels
Fig. 4. Uncitedness rate of articles published in the Brazilian journals in Sample 2.
Fig. 1 in The correlations between certain features of the journal Neotropical Ichthyology and its impact factor: a comparative analysis at the thematic and national levels
Fig. 1. Brazilian journals and their corresponding self-cited rates between 2006 and 2011.
Fig. 2 in The correlations between certain features of the journal Neotropical Ichthyology and its impact factor: a comparative analysis at the thematic and national levels
Fig. 2. Brazilian journals and their corresponding self-citing rates between 2006 and 2011.
Uncertainties in greenhouse gas emission factors: A comprehensive analysis of switchgrass-based biofuel production
<p>This study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass-based biofuel production in Michigan. Using three life cycle assessment (LCA) databases— US lifecycle inventory database (USLCI), GREET, and Ecoinvent—each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI. The convergence of results across our previous study, static calculations, and Monte Carlo simulations enhances the credibility of estimated GWI values. Static calculations, validated by Monte Carlo simulations, offer reasonable central tendencies, providing a robust foundation for policy considerations. However, the wider range observed in Monte Carlo simulations underscores the importance of potential variations and uncertainties in real-world applications. Sensitivity analyses identify biofuel yield, GHG emissions of electricity, and soil organic carbon (SOC) change as pivotal parameters influencing GWI. Decreasing uncertainties in GWI may be achieved by making greater efforts to acquire more precise data on these parameters. Our study emphasizes the significance of considering diverse GHG factors and databases in GWI assessments and stresses the need for accurate electricity fuel mixes, crucial information for refining GWI assessments and informing strategies for sustainable biofuel production.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.