Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
15,459
datasets available to search
ShareScore release 0.9.0
Dataset results
15,459 results for “Factors”
Factores de deterioro del hábitat residencial
<p>Coordinador del Seminario: Carlos A. Navarrete Ulloa.<br> Expositor: Ramona Esmeralda Velázquez García.</p> <p>Comité Ejecutivo PRONACE-Vivienda</p> <ul> <li>Fernando Córdova Canela, Centro Universitario de Arte, Arquitectura y Diseño, Universidad de Guadalajara (UdeG).</li> <li>Francisco Javier Porras Sánchez, Instituto de Investigaciones Dr. José María Luis Mora.</li> <li>Gabriel Castañeda Nolasco, Universidad Autónoma de Chiapas (UNACH).</li> <li>Carlos A. Navarrete Ulloa, Centro Universitario de Tonalá, (UdeG).</li> </ul> <p>Exposición realizada en el marco del PRONACE Vivienda en el cual se comenta la lectura:</p> <p>Tapia, R., Lange, C., y Larenas, J. (2009). Factores de deterioro del hábitat residencial y vulnerabilidad social en la conformación de barrios precarios: breve revisión de algunos programas de barrios en Chile y en la Región. Santiago de Chile: Universidad de Chile, Programa Domeyko, Cuaderno de Trabajo Volumen, (1).</p> <p><a href="https://repositorio.uchile.cl/handle/2250/144796">https://repositorio.uchile.cl/handle/2250/144796</a></p>
Dataset Global Warming Forecast using Acceleration Factors
<p>The dataset includes results of Global Warming forecast using four methods.</p> <p>The methods include a parabolic trendline of the last 61 years of global warming and cumulated CO2 emissions.</p> <p>Two other methods apply the velocity and the acceleration of global warming and cumulative CO2 emissions.</p> <p>The relation between the global surface temperature change and the change in the cumulative CO2 emissions was determined in previous publications as 0.000745°C/GtCO2.</p> <p>The average result from all four methods for the business as usual CO2 mitigation scenario is 4.4°C (4.1°C -5.0°C).</p> <p>According to this forecast, the global temperature change will reach 1.5°C in 2031 (9 years from now) and 2.0°C in 2047 (25 years from now).</p>
Benchmarking tools for transcription factor prioritization
<p><strong>Abstract:</strong></p> <p>Spatiotemporal regulation of gene expression is controlled by transcription factor (TF) binding to regulatory elements, resulting in a plethora of cell types and cell states from the same genetic information. Due to the importance of regulatory elements, various sequencing methods have been developed to localise them in genomes, for example using ChIP-seq profiling of the histone mark H3K27ac that marks active regulatory regions. Moreover, multiple tools have been developed to predict TF binding to these regulatory elements based on DNA sequence. As altered gene expression is a hallmark of disease phenotypes, identifying TFs driving such gene expression programs is critical for the identification of novel drug targets.In this study, we curated 84 chromatin profiling experiments (H3K27ac ChIP-seq) where TFs were perturbed through e.g., genetic knockout or overexpression. We ran nine published tools to prioritize TFs using these real-world data sets and evaluated the performance of the methods in identifying the perturbed TFs. This allowed the nomination of three frontrunner tools, namely RcisTarget, MEIRLOP and monaLisa. Our analyses revealed opportunities and commonalities of tools that will help to guide further improvements and developments in the field.</p> <p><strong>Dataset description:</strong></p> <ul> <li>tf_tool_benchmark_atacseq_diffPeaks.tar.gz -Archive containing differential peak statistics, tool diff peak input files (fore- and background) for all currated ATAC-seq datasets. </li> <li>tf_tool_benchmark_h3K27ac_chipseq_diffPeaks.tar.gz - Archive containing differential peak statistics, tool diff peak input files (fore- and background) for all currated H3K27ac ChIP-seq datasets. </li> <li>tf_tool_benchmark_atacseq_results.tar.gz - Archive containing the raw tool results for each ATAC-seq dataset.</li> <li>tf_tool_benchmark_chipseq_results.tar.gz - Archive containing the raw tool results for each H3K27ac ChIP-seq dataset.</li> <li>tf_tool_benchmark_results.tar.gz - Archive containing tool results summary for plotting (rds files).</li> </ul> <p><strong>Contact: </strong>Sebastian Steinhauser - sebastian.steinhauser@novartis.com</p>
Role of environmental factors in the genetic structure of a highly mobile seabird
<p><strong>Aim:</strong> Environmental features can act as selection pressures and barriers to gene flow between populations. The genetic structuring of highly mobile but philopatric seabirds creates a paradox, and the role of oceanographic and geographic variables is still poorly understood. In this study, we investigate the influence of environmental and geographic variables in the genetic and phenotypic diversity of a pantropical seabird breeding in islands and archipelagos separated by different geographic distances, up to thousand kilometers, and which differ in environmental characteristics.</p> <p><strong>Location:</strong> Islands and archipelagos in the southwestern Atlantic Ocean.</p> <p><strong>Taxon:</strong> <em>Sula dactylatra</em>, Lesson, 1831 (masked booby)<em>.</em></p> <p><strong>Methods:</strong> The population structure of the species was accessed through mitochondrial and nuclear DNA. To test Isolation by Environment (IBE) <em>vs</em>. by Distance (IBD), sea surface temperature, primary productivity, and salinity, as well as isotopic niche based on carbon and nitrogen, and distances between colonies and from the continent, were used. We also tested the correlation between the genetic structure and the morphometry of individuals in each colony.</p> <p><strong>Results:</strong> We identified the presence of low genetic structure between populations. Nevertheless, differences were identified between inshore and offshore colonies, with the influence of landscape characteristics of these two types of environment. The morphometric and isotopic niche variations are consistent with this segregation.</p> <p><strong>Main conclusions:</strong> Environmental variables of coastal and oceanic environments seem to influence the genetic structure of masked boobies, even though it is low in the SW Atlantic Ocean, highlighting the role of environmental heterogeneity in shaping biodiversity.</p>
Symbol Representation of the Three Gluon Form Factor in N=4 Planar Super Yang-Mills Theory
<p>Datasets describing the symbol of the three-gluon form factor in N=4 planar super Yang-Mills theory, generated using the amplitude bootstrap approach. The file "EZ_symb_new_norm" contains the symbol form of this quantity at 1 through 5 loops of precision, while the file "EZ6_symb_new_norm" contains the symbol at 6 loops. The file "EZ_symb_quad_new_norm" contains the symbol at 1 through 6 loops in compressed "quad" form, where the final-entry conditions described in (https://arxiv.org/pdf/2204.11901) are used to dramatically reduce the total number of terms in the symbol. The file "EZ7_symb_quad_new_norm" contains the symbol at 7 loops in the "quad" form. </p> <p>The tag “new_norm” refers to the fact that in the symbols given here, the letters a,b,c are defined by a = sqrt(u/(v*w)), b = sqrt(v/(w*u)), c = sqrt(w/(u*v)), as in arXiv:2405.06107, in order to make all coefficients integers. In contrast, in arXiv:2204.11901, the letters a,b,c were defined by a = u/(v*w), b = v/(w*u), c = w/(u*v).</p> <p>In addition to the funding sources listed, MW was supported by research grant 00025445 from Villum Fonden.</p>
Evaluation of transcription factor knockout impact on paclitaxel response for Triple Negative Breast Cancer
<div>Data and code related to Zenodo repository: 10.5281/zenodo.11238552</div> <div> </div> <div>Two experimental formats included:</div> <div>'fixed' prefix: data from terminal time point of siRNA screen applied to HCC1143, HCC1806, and MDA-MB-468 Triple Negative Breast Cancer cell lines.</div> <div>'live' prefix: data from live-cell imaging of cell cycle reporter (HDHB-mClover/NLS-mCherry) HCC1143 Triple Negative Breast Cancer cell line.</div> <div>Note: 'live' level 1 data is available upon request (heiserl@ohsu.edu, calistri@ohsu.edu).</div> <div> </div> <div>Experimental goal:</div> <div>Evaluate whether siRNA knockdown of transcription factors elevated during paclitaxel response impact cell count, cell morphology or cycling dynamics.</div> <div> </div> <div>Methods:</div> <div>siRNA Knockdown: Cells were plated in 90ul of serum free media per well of a 96 well plate. 24 hours later, siRNA knockdown mixture was prepared using a cell-line optimized concentration of Lipofectamine RNAiMAX (cat 13778075-075, Invitrogen) and siRNA (Horizon Discovery ON-TARGETplus) following RNAiMAX recommended protocol. The final concentration of siRNA per well was 1pmol and the final volume of RNAiMAX per well was 75nL for HCC1143, and 37.5nL for HCC1806 or MDA-MB-468 in 100uL of cell containing volume. 24 hours after siRNA transfection cells were treated with an addition of 100uL complete media containing either DMSO vehicle control or paclitaxel. </div> <div> </div> <div>Fixed-cell assays: Cells were plated at 3000 cells in 100ul of complete media per well in a 96 well plate (#08-772-225, FisherScientific). After 24 hours, an additional 100ul of either vehicle (0.1% DMSO) or paclitaxel containing complete media was added. After 72 hours cells were fixed with 4% Formaldehyde (#28908, ThermoFisher Scientific) for 15 minutes at room temperature, then permeabilized with 0.3% Triton X-100 (#X100-100ML, Sigma Aldrich) for 10 minutes at room temperature, then washed twice with PBS. Fixed cells were then stained with 0.5ug/mL DAPI (4083S, Cell Signaling Technology) in PBS for 15 minutes at room temperature. Following DAPI staining, wells were washed once with PBS, then stained with 1:20,000 HCS CellMask Green in PBS (H32714, Invitrogen) for 15 minutes at room temperature. Wells were washed twice with room temperature PBS and then 4 fields of view per well imaged on an InCell 6000 (GE Healthcare). Images were segmented with two custom Cellpose models to segment the nucleus (using parameters: diameter = 50, chan = DAPI, chan2 = Cellmask Orange) and cytoplasm (using parameters: diameter = 90, chan = Cellmask Orange, chan2 = DAPI). Image quantification was performed in R (v4.3.1) using EBImage (v4.42.0), and cells were annotated based on the number of distinct nuclei segmented within each cytoplasmic mask. </div> <div> </div> <div>HDHB reporter live-cell assays: siRNA knockdown and drug treatment was performed as described above, and then the plate was loaded on an Incucyte S3 (Sartorious) and cells imaged every 15 minutes for 72 hours post drug treatment. At each timepoint 4 fields of view were captured at 20x magnificantion in each well using the phase, red and green channels. A cytoplasmic mask was computed from the mean of normalized red/green channel (cellpose parameters: diameter = 57, chan = mean(normalized(red), normalized(green)), and a nuclear mask was computed from the red channel (cellpose parameters: diameter = 30, chan = DAPI) using custom trained Cellpose models. Image quantification was performed in R (v4.3.1) using EBImage (v4.42.0). An additional perinuclear ring mask was computed as the 11 pixel dilation from the nuclear mask, but still bound by the cytoplasmic mask. To determine mClover localization thresholds for cell cycle assignment, 250 cell images were randomly selected and manually assigned to the G1, S/G2 or M cell cycle state based on mClover localization. The mClover intensity ratios were then used to determine thresholds for automated cell cycle phase calling which was applied to the rest of the data set (Supplemental Figure 5A). Mononuclear cells with a Perinuclear:Nuclear mean intensity ratio greater than 0.8 and Nuclear:Cytoplasmic total intensity less than 0.5 were assigned to the S/G2 phase. Mononuclear and Multinuclear cells with a Nuclear:Cytoplasmic total intensity ratio greater than 0.8 and Perinuclear:Nuclear mean intensity ratio less than 0.8 were assigned to the ‘M’ phase. The remainder of mononuclear cells were assigned ‘G1’, and the remainder of multinucleated cells were assigned ‘Multinucleated’. </div> <div> </div> <div>Included files:</div> <div>fixed_level_1-plate_#.zip : Six .zip archives containing the raw images (DAPI/CellMask/Brightfield) from fixed-cell experiments.</div> <div>plate 1: HCC1143 cells treated with plate A schema</div> <div>plate 2: HCC1143 cells treated with plate B schema</div> <div>plate 3: HCC1806 cells treated with plate A schema</div> <div>plate 4: HCC1806 cells treated with plate B schema</div> <div>plate 5: MDA-MB-468 cells treated with plate A schema</div> <div>plate 6: MDA-MB-468 cells treated with plate B schema</div> <div>fixed_level_2: Data quantified from cellpose masks at the single-nuclei level (redundant cytoplasm information)</div> <div>fixed_level_3: Data from 'fixed_level_2.csv' collapsed to the single cell level, including staining intensity and aggregate nuclear information</div> <div>fixed_incell_to_cellpose.rmd: R markdown code for converting original incell files (fixed_level_1) to RGB images for cellpose segmentation</div> <div>fixed_image_quantification.rmd: R markdown code for quantifying images using cellpose segmentation masks and original images (fixed_level_1)</div> <div>fixed_cellpose_models.zip: Archive including cellpose models used for fixed experiment</div> <div>live_level_2: Data quantified from cellpose masks at the single-nuclei level (redundant cytoplasm information)</div> <div>live_level_3: Data from 'live_level_2.csv' collapsed to the single cell level, including staining intensity and aggregate nuclear information</div> <div>live_level_4: Data from 'live_level_3.csv' collapsed to the single condition level summarizing the number, multinucleation status and phase of cells at each time point.</div> <div>live_image_quantification.rmd: R markdown code for quantifying images using cellpose segmentation masks and original images (live_level_1).</div> <div>l ive_incu_archive2rgb.rmd: R markdown code for converting incucyte archive formatted data into RGB images, where the blue channel is the arithmetic mean of the min-max (0-1) normalized red and green channels.</div> <div>live_cellpose_models.zip: Archive including cellpose models used for live experiment.</div> <div> </div> <div> </div>
University dropout: A systematic review of the main determinant factors
<p><strong><span>Introduction:</span></strong><span> This research is a systematic review aimed at synthesizing scientific evidence on the causes of university dropout, focusing on the subcategories of vocational guidance, academic performance, socioeconomic status, and institutional aspects between 2020 and June 2024. <strong>Methods:</strong> Only articles addressing university dropout were considered, analyzing dimensions such as vocational guidance, academic performance, socioeconomic status, and institutional aspects. Articles published in indexed scientific journals with double-blind, double-blind peer, or open reviews between 2020 and June 2024 were included. The main databases used were Scopus, Web of Science, and Google Scholar. To assess the risk of bias in qualitative studies, the criteria from the article "Validity criteria for qualitative research: three epistemological strands for the same purpose" were used. For quantitative studies, the criteria from the article "Evaluating survey research in articles published in Library Science journals" were followed. For mixed-method studies, both sets of criteria were combined. <strong>Results:</strong> A total of 23 studies were included: 15 quantitative (65.22%), 3 qualitative (13.04%), and 5 mixed-method (21.74%). All studies (100%) addressed the subcategories of socioeconomic status and institutional aspects. Regarding the academic performance subcategory, 86% of the studies addressed it, while the vocational guidance subcategory was covered by 73.91% of the studies. <strong>Conclusions:</strong> Vocational guidance, academic performance, socioeconomic status, and institutional aspects are crucial for reducing university dropout. Providing adequate professional guidance, academic support, financial assistance, and strong institutional support is fundamental to improving student retention and academic success.</span></p>
Orthodontically Induced External Apical Root Resorption with genetic and non-genetic factors
<p>Orthodontically Induced External Apical Root Resorption (OIEARR) dataset with genetic and non-genetic factors from a sample of 195 patients previously submitted to orthodontic treatment. Methodology for patient selection is described in published related works as well as materials and methods. Percentage of OIEARR were assessed in maxillary teeth: the four incisors and the two canines. Ten clinical and treatment variables were recorded: gender, age, treatment duration, premolar extractions, skeletal pattern, Hyrax appliance, functional appliance, overjet, anterior open bite and tongue thrust. Single nucleotide polymorphisms (SNPs) of six genes : rs114363 from <em>IL1B</em>; rs3102735 from <em>TNFRSF11B</em>, encoding OPG; rs1059703 from <em>I</em><em>RAK1</em>; rs315952 from <em>IL1RN</em>; rs1805034 from <em>TNFRSF11A</em>, encoding RANK; rs1718119 from <em>P2RX7</em>.</p>
Correlator data for determination of the I=1 pion-pion scattering amplitude and timelike pion form factor from Nf=2+1 lattice QCD
<p>Bootstrap samples of all correlation functions involved in the analysis of pion-pion scattering data and the timelike pion form factor described in "The I =1 pion-pion scattering amplitude and timelike pion form factor from N f = 2 + 1 lattice QCD". Additionally, an analysis file is provided for each ensemble which stored the analysis choices made in that work. These data are intended for use with the Jupyter notebook located in https://github.com/ebatz/jupan, which provides an interface. This notebook performs the entire analysis chain discussed in the above paper. </p>
Virtual ChIP-seq predictions of binding of 36 transcription factor in Roadmap Epigenomics Project tissues
<p>This dataset contains predictions of Virtual ChIP-seq for binding of 36 transcription factors in Roadmap Epigenomics dataset tissues with matched DNase-seq and RNA-seq data.</p> <p>Tarball contains subfolders for each of the 36 TFs where Virtual ChIP-seq median MCC in validation cell types was > 0.3.</p> <p>Each subfolder contains gzipped BED files. Each file is named as <Tissue>_<Age>_<TF>_<Accession>_Predictions.bed.gz. Columns correspond to Chromosome, Start, End, <Tissue>_<Age>_<TF>_<Accession>, Posterior probability</p> <p>You can use the posterior probabilities provided in Virchip_PosteriorCutoffs_V3.0.0.tsv. These are posterior probability cutoffs which maximized MCC in H1-hESC cell type, or are set to 0.4 if there was no ChIP-seq data of that TF in H1-hESC (0.4 is the mode of all optimal posterior probability cutoffs in H1-hESC).</p>
Data from: Selectivity of Guanine Nucleotide Exchange Factor-mediated Cdc42 activation in primary human endothelial cells
<p>Data that was reported in "Selectivity of Guanine Nucleotide Exchange Factor-mediated Cdc42 activation in primary human endothelial cells" by </p> <p>Nathalie R. Reinhard<sup>1</sup>, Sanne van der Niet<sup>1</sup>, Anna Chertkova<sup>1</sup>, Marten Postma<sup>1</sup>, Theodorus W.J. Gadella Jr.<sup>1</sup>, Peter L. Hordijk<sup>1,2</sup>, and Joachim Goedhart<sup>1*</sup><br> </p> <p><strong>Affiliations:</strong></p> <p><sup>1 </sup>University of Amsterdam, Molecular Cytology, Swammerdam Institute for Life Sciences, van Leeuwenhoek Centre for Advanced Microscopy, Amsterdam, the Netherlands</p> <p><sup>2 </sup>Department of Physiology, Free University Medical Center, Amsterdam, The Netherlands</p> <p> </p> <p>*Correspondence to: j.goedhart@uva.nl</p>
Heart Failure eQTLs companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure"
<p>These are the results of a QTL analysis companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure". We performed RNA expression measurements and obtained genotype information in genome-wide markers for 313 patients (177 failing hearts , 136 donor, non-failing [control] hearts) using Affymetrix expression and Affymetrix Human 6.0 respectively.<strong> </strong>Prior to eQTL discovery, we used PEER to find hidden covariates that could confound signals in our data as well as filtering any genotypes with major allele frequencies less than 5%. To test associations between gene expression in each cohort separately, we used QTLTools with an additive model accounting for gender, age, sample site, and the PEER factors as covariates. We corrected for eQTL multiple association testing using a 10000 permutations per locus in a 2 megabase window and a false discovery rate cutoff of 5%. To select the number of PEER factors, we performed the full analysis multiple times from 1 to 15 PEER factors and observed a saturation of new QTLs being discovered when using 10 factors.</p> <p>Four files are provided, two for each cohort (cases and controls):</p> <p>- peer_[cases|controls]_nominal.txt: Nominal associations with a p-value threshold of 0.001</p> <p>- peer_[cases|controls]_permutations_all.significant.txt: All significant associations detected after the QTLtools permutation test.</p> <p>The column names are those from QTLtools, in order:</p> <p><br> 1. The phenotype ID<br> 2. The chromosome ID of the phenotype<br> 3. The start position of the phenotype<br> 4. The end position of the phenotype<br> 5. The strand orientation of the phenotype<br> 6. The total number of variants tested in cis<br> 7. The distance between the phenotype and the tested variant (accounting for strand orientation)<br> 8. The ID of the tested variant ( in Affy 6.0 SNP ids)<br> 9. The chromosome ID of the variant<br> 10. The start position of the variant<br> 11. The end position of the variant<br> 12. The nominal P-value of association between the variant and the phenotype<br> 13. The corresponding regression slope<br> 14. A binary flag equal to 1 is the variant is the top variant in cis</p>
QSPR models for bioconcentration factor (BCF): Are they able to predict data of industrial interest?
<p>This dataset is described and studied in the article </p> <p>"QSPR models for bioconcentration factor (BCF): Are they able to predict data of industrial interest?"</p> <p>published in <em>SAR and QSAR Environmental Research</em> (Taylor&Francis).</p> <p>Files description:</p> <p>SI_BCFtrainset.xlsx: a collection of 1129 chemical structures and CAS identifiers with their logBCF values extracted from various literature sources.</p> <p>SI_BCFtestset.xlsx: a collection of 204 chemical structures for which the logBCF is considered of lower reliability and used as an external test set.</p> <p>SI_FullDataset_rawdata.csv: the raw data composed of 15372 entries with the following columns: CASRN, Tissue, Duration [d], Test organism, Exposure type, Steady state, RESPONSE, RESPONSE UNIT, Media type, TakenFrom, TITLE, AUTHOR, YEAR, SOURCE, SMILES</p> <p>SI_ExcludedOutliers34.csv: 34 chemical structures that have been identified as suspicious during analysis.</p> <p> </p>
Dateset on 'Disentangling associations of human wellbeing with green infrastructure, degree of urbanity, and social factors around an Asian megacity'
<p>The data was collected a part of the baseline survey on household socio-economics among the Bengalurian along the rural-urban interface. </p>
Supplemental data for: Mapping lifestyle factors in blood glucose variability in adolescents with Type 1 Diabetes Mellitus- A pilot study
<div> <p>The dataset was used in the paper “Mapping lifestyle factors in blood glucose variability in adolescents with Type 1 Diabetes Mellitus- A pilot study”. The article is currently under review for publication. DOI to be inserted.</p> </div> <div> <p>A data-in-brief article is to be published to give in-depth information about the data collected to improve reproducibility "Dataset for: Lifestyle Factors and Blood Glucose Variability in Adolescents with Type 1 Diabetes Mellitus". DOI to be inserted. </p> <p> </p> <p>The aim of the study was to assess whether adolescents with T1D in Ireland meet current nutrition and physical activity (PA) guidelines and to explore the impact of nutrition and PA on glycaemic variability (GV). The dataset includes continuous glucose monitoring (CGM) data, dietary intake records, and PA metrics, providing a comprehensive view of the participants' glucose levels and associated lifestyle behaviours.</p> </div>
Supplementary Files for "Proteomic analysis of the sponge Aggregation Factor implicates an ancient toolkit for allorecognition and adhesion in animals"
<p>This repository hosts supplemental files for the Manuscript "Proteomic analysis of the sponge Aggregation Factor implicates an ancient toolkit for allorecognition and adhesion in animals" by Ruperti, et al., 2024.</p> <ul> <li><strong>Suppl_File_wreath_domain_model.pdb</strong>: AlphaFold3 model for the <em>C. prolifera</em> MAFp3 wreath domain (aa 33 - 317)</li> <li><strong>Suppl_File_MAFAP1_Cterm_model.cif</strong>: AlphaFold3 model for the <em>C. prolifera</em> MAFAP1 C-terminal domain, region 1 and 2</li> <li><strong>Suppl_File_AFInteracting_hmm.hmm</strong>: HMM sequence profile of AF-interacting region of C. prolifera proteins</li> <li><strong>XXX_Foldseek.zip</strong>: Foldseek raw search results, separated by target databases (Swissprot, AFDB, CATH50)</li> </ul>
Integration of High-Tc Superconductors with High-Q-Factor Oxide Mechanical Resonators (Dataset)
<p>Micro-mechanical resonators are building blocks of a variety of applications in basic science and consumer electronics. This device technology is mainly based on well-established and reproducible silicon-based fabrication processes with outstanding performances in term of mechanical <em>Q</em>-factor and sensitivity to external perturbations. Broadening the functionalities of micro-electro-mechanical systems (MEMS) by the integration of functional materials is a key step for both applied and fundamental science. However, combining functional materials with silicon-based devices is challenging. An alternative approach is directly fabricating MEMS based on compounds inherently showing non-trivial functional properties, such as transition metal oxides. Here, a full-oxide approach is reported, where a high-Tc superconductor YBa<sub>2</sub>Cu<sub>3</sub>O<sub>7</sub> (YBCO) is integrated with high <em>Q</em>-factor micro-bridge resonators made of single-crystal LaAlO<sub>3</sub> (LAO) thin films. LAO resonators are tensile strained, with a stress of about 350 MPa, show a <em>Q</em>-factor above 200k, and have low roughness. YBCO overlayers are grown ex situ by pulsed laser deposition and YBCO/LAO bridges show zero resistance below 78 K and mechanical properties similar to those of bare LAO resonators. These results open new possibilities toward the development of advanced transducers, such as bolometers or magnetic field detectors, as well as experiments in solid state physics, material science, and quantum opto-mechanics.</p>
Emission factors and chemical composition of particulate matter from residential biomass combustion
<p>Emission factors and chemical composition of particulate matter from residential biomass combustion.</p>
Forest carbon removal factor variance by climate domain
<p>Uncertainty (variance) in removal factor (annual sequestration rate) for forest carbon in new and existing forests by climate domain (tropical, subtropical, temperate, boreal). Uncertainty analysis is from Harris et al. 2021 Nature Climate Change. Units are aboveground carbon Mg^2/ha^2/year^2. New and existing forest are distinguished by the presence or absence of Hansen et al. 2013 tree cover gain pixels. </p> <p>Note: Uncertainty for existing temperate forest removal factors is so high because the IPCC national greenhouse gas inventory guidelines have a very high uncertainty for these forests (2019 refinement of guidelines). </p> <p>Note: Uncertainty analysis is for published version of the model (v1.2.0).</p> <p>https://github.com/wri/carbon-budget</p>
TF-Marker: A comprehensive manually curated database for transcription factors and related markers in specific cell and tissue types in human.
<p>Here, we developed the TF-Marker database (TF-Marker, http://bio.liclab.net/TF-Marker/) which is committed to a comprehensive manual curation of TFs and related markers with experimental evidence in specific cell and tissue types in human. Currently, through reviewing <strong>2,091</strong> published literature, we have manually classified TFs and related markers into five types according to their functions: 1) <strong>TF</strong>: TFs, which regulate the expression of markers; 2) <strong>T Marker</strong>: markers, which are regulated by TFs (TF and T Marker pairs can identify cell types more specifically); 3) <strong>I Marker</strong>: markers, which influence the activity of TFs (I Markers can also influence the development of specific cells and tissues); 4) <strong>TFMarker</strong>: TFs, which play roles as markers (TFMarkers are cell/tissue-specific TFs used as cell markers in biology experiments); and 5) <strong>TF Pmarker</strong>: TFs, which play roles as potential markers. By curating thousands of published literature, <strong>5,905</strong> entries including <strong>1,316</strong> TFs, <strong>1,092</strong> T Markers, <strong>473</strong> I Markers, <strong>1,600</strong> TFMarkers and <strong>1,424</strong> TF Pmarkers, were annotated in <strong>383</strong> cell types and <strong>95</strong> tissue types in human. Moreover, TF-Marker divided markers into disease markers and tissue/cell-specific markers. TF-Marker is an elaborate database, which provides TFs and related markers supported by experimental evidence. We believe TF-Marker will provide strong support for research into cell/tissue-specific TFs and related markers.</p>
ScienceDex guides
Understand access before you commit
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.