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Figure 6 in A new icriodontid conodont cluster with specific mesowear supports an alternative apparatus motion model for Icriodontidae
Figure 6. Diagrams illustrating orientation and direction of denticle tip wear. A, Icriodus aff. michiganus; left-side illustration shows the orientation of the inclined facet plane, right-side illustration the direction of vertically inclined facet; Middle Devonian, Eifel, Germany; sample BL-12-29c-9. B, Icriodus sp. left-side illustration shows the orientation of the inclined facet plane, middle the direction of the vertically inclined facet, and right the orientation and direction of the facet plane of median row denticles; Middle Devonian, Eifel, Germany; sample BL-12-29c-3).
Figure 4. A in A new icriodontid conodont cluster with specific mesowear supports an alternative apparatus motion model for Icriodontidae
Figure 4. A, denticle tip wear of the dextral I element of Caudicriodus woschmidti; Early Devonian, southern Burgenland, Austria; Ki/ 4/2a-1, NHMW 2011/0374/0001. B, detailed view of oral surface of the dextral I element with extent and orientation of tip wear indicated by dotted line and arrow head.
Figure 3 in A new icriodontid conodont cluster with specific mesowear supports an alternative apparatus motion model for Icriodontidae
Figure 3. Chronological listing of notation history for icriodontid apparatus elements. Morphologically similar coniform element types and the icriodontan element evaluated for this study are highlighted in different colours or shades.
Figure 2 in A new icriodontid conodont cluster with specific mesowear supports an alternative apparatus motion model for Icriodontidae
Figure 2. Conodont cluster of Caudicriodus woschmidti, Early Devonian, southern Burgenland, Austria; Ki/4/2a-1, NHMW 2011/0374/ 0001. A, SEM scan of the conodont cluster. B, detailed view of the coniform elements (C1–C5) close to the dextral I element. C, D, computer microtomography-based three-dimensional reconstruction with identification of all elements. E, hypothetical arrangement of all elements preserved within the fused conodont cluster.
Figure 1 in A new icriodontid conodont cluster with specific mesowear supports an alternative apparatus motion model for Icriodontidae
Figure 1. Locality map and section log from the 'Kottwitz' quarry (southern Burgenland, Austria), where the Caudicriodus woschmidti conodont cluster was found.
Research Data for Comparative Evaluation of RT-PCR and Antigen-based Rapid Diagnostic Tests (Ag-RDTs) for SARS-CoV-2 Detection: Performance, Variant Specificity, and Clinical Implications
<p>This dataset represents laboratory findings for the comparative evaluation of the diagnostic performance of Ag-RDTs (Flourescence Immunoassay and Lateral Flow Immunoassay) with RT-PCR</p>
Transcriptomic atlas reveals organ-specific disease tolerance in sickle cell mice. Dataset for bone marrow, HbSS Townes mice injected or not with heme
<p>The objective of this experiment was to explore the transcriptome of the HbSS Townes mouse model of sickle cell disease. Townes model mice carry several human hemoglobin knock-in genes replacing the endogenous mouse genes and may be useful in studying sickle cell disease. All mice were genotyped, age- and sex-matched littermates. All HbAA (control, normal human hemoglobin) vs HbSS (sickle cell disease, mutated human hemoglobin) mice were used for experimentations at 6-8 weeks of age, to limit intra-group heterogeneity. Hemin (Ferriprotoporphyrin IX) was purchased from Frontiers Scientific and injected intravenously (iv.) in a retroorbital sinus at a concentration of 24 µmol/kg. Control mice received PBS instead. Mice were anesthetized with isoflurane 2-3% for injections, blood collection and sacrifice. All mice were sacrificed by cervical dislocation, 4 hours after injection.</p> <p>Here the dataset for HbSS mice injected or not with heme is uploaded.</p> <p>The corresponding dataset for the HbAA mice injected or not with heme can be found at <strong>10.5281/zenodo.10961162</strong></p> <p>Bone marrow RNA was extracted by Macherey Nagel kit, according to the manufacturer’s instructions. The quality and quantity of mRNA were evaluated using a 2100<br>bioanalyzer with TNA 6000 NanoKits (all Agilent Technologies, Palo Alto, CA, USA). RNA Integrity Numbers superior to 7 were eligible for subsequent reverse transcription into cDNA. RNAseq was performed at the GenomIC plateform Cochin Institute INSERM U1016. After RNA extraction, RNA quality (RNA integrity number) was estimated. 1μg of high-quality total RNA sample (RIN &gt;7) was processed to build up the libraries, using TruSeq Stranded mRNA kit (Illumina) according to manufacturer instructions. Briefly, purified poly-A containing mRNA molecules were fragmented and reverse-transcribed using random primers. Replacement of dTTP by dUTP during second strand synthesis allowed us to achieve strand specificity. Addition of a single A base to the cDNA was followed by ligation of Illumina adapters.<br>Libraries were quantified by qPCR using KAPA Library Quantification Kits for Illumina Libraries (KapaBiosystems, Wilmington, MA). Library profiles were assessed using DNA High Sensitivity LabChip kits on an Agilent Bioanalyzer. Libraries were sequenced on an Illumina Nextseq 500 instrument using 75 base-lengths read V2 chemistry in a paired-end mode. After sequencing, primary analysis based on AOZAN software (ENS, Paris), was applied to demultiplex and control the quality of the raw data (based of FastQC modules / version 0.11.5).</p> <p>The dataset here represents 4 groups of mice, 4 mice per group as follows: HbAA PBS, HbAA heme, HbSS PBS, HbSS heme. </p> <p> </p>
Data from: Tissue-specific O-GlcNAcylation profiling identifies substrates in translational machinery in the Drosophila mushroom body contributing to olfactory learning
<p><em>O-</em>GlcNAcylation is a dynamic post-translational modification that diversifies the proteome. Its dysregulation is associated with neurological disorders that impair cognitive function, and yet identification of phenotype-relevant candidate substrates in a brain-region-specific manner remains unfeasible. By combining an <em>O-</em>GlcNAc binding activity derived from<em> Clostridium perfringens</em> OGA (<em>Cp</em>OGA) with TurboID proximity labeling in <em>Drosophila</em>, we developed an <em>O-</em>GlcNAcylation profiling tool that translates <em>O-</em>GlcNAc modification into biotin conjugation for tissue-specific candidate substrates enrichment. We mapped the <em>O-</em>GlcNAc interactome in major brain regions of <em>Drosophila</em> and found that components of the translational machinery, particularly ribosomal subunits, were abundantly <em>O-</em>GlcNAcylated in the mushroom body of <em>Drosophila</em> brain. Hypo-<em>O-</em>GlcNAcylation induced by ectopic expression of active <em>Cp</em>OGA in the mushroom body decreased local translational activity, leading to olfactory learning deficits that could be rescued by dMyc overexpression-induced increase of protein synthesis. Our study provides a useful tool for future dissection of tissue-specific functions of <em>O-</em>GlcNAcylation in <em>Drosophila</em> and suggests a possibility that <em>O-</em>GlcNAcylation impacts cognitive function via regulating regional translational activity in the brain.</p>
Dataset for Evaluating habitat-specific interference in automated radio telemetry systems: implications for animal movement studies
<h1>Abstract </h1> <p>Automated radio telemetry systems have become a popular and invaluable tool in tracking the activity and movement of wild animals. However, many environmental conditions can hinder accuracy when tracking with this technology. For instance, study sites may contain multiple habitat types, each habitat uniquely affecting the signal strength received from tagged species. To investigate the influence of a structurally diverse study site on an automated radio telemetry system, we conducted this project at a restored and managed pine barren habitat that consisted of a mix of mature pitch pine, treated pitch pine, scrub oak, and hardwood forests. This site, Montague Plains Wildlife Management Area, Montague, Massachusetts, is also a known breeding ground for Eastern whip-poor-will (Antrostomus vociferus). To measure the relationship of radio signal strength with distance across each habitat, we used radio telemetry equipment manufactured by Cellular Tracking Technologies. We produced negative exponential decay functions measuring radio signal strength over distance and tested for differences among habitat types on radio signal strength (RSS). We found that decay function parameters significantly differed by habitat type, prompting us to investigate if accounting for these differences improved location estimate accuracy. To test this, we estimated known locations using trilateration methods with and without habitat calibration. Comparing these tests indicates that habitat-specific adjustments significantly improved location accuracy. Lastly, we visualized estimated RSS-based locations of one week of whip-poor-will data and compared them to GPS data generated from the same individual. Previous studies have accounted for types of environmental interference (like elevation) in the field but have avoided incorporating habitat-specific factors by working with node networks covering a relatively small area, but in this study, we examined the potential to scale up for larger areas and in more complex habitats.</p> <p> </p>
F I G U R E 3 in Estimating multiple years, tributary-specific, and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture-mark-recapture experiments
F I G U R E 3 (a) Posterior distribution of the difference between the true value and posterior distribution estimates (in percentage of the true value) of annual total smolt abundance obtained from DM (Dirichlet-multinomial) model M5 (simulation exercise). Each line is a year, and the 10 (one for each replicate) distributions are overlaid. The overall average difference is indicated in the left corner of the panel and represented by a red dashed vertical line. (b) C.V. of the posterior distributions of total abundance obtained from DM model M5. Each blue dot corresponds to the C.V. of a unique year/replicate, and each dashed blue horizontal line corresponds to the average C.V. of a replicate across years. The average C.V. across years and replicates (red dashed line) is also indicated in the top-right corner of the panel.
F I G U R E 4 in Estimating multiple years, tributary-specific, and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture-mark-recapture experiments
F I G U R E 4 (a–c) Posterior distribution of the relative contribution of the smolt abundance associated with each upstream rotary screw trap (RST) and (d) posterior distribution of the relative contribution of the "rest" of the smolt abundance in relation to the total abundance for the DM (Dirichletmultinomial) model M5 (simulation exercise). The red dots correspond to the true values in the simulated dataset.
F I G U R E 1 in Estimating multiple years, tributary-specific, and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture-mark-recapture experiments
F I G U R E 1 Restigouche River catchment map with main streams and lakes. Red dots indicate the location of the rotary screw traps (RST). Black polygons indicate subcatchments draining into upstream RSTs. Top-right panels: zoom on the location of the two downstream RSTs and example of a smolt with a streamer tag. Photo credit: Marie-Camille St-Amour.
F I G U R E 5 in Estimating multiple years, tributary-specific, and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture-mark-recapture experiments
F I G U R E 5 (left panels, a–e) Posterior distributions of the estimated annual catchability θt,k at each rotary screw trap (RST), (middle panels, f– h) posterior distributions of the annual smolt abundance estimates associated with each upstream RST (Nmt,i), (i) the total smolt abundance for the Restigouche catchment (Nmtot), and (right panels, j–m) posterior distributions of the annual relative proportions of the total smolt abundance t of the Restigouche catchment estimated for each upstream RST and for the unsampled areas (rest, bottom-right panel) using the Restigouche dataset. In the catchability panels (a–e), the dashed gray line is the median, and the light and dark gray areas indicate the 2.5th–97.5th and 25th– 75th interquantile ranges, respectively, of the hyperparameter μθ, that is, the average catchability of each RST of the time series. In the relative k proportions panels (f–i) the thick dashed colored line in the top three panels indicates the average proportion of the total catchment abundance estimated at the upstream RST. The thick dashed gray lines indicate the proportions of the Restigouche catchment wetted areas of the subbasins sampled at the upstream RST(j–l) and the wetted area for the remainder of the Restigouche catchment not sampled by any upstream RST (m); the proportions for the rest of the catchment area change over years based on the specific upstream RSTs that operated in a given year. For all panels, the colored dot is the median, and the thin and thick vertical segments indicate the 2.5th–97.5th and 25th–75th interquantile ranges, respectively.
Neural Network predictions and ERA5 reference of integrated water vapour, and temperature and specific humidity profiles based on simulated microwave radiometer observations
<p>This data set contains predictions of the Neural Network retrievals described in <strong>[1]</strong>, where simulated microwave radiometer observations (brightness temperatures, TBs) from the evaluation data subset of <strong>[2]</strong> (years 2001, 2006, 2011, 2015) were used as input to the Neural Network. As described in Section 3.2 of <strong>[1]</strong>, we trained an ensemble of 20 Neural Networks for each retrieved atmospheric quantity and applied them to the ERA5 evaluation data set to estimate the robustness of the retrievals with respect to random perturbations. The following atmospheric quantities were retrieved: </p> <ul> <li>temperature profile (variable name 'temp_p', filename suffix 'temp_test_417'),</li> <li>boundary layer temperature profile (variable name 'temp_p', filename suffix 'temp_test_424'),</li> <li>specific humidity profile (variable name 'q_p', filename suffix 'q_test_472'),</li> <li>integrated water vapour (variable name 'iwv_p', filename suffix 'iwv_test_126')</li> </ul> <p>The cryptic 3-digit filename suffixes represent different settings of the Neural Network retrieval. More information can be found in <strong>[3]</strong>. Variables that do not have the "_p" suffix are ERA5 data and used as reference to estimate errors of the retrievals by comparing them with the predictions. The dimension 'n_s' represents the ERA5 data sample number while the dimension 'n_rand' designates the ensemble of Neural Networks.</p> <p>These files can be created when running run_NN_retrieval (contained in NN_retrieval.py, see <strong>[3]</strong>) with exec_type='20_runs' and eval_mode=True and test_id either "126", "417", "424" or "472". However, as this might take some hours, we provide them here.</p> <p> </p> <p><strong>[1]:</strong> Walbröl, A., Griesche, H. J., Mech, M., Crewell, S., and Ebell, K.: Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, Atmospheric Measurement Techniques, 17, 6223-6245, https://doi.org/10.5194/amt-17-6223-2024, 2024.</p> <p><strong>[2]:</strong> Walbröl, A., and Mech, M.: ERA5 based training, validation and evaluation data for retrievals combining 22-58 GHz with 175-340 GHz microwave radiometer measurements during MOSAiC (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.10997365, 2024.</p> <p><strong>[3]: </strong>Walbröl, A.: Codes for: Combining low and high frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products (1.0.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.11123136" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11123136</a>, 2024.</p>
Specification-Driven Code Translation By Large Language Models: How Far Are We?
<p>The artifacts and dataset for "Specification-Driven Code Translation By Large Language Models: How Far Are We?"</p>
YY1 mutations disrupt corticogenesis through a cell type-specific rewiring of cell-autonomous and non-cell-autonomous transcriptional programs
<p>This supplementary data includes counts from bulk and pseudobulk omic experiments, h5ad for single-cell experiments, and outputs of differential expression and enrichments performed on different omics assays.</p>
A development-specific POLX essential for programmed genome rearrangement in Paramecium tetraurelia
<p><span>During the sexual cycle, programmed genome rearrangement (PGR) in <em>Paramecium tetraurelia</em> involves the non-homologous end joining (NHEJ) DNA repair pathway to eliminate specific germinal Internal Eliminated Sequences (IESs) from the newly developing somatic nucleus. In addition to the core NHEJ factors Ku70/80 and Xrcc4/Lig4, additional enzymes are required to process the 4-base 5’-protruding ends generated following DNA cleavage at IES boundaries, prior to their ligation. Here, we report that PolX (a,b,c,d), four <em>P. tetraurelia</em> distant orthologs of the human Pol</span><span>l</span><span> DNA polymerase, are involved in repair of IES excision junctions. During rearrangements, PolX-depleted cells accumulate genome-wide errors, such as unrepaired double-strand breaks, 1-nucleotide deletions and IES retention. Although all PolX paralogs can process DNA ends, two of them (PolXa&b) are induced during PGR and have acquired tight nuclear anchoring properties through their N-terminal region, which contains a predicted BRCT domain. Finally, we show that PolXa accumulates in nuclear foci together with other NHEJ proteins and the Dicer-like enzyme Dcl5, which is involved in the biogenesis of IES-specific small RNAs. We propose</span><span><span> </span></span><span>that these “DNA repair foci” correspond to the sites where IES concatemers, a by-product of IES excision, are ligated together to produce the precursors of iesRNAs.</span></p>
Sex-specific trunk movement coordination in participants with low-back pain and asymptomatic controls
<p>This is the data set and the code used for our publication. </p>
Gene expression and splicing counts from 49 tissues from GTEx v6p genome build hg19 - non-strand specific
<p><strong>Dataset description:</strong></p> <p>49 folders, each corresponding to one tissue from GTEx v6p and containing the following files:</p> <ol> <li> <p>geneCounts: gene-level counts </p> </li> <li> <p>k_j: split counts spanning from one exon to another.</p> </li> <li> <p>k_theta: non-split counts covering a splice site</p> </li> <li> <p>n_psi3: total split counts from a given acceptor site</p> </li> <li> <p>n_psi5: total split counts from a given donor site</p> </li> <li> <p>n_theta: total split and non-split counts for a given splice site</p> </li> <li> <p>Sample annotation describing each sample from the dataset</p> </li> <li> <p>Description file with global information from the dataset</p> </li> </ol> <p>The gene counts were originated using the GTF file from <a href="http://www.gencodegenes.org/human/release_29lift37.html">release 29 of GENCODE</a>, and the split and non-split counts contain only the annotated junctions from the same release. Statistics are reported only for GENCODE-annotated introns and splice sites, in compliance with the regulations of the GTEx consortium. For a description of the samples, methods, and protocols, see the GTEx publication specified below.</p> <p><strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, the <a href="https://github.com/gagneurlab/drop">Detection of RNA outliers Pipeline (DROP) </a> can be used to compute gene expression and splicing outliers.<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode29<br> <strong>Strand specific: </strong>FALSE<br> <strong>Paired end: </strong>TRUE<br> <strong>Protocol: </strong>poly(A) enrichment</p> <p><strong>Contact:</strong> Vicente A. Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de</p> <p><strong>Citation:</strong> Write the following in the "Data availability" section of the manuscript or similar replacing the three citations by the ones from the References section below:</p> <blockquote> <p><strong>The count matrices for the GTEx samples <cite GTEx publication, see below> were downloaded from Zenodo (doi: 10.5281/zenodo.5596755) and were generated through DROP <cite DROP, see below> using the release 29 of the GENCODE annotation <cite GENCODE, see below>. </strong></p> </blockquote> <p>Also, write the following in the Acknowledgements section:<br> </p> <blockquote> <p><strong>The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The raw data used for the analyses described in this manuscript were obtained from the GTEx Portal on June 12, 2017, under accession number dbGaP phs00424.v6.p1.</strong></p> </blockquote> <p><br> </p>
Summary statistics from "Sex-Specific Causal Relations between Steroid Hormones and Obesity—A Mendelian Randomization Study"
<p>GWAMA summary statistics of four steroid hormone levels and one steroid hormone ratio using fixed-effect model.</p> <p>When using this data, please cite: Pott J, Horn K, Zeidler R, et al.. Sex-Specific Causal Relations between Steroid Hormones and Obesity - A Mendelian Randomization Study. <em>Metabolites</em> <strong>2021</strong>, <em>11</em>, 738. https://doi.org/10.3390/metabo11110738</p> <p>All txt files contain the following columns:</p> <ul> <li>markername</li> <li>chr</li> <li>bp_hg19 (base position according to hg19)</li> <li>ea (effect allele)</li> <li>oa (other allele)</li> <li>eaf (effect allele frequency)</li> <li>info (minimal info score across all used studies)</li> <li>nSamples (sample size per SNP)</li> <li>nStudies (number of studies)</li> <li>beta (effect estimate)</li> <li>se (standard error)</li> <li>p (p-value)</li> <li>I2 (SNP heterogeneity across studies)</li> <li>phenotype (phenotyp setting)</li> </ul>
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.