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30,947 results for “Profile”
CarbonTracker-Lagrange North America GOSAT Vertical Profile of Footprints V1 (CMS_CTL_NA_GOSAT_FOOTPRINTS)
This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data products for particle receptors co-located with atmospheric column observations from the GOSAT satellite. Meteorological fields from the WRF model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the "receptor" location), to create the adjoint of the transport model in the form of a "footprint" field. The footprint, with units of mixing ratio per surface flux, quantifies the influence of upwind surface fluxes on greenhouse gas concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. For each column observation location, the receptors are located at 23 discrete vertical levels throughout the atmospheric column. The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.
CarbonTracker-Lagrange South America OCO-2 Vertical Profile of Footprints V1 (CMS_CTL_SA_OCO2_FOOTPRINTS)
This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data products for particle receptors co-located with atmospheric column observations from the OCO-2 satellite. Meteorological fields from the WRF model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the "receptor" location), to create the adjoint of the transport model in the form of a "footprint" field. The footprint, with units of mixing ratio per surface flux, quantifies the influence of upwind surface fluxes on greenhouse gas concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. For each column observation location, the receptors are located at 14 discrete vertical levels throughout the atmospheric column. The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.
Raw and normalized count data for "Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling"
<p>SingleCellExperiment objects containing raw and normalized counts, as well as reduced dimension representations and cell type annotations for both the follicular lymphoma samples (sce_follicular_annotated_final.rds) and high grade serous ovarian cancer samples (sce_hgsc_annotated_final.rds) as detailed in the paper, as well as reactive lymph node data (sce_RLN_normalized.rds).</p>
Fig. 55. Batomorphii indet., teeth. A–E. MSC 37675.2, basal Lisbon Formation. A. Labial view. B. Lingual view. C. Profile view. D. Oral view. E. Basal view. F–J. MSC 37675.1, basal Lisbon Formation. F. Labial view. G. Lingual view. H. Profile view. I. Oral view. J. Basal view. K–O in Taxonomy and biostratigraphy of the elasmobranchs and bony fishes (Chondrichthyes and Osteichthyes) of the lower-to-middle Eocene (Ypresian to Bartonian) Claiborne Group in Alabama, USA, including an analysis of otoliths
Fig. 55. Batomorphii indet., teeth. A–E. MSC 37675.2, basal Lisbon Formation. A. Labial view. B. Lingual view. C. Profile view. D. Oral view. E. Basal view. F–J. MSC 37675.1, basal Lisbon Formation. F. Labial view. G. Lingual view. H. Profile view. I. Oral view. J. Basal view. K–O. SC2012.42.154, basal Lisbon Formation. K. Labial view. L. Lingual view. M. Profile view. N. Oral view. O. Basal view. Labial at top in oral and basal views. Labial at top in oral and basal views. Scale bars = 1 mm.
Fig. 67. Balistidae Risso, 1810, tooth. A–D. MSC 2402, Gosport Sand. A. Profile view. B. Basal view. C. Oral view. D in Taxonomy and biostratigraphy of the elasmobranchs and bony fishes (Chondrichthyes and Osteichthyes) of the lower-to-middle Eocene (Ypresian to Bartonian) Claiborne Group in Alabama, USA, including an analysis of otoliths
Fig. 67. Balistidae Risso, 1810, tooth. A–D. MSC 2402, Gosport Sand. A. Profile view. B. Basal view. C. Oral view. D. Anterior view. Anterior at left in A–C. Scale bar = 5 mm.
Fig. 41. Aturobatis aff. A. aquensis Adnet, 2006, teeth. A–D. MSC 37687, basal Tallahatta Formation. A. Orolingual view. B. Labial view. C. Oral view. D. Profile view. E–H. MSC 38464, basal Tallahatta Formation. E. Orolingual view. F. Labial view. G. Oral view. H in Taxonomy and biostratigraphy of the elasmobranchs and bony fishes (Chondrichthyes and Osteichthyes) of the lower-to-middle Eocene (Ypresian to Bartonian) Claiborne Group in Alabama, USA, including an analysis of otoliths
Fig. 41. Aturobatis aff. A. aquensis Adnet, 2006, teeth. A–D. MSC 37687, basal Tallahatta Formation. A. Orolingual view. B. Labial view. C. Oral view. D. Profile view. E–H. MSC 38464, basal Tallahatta Formation. E. Orolingual view. F. Labial view. G. Oral view. H. Profile view. Scale bars = 5 mm.
Systematic profiling of full-length immunoglobulin and T-cell receptor repertoire diversity in rhesus macaque through long read transcriptome sequencing
<p>Using long read sequencing, we sequenced four Indian-origin rhesus macaque tissues. From raw full-length, non-chimeric circular consensus sequencing (CCS) reads, we obtained high quality, full-length sequences for over 6,000 unique immunoglobulin and T-cell receptor transcripts, without the need for sequence assembly.</p>
Single cell and immune repertoire profiling of COVID-19 patients reveal novel therapeutic candidates
<p>COVID-19, a novel pneumonia caused by SARS-CoV-2, has rapidly become the largest threat to public health. Recent studies reported lymphocytopenia but also expansions of antigen-specific T/B cells during early-recovery stage. Here we investigated 16 early-recovery patients using scRNA-seq, HLA-genotyping and deep immune repertoire profling. Our analysis revealed 916 COVID-19-specific TCR groups enriched for a novel cytotoxic CD8+ phenotype with both resident memory (ZNF683+) and tissue exit (SIRP5) markers, and identified 114 statistically-confident virus epitopes. Further, we uncovered 374 BCR groups with somatic hypermutations and/or class switch recombinations. Molecular dynamics simulations revealed 15% of the highly expanded groups may serve as neutralizing antibodies against the virus Spike protein. Finally, we discovered that bacterial infection in COVID-19 patients may elevate monocytic myeloidderived suppressor cells via TLR4/NF!B signaling and cause disease aggravation. Together, we expect our findings and immune-receptor datasets to provide immediate therapeutic options against the pandemic of COVID-19.</p>
Epigenomic profiling of TASOR in Hela cells using ChIP, CUT&RUN and CUT&Tag
<p>Example bigwig coverage tracks illustrating TASOR-regulated H3K9me3 and TASOR genome binding.</p> <p>Mapped to hg38.</p> <p>For detailed methods, see linked preprint.</p>
The transcriptomic profiling of COVID-19 compared to SARS, MERS, Ebola, and H1N1
<p><strong>COVID-19 </strong>pandemic is a global crisis that threatens our way of life. As of April 29, 2020, COVID-19 has claimed more than 200,000 lives, with a global mortality rate of ~7% and recovery rate of ~30%. Understanding the interaction of cellular targets to the SARS-CoV2 infection is crucial for therapeutic development. Therefore, the aim of this study was to perform a comparative analysis of transcriptomic signatures of infection of COVID-19 compared to different respiratory viruses (Ebola, H1N1, MERS-CoV, and SARS-CoV), to determine unique anti-COVID1-19 gene signature. We identified for the first time molecular pathways for Heparin-binding, RAGE, miRNA, and PLA2 inhibitors, to be associated with SARS-CoV2 infection. The <em>NRCAM</em> and <em>SAA2</em> that are involved in severe inflammatory response, and <em>FGF1</em> and <em>FOXO1</em>genes, which are associated with immune regulation, were found to be associated with a cellular gene response to COVID-19 infection. Moreover, several cytokines, most significantly the <em>IL-8</em>, <em>IL-6</em>, demonstrated key associations with COVID-19 infection. Interestingly, the only response gene that was shared between the five viral infections was <em>SERPINB1</em>. The PPI study sheds light on genes with high interaction activity that COVID-19 shares with other viral infections. The findings showed that the genetic pathways associated with Rheumatoid arthritis, AGE-RAGE signaling system, Malaria, Hepatitis B, and Influenza A were of high significance. We found that the virogenomic transcriptome of infection, gene modulation of host antiviral responses, and GO terms of both COVID-19 and Ebola are more similar compared to SARS, H1N1, and MERS. This work compares the virogenomic signatures of highly pathogenic viruses and provides valid targets for potential therapy against COVID-19.</p> <p><strong>Supplementary tables and figures</strong></p> <p><strong>Figure 1</strong> : Significant DEGs across the five transcriptomic profiles , corresponding genes, chromosome locations, gene expression and significance scores. The DEGs related genes and chromosomal location (A). The DEGs information regarding host response to COVID-19 (B), Ebola (C), MERS-CoV (D) , H1N1 (E) and SARS-CoV (F) viral infections. The pvalues were scaled were scaled across gene profiles according to maximum and minimum values (ppvalue). The circles size and color is linked to DEGs significance and gene expression (LogFC) scores, respectively.</p> <p><strong>Figure 2 : </strong>Analysis of the gene enrichment of DEGs correlated with the host response to COVID-19. Categories of GO terms (A), significance scores (-10log-pvalue) (B), and number of associated DEGs (C). The COVID-19-associated DEGs (D), status across the studied infectious diseases (E), and selected linked GO terms (F).</p> <p><strong>Figure 3: </strong>The Venn diagram of viral associated genes. The number of uniquely shared genes associated with the host response to COVID-19, Ebola, H1N1, MERS-CoV, and SARS-CoV viral infections.</p> <p><strong>Figure 4: </strong>The Venn diagram of viral associated GO terms. The number of uniquely shared GO terms of DEGs associated with the host response across COVID-19, Ebola, H1N1, MERS-CoV, and SARS-CoV viral infections.</p> <p><strong>Figure 5: </strong>The PPIs network of DEGs associated with COVID-19. The PPI of host expressed DEGs under COVID-19 infection. DEGs shared between COVID-19 and Ebola, H1N1, MERS-CoV, and SARS-CoV are color-coded according to kind of infection. The gene node size is relative to its interaction activity. DEGs are collected in different groups according to their level of interaction activity.<br> </p> <p><strong>Figure 6: </strong>The PPIs network and gene enrichment analysis of highly interactive genes associated with COVID-19.</p> <p><strong>Figure S1 :</strong> The PPI network and gene enrichment analysis of the 173 genes that characterized the host response of COVID-19.</p> <p><strong>Figure S2: </strong>The PPI network and gene enrichment analysis of the 58 genes that are uniquely shared between COVID -19 and Ebola viral infections .</p> <p><strong>Figure S3 : </strong>The PPI network and gene enrichment analysis of the 51 genes that are uniquely shared between COVID-19 and MERS-CoV viral infections.</p> <p><strong>Figure S4 : </strong>The PPI network and gene enrichment analysis of the 31 genes that are uniquely shared between COVID-19, Ebola, and MERS-CoV viral infections.</p> <p><strong>Figure S5</strong> : The gene expression heatmap of genes COVID-19 shares with different viral infections.</p> <p><strong>Figure S6 : </strong>The PPI network and gene enrichment analysis of genes that are differentially expressed across studied viral infections and shared with COVID-19.</p> <p><strong>Table </strong><strong>S</strong><strong>1 : </strong>The data information used in this study.</p> <p><strong>Table S</strong><strong>2</strong>: The information of DEGs associated the host response of COVID-19, Ebola, H1N1, MERS-CoV, and SARS-CoV viral infections.</p> <p><strong>Table S</strong><strong>3</strong>: The Venn analysis results of DEGs and GO terms uniquely shared across of COVID-19, Ebola, H1N1, MERS-CoV, and SARS-CoV viral infections.</p> <p><strong>Table S</strong><strong>4</strong>: Selected gene enrichment analysis of uniquely shared group of genes across the host response of COVID-19, Ebola, H1N1, MERS-CoV, and SARS-CoV viral infections.</p> <p><strong>Table S</strong><strong>5</strong>: The gene expression information of DEGs that COVID-19 share with the studied infectious diseases.</p> <p><strong>Table S</strong><strong>6</strong>: Selected gene enrichment analysis of uniquely shared group of GO terms across the host response of COVID-19 and studied viral infections.</p>
Can we assume the gene expression profile as a proxy for signaling network activity? (Supplementary Materials)
<p>These are just supplementary files.</p>
Field and Genetic Profile Data for Grizzly Bear Hair-Snag Sampling and Scat Sampling along Roads in North America
<p>These datasets contain field or genetic profile data for grizzly bear hair-snag sampling and scat sampling along roads in North America. See method details in the published article: Phoebus et al. 2020. A Comparison of Grizzly Bear Hair-Snag Sampling and Systematic Scat Sampling along Roads to Inform Wildlife Population Monitoring. Wildlife Biology, WLB-00697. </p> <p>An inadequate sample in the field data means that the particular lab test failed (i.e. species, sex or individual identification test) likely due to poor sample quality (i.e. too little DNA in the sample). </p>
Gradients1-KOK1606-Hyperpro-Profiles_2020-03-25_v1.2
<p>Cruise: KOK1606, Gradients 1</p> <p>Project Name: Simons Foundation, Gradients NPSG</p> <p>Description:</p> <p>The Satlantic (now Seabird) HyperPro is an in-situ free-fall profiling unit designed to measure the apparent optical properties of the ocean with concurrent measurements of temperature, salinity, chlorophyll and dissolved matter fluorescence, and optical backscattering (a proxy for total particle load). The unit is equipped with one up-looking and one down-looking hyperspectral (350-800nm) radiometer with 10 ± 0.3 nm resolution, a WET Labs ECO-Puck Triplet, and temperature, pressure, and conductivity sensors. It also incorporates a ship-mounted surface (air) hyperspectral radiometer. These data are collated from the vertical profiles conducted during the Gradients I cruise.</p>
Characterizing Energy Consumption of Third-Party API Libraries using API Utilization Profiles (ESEM'2020 Dataset)
<p><strong>Motivation</strong></p> <p>This repository contains the data-set used as a basis for our ESEM'2020 paper <em>Characterizing Energy Consumption of Third-Party API Libraries using API Utilization Profiles</em> The dataset is comprised of 2 test scenarios: The first one is based on the paper from Rocha et al. (2019), the second one is based on the commonly known Google Gson library.</p> <p><strong>Description of the dataset</strong></p> <p>The dataset is stored in a file *.csv and contains the following data:</p> <ul> <li>id - an individual identifier</li> <li>name - the name of the library examined</li> <li>className - the class name as an abbreviation</li> <li>method - the name of the executed method</li> <li>duration - duration of method execution</li> <li>energyConsumption - computed energy consumption</li> <li>watts - recorded wattage</li> <li>uApi - the computed uAPI profile value</li> </ul> <p>Besides the data, this repository also contains the result images from the ESEM'2020 paper in pdf-file format.</p> <p><em><strong>Android I/O Experiment</strong></em></p> <p>For the Android I/O Experiment we examined the correlation between API utilization and energy consumption. For the dataset, we took inspiration from Rocha et al. (2019). The dataset uses abbreviations for the examined classes which are further described in the <em>readme.md</em> file</p> <ul> <li>Filename: io_test_052020.csv</li> </ul> <p><em><strong>Google Gson Experiment</strong></em></p> <p>The Google Gson dataset contains recordings for Google Gson version 2.8.5. Again, the dataset was used as a foundation for examining the correlation between the computed API profiles and energy consumption.</p> <ul> <li>Filename: gson_test_052020.csv</li> </ul> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p>
Electron density profiles from Autoscala and ISR for hmF2 assessment
<p>These data allow to compare hmF2 provided by the latest version of Autoscala (version 2018), and hmF2 produced by ARTIST with the correspondent obtained by ISR. This comparison is performed on a set of data recorded at Millstone Hill from April 1997 to December 2017.<br> The data and programs consider also other issues such as the results provided by the old version of Autoscala (version 2007), and the problem of comparing electronic density profiles. However, these analysis are not finalized. Only the comparison of hmF2 provided by the latest version of Autoscala, and hmF2 produced by ARTIST with the correspondent obtained through ISR is finalized.<br> </p>
Data from: Effects of 9,10-phenanthrenequione on antioxidant indices and metabolite profiles in Takifugu obscurus plasma
<p class="MDPI31text"><span><span>Derived from polycyclic aromatic hydrocarbons (PAHs), oxygenated-PAHs (oxy-PAHs) may pose hazards to </span></span><span><span>aquatic</span></span><span><span> organisms, which remains largely unknown</span></span>. <i><span><span>Takifugu obscurus</span></span></i><span><span> is an important anadromous fish species </span></span><span><span>of </span></span><span><span>high economic and ecological values. </span></span><span><span>In </span></span><span><span>t</span></span><span><span>he present study,</span></span><span><span> <i>T. obscurus</i> </span></span><span><span>was</span></span><span><span> acutely exposed to</span></span> <span><span>44.29 </span></span><span><span>μg/L</span></span><span><span> 9,10-phenanthrenequione (9,10-PQ) for 96 hours. </span></span><span><span>Change</span></span><span><span>s of antioxidant indices and metabolite profiles in plasma were compared between 9,10-PQ treatment and the control. The results showed that 9,10-PQ treatment significantly increased MDA content during 6 hours to 96 hours, increased SOD and CAT activities at 6 hours, but decreased them at 96 hours. These results indicated that 9,10-PQ induced oxidative stress to fish. </span></span><span><span>Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS)</span></span><span><span> analysis revealed that four</span></span><span><span> metabolic pathways were influenced in response </span></span><span><span>to treatment with 9,10-PQ, including </span></span><span><span>glycerophospholipid metabolism</span></span><span><span>,</span></span><span><span> phenylalanine, tyrosine and tryptophan biosynthesis</span></span><span><span>,</span></span><span><span> purine metabolism and sulfur metabolism. These pathways </span></span><span>are </span><span>associated with </span><span>antioxidant mechanisms, biosynthesis of </span><span>neurotransmitters and innate immune functions. Thus, the as-obtained results confirmed that 9,10-PQ induced</span> oxidative stress and raised concerns of neurotoxicity and immunotoxicity to fish. <span>Overall, the present study posed a high environmental risk of oxy-PAHs to aquatic ecosystems. </span><span><span>Derived from polycyclic aromatic hydrocarbons (PAHs), oxygenated-PAHs (oxy-PAHs) may pose hazards to </span></span><span><span>aquatic</span></span><span><span> organisms, which remains largely unknown</span></span>. <i><span><span>Takifugu obscurus</span></span></i><span><span> is an important anadromous fish species </span></span><span><span>of </span></span><span><span>high economic and ecological values. </span></span><span><span>In </span></span><span><span>t</span></span><span><span>he present study,</span></span><span><span> <i>T. obscurus</i> </span></span><span><span>was</span></span><span><span> acutely exposed to</span></span> <span><span>44.29 </span></span><span><span>μg/L</span></span><span><span> 9,10-phenanthrenequione (9,10-PQ) for 96 hours. </span></span><span><span>Change</span></span><span><span>s of antioxidant indices and metabolite profiles in plasma were compared between 9,10-PQ treatment and the control. The results showed that 9,10-PQ treatment significantly increased MDA content during 6 hours to 96 hours, increased SOD and CAT activities at 6 hours, but decreased them at 96 hours. These results indicated that 9,10-PQ induced oxidative stress to fish. </span></span><span><span>Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS)</span></span><span><span> analysis revealed that four</span></span><span><span> metabolic pathways were influenced in response </span></span><span><span>to treatment with 9,10-PQ, including </span></span><span><span>glycerophospholipid metabolism</span></span><span><span>,</span></span><span><span> phenylalanine, tyrosine and tryptophan biosynthesis</span></span><span><span>,</span></span><span><span> purine metabolism and sulfur metabolism. These pathways </span></span><span>are </span><span>associated with </span><span>antioxidant mechanisms, biosynthesis of </span><span>neurotransmitters and innate immune functions. Thus, the as-obtained results confirmed that 9,10-PQ induced</span> oxidative stress and raised concerns of neurotoxicity and immunotoxicity to fish. <span>Overall, the present study posed a high environmental risk of oxy-PAHs to aquatic ecosystems. </span></p>
CRC tumor profiled in Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling
<p>Plain text files from mgatk output of CRC tumor sample originally described in "Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling" (https://doi.org/10.1038/s41587-020-0645-6). </p>
Selected data for Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling
<p>TF1 cell line (Figure 3) and CRC cancer (Figure 4) data from the "Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling" work. </p>
Transcriptome profiling of potato (Solanum tuberosum L.) responses to root-knot nematode (Meloidogyne javanica) infection during a compatible interaction
<p>Supplementary data</p>
Integrative analyses of single-cell transcriptome and immune profiling reveal clonal expansion of T cells in the blood and cerebrospinal fluid of Parkinson's disease
<p>An increasing number of studies has indicated that the immune system plays important roles in the pathogenesis of Parkinson's disease. However, little is known about the contribution of adaptive immune responses in Parkinson's disease. Here, we performed comprehensive integrative analyses of single-cell transcriptome and immune profiling of the blood of 8 Parkinson's patients and 13 healthy controls as well as the cerebrospinal fluid of 6 Parkinson's patients, 4 Alzheimer's patients, 5 mild cognitive impairment (MCI) patients and 9 healthy controls. In total, 22 T cell subsets with distinct functions and clonalities were identified from 121,402 T cells. We observed significant clonal expansion of effector CD8+ T cells in Parkinson's patients, which formed a gradient of transcriptional states from central memory CD8+ T cells to early effector CD8+ T cells followed by terminal effector CD8+ T cells. Shared TCRs in this progression suggest TCRs may be involved in the state transition of CD8+ T cells stimulated by antigens. Notably, we also found that a group of clonally expanded cytotoxic CD4+ T cells were significantly increased in Parkinson's patients compared to controls, suggesting their cytotoxic roles in Parkinson's disease. Finally, we screened putative TCR-antigen pairs that existed in both blood and cerebrospinal fluid of patients with Parkinson's disease. These results reveal an adaptive immune response in the blood and cerebrospinal fluid of Parkinson's disease and provide novel evidence of clonal, antigen-experienced T cells patrolling in the blood and cerebrospinal fluid of Parkinson's disease.</p>
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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.