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R code and data for: How much is enough? Minimum sample sizes in community ecology
<p>minimum_sample_sizes_code.R includes the complete set of instructions used to carry out the analyses in the related manuscript, plus save the computed data files and generate the text figures. data_files.tar.gz includes all of the files generated by the R code.</p>
Merged anvio profiles for Wolbachia MAGs from Culex pipiens midgut and ovary samples
<p>Anvi’o merged profile databases for <em>Wolbachia</em> MAGs from <em>Culex pipiens</em> midgut and ovary metagenomes. </p>
single cell RNA seq data of 3 healthy sample from frontal lobe and temporal lobe
<p><span>Frontotemporal lobe abnormalities are linked to neuropsychiatric disorders and cognition, but the role of cellular heterogeneity between temporal lobe (TL) and frontal lobe (FL) in the vulnerability to genetic risk factors remains to be elucidated. We provided single-nucleus transcriptome analysis in “fresh” human FL and TL which are integrated with genetic susceptibility, gene dysregulation in neuropsychiatric disease, and psychoactive drug response data. We show how intrinsic differences between TL and FL contribute to the vulnerability of specific cell types to both genetic risk factors and psychoactive drugs. Neuronal populations, specifically PVALB-neurons, were most highly vulnerable to genetic risk factors for psychiatric disease. These psychiatric disease-associated genes were mostly upregulated in the TL, and dysregulated in the brain of patients with obsessive-compulsive disorder, bipolar disorder and schizophrenia. these data provide prefound insight into brain frontotemporal lobe.</span></p>
Рис. 2. Распределение Значений биомассы и численности Macoma balthica по станциЯм отбора проб. Fig. 2. Distribution of the Macoma balthica biomass and abundance values at sampling stations. in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer
Рис. 2. Распределение Значений биомассы и численности Macoma balthica по станциЯм отбора проб. Fig. 2. Distribution of the Macoma balthica biomass and abundance values at sampling stations.
Рис. 4. Распределение станций отбора проб по глубине и типу грунта (круЖком обведены станции, на которых макробентос не обнаруЖен; БО – биогенные остатки, ГМ – галька мелкаЯ, Гр – гравий, И – ил, П – песок). Fig. 4. Distribution of sampling stations by depth and type of bottom sediments (circles are around the stations where no macrobenthos was detected; БО – biogenic residues, ГМ – pebbles, Гр – gravel, И – silt, П – sand). in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer
Рис. 4. Распределение станций отбора проб по глубине и типу грунта (круЖком обведены станции, на которых макробентос не обнаруЖен; БО – биогенные остатки, ГМ – галька мелкаЯ, Гр – гравий, И – ил, П – песок). Fig. 4. Distribution of sampling stations by depth and type of bottom sediments (circles are around the stations where no macrobenthos was detected; БО – biogenic residues, ГМ – pebbles, Гр – gravel, И – silt, П – sand).
Рис. 5. АналиЗ линейной коррелЯции параметров макробентоса от доминируюЩей фракции в пробе грунта (А, Б) и глубины (В, Г). Fig. 5. Analysis of the linear correlation of macrobenthos parameters with the dominant fraction in the bottom sample (А, Б) and depth (В, Г). in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer
Рис. 5. АналиЗ линейной коррелЯции параметров макробентоса от доминируюЩей фракции в пробе грунта (А, Б) и глубины (В, Г). Fig. 5. Analysis of the linear correlation of macrobenthos parameters with the dominant fraction in the bottom sample (А, Б) and depth (В, Г).
Рис.1. Карта-схема района исследований. ● – станции отбора планктонных и бентосных проб. Fig.1. A schematic map of the studied area. ● – sampling stations. in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer
Рис.1. Карта-схема района исследований. ● – станции отбора планктонных и бентосных проб. Fig.1. A schematic map of the studied area. ● – sampling stations.
Рис. 2. Распредение биомассы Mytilus trossulus septentrionalis на литорали дальневоcточных морей России. Здесь и далее на гистограммах по оси абцисс после географических пунктов в скобках укаЗана выборка (число иЗученных проб), по оси ординат – максимальные ЗначениЯ биомассы вида. Под Значением биомассы 0.1 г/м² подраЗумеваютсЯ качественные пробы. СокраЩениЯ (бмп) и (топ) оЗначают соответственно беринговоморское и тихоокеанское побережьЯ Восточной Камчатки. Побережье Зал. Петра Великого от устьЯ р. Туманной к северу до м. Поворотного условно отноcитсЯ к южному Приморью; побережье к северу от м. Поворотного (пос. Преображение, б. СоколовскаЯ) до б. Ольга, включительно, условно относитсЯ к среднему Приморью; побережье к северу от б. Ольга до м. Белкина и материковое побережье Татарского пролива относим к северному Приморью. Fig. 2. The distribution of biomass of Mytilus trossulus septentrionalis in the intertidal zone of the Far Eastern seas of Russia. Here and throughout on histograms, on the abcissa is the number of studied samples (numbers in parentheses following the names geographic localities), on the ordinate is the maximum biomass of species. The number 0.1 g wet wt m-2 means the qualitative samples. Abbreviations (bmp) and (top) mean the Bering Sea coast and the Pacific coast of eastern Kamchatka. The coast of Peter the Great Bay from the mouth of the Tumannaya River to Cape Povorotny is conditionally referred to as southern Primorye; the area north of Cape Povorotny (Preobrazhenie Settlement, Sokolovskaya Bay) to Olga Bay inclusive is conditionally referred to as middle Primorye; north of Olga Bay to Cape Belkin and the mainland coast of the Tatar Strait to as northern Primorye. in Bivalve mollusks of the intertidal zone of the Far Eastern seas of Russia
Рис. 2. Распредение биомассы Mytilus trossulus septentrionalis на литорали дальневоcточных морей России. Здесь и далее на гистограммах по оси абцисс после географических пунктов в скобках укаЗана выборка (число иЗученных проб), по оси ординат – максимальные ЗначениЯ биомассы вида. Под Значением биомассы 0.1 г/м² подраЗумеваютсЯ качественные пробы. СокраЩениЯ (бмп) и (топ) оЗначают соответственно беринговоморское и тихоокеанское побережьЯ Восточной Камчатки. Побережье Зал. Петра Великого от устьЯ р. Туманной к северу до м. Поворотного условно отноcитсЯ к южному Приморью; побережье к северу от м. Поворотного (пос. Преображение, б. СоколовскаЯ) до б. Ольга, включительно, условно относитсЯ к среднему Приморью; побережье к северу от б. Ольга до м. Белкина и материковое побережье Татарского пролива относим к северному Приморью. Fig. 2. The distribution of biomass of Mytilus trossulus septentrionalis in the intertidal zone of the Far Eastern seas of Russia. Here and throughout on histograms, on the abcissa is the number of studied samples (numbers in parentheses following the names geographic localities), on the ordinate is the maximum biomass of species. The number 0.1 g wet wt m-2 means the qualitative samples. Abbreviations (bmp) and (top) mean the Bering Sea coast and the Pacific coast of eastern Kamchatka. The coast of Peter the Great Bay from the mouth of the Tumannaya River to Cape Povorotny is conditionally referred to as southern Primorye; the area north of Cape Povorotny (Preobrazhenie Settlement, Sokolovskaya Bay) to Olga Bay inclusive is conditionally referred to as middle Primorye; north of Olga Bay to Cape Belkin and the mainland coast of the Tatar Strait to as northern Primorye.
РИС. 5. ЗагрЯЗнение готовых обраЗцов длЯ СЭМ при длительном хранении в негерметичных условиЯх (A–C) либо при хранении проШедШих процедуру мацерированиЯ беЗ последуюЩего обеЗЗараживаниЯ (D, E). A–С. Бактерии на поверхности глохидиев (Nodularia douglasiae, р. ИлистаЯ, бассейн оЗ. Ханка, Приморский кр.). А. ВнеШний вид глохидиЯ, основное ЗагрЯЗнение на створке в верхней части фото. В. Крючок глохидиЯ, основное ЗагрЯЗнение в левой части фото. С. Створка, вид иЗнутри. D. Единичные бактерии на створке глохидиЯ, вид иЗнутри (Kunashiria japonica, оЗ. Утиное, о-в Зелёный, Курильские о-ва). E. Гифы гриба на створке глохидиЯ, вид на наружную пору (Beringiana beringiana, оЗ. АЗабачье, Камчатка). МасШтаб 50 мкм (А, C), 10 мкм (В, D), 1 мкм (Е). Микроскопы Zeiss MERLIN (А, B, C, E), Zeiss EVO 40 (D), напыление хромом (А–С), Золотом (D), углеродом (Е). FIG. 5. Contamination of the SEM ready-made samples during long-term storage under unsealed conditions (A–C) or during storage the samples that have passed the maceration procedure without subsequent disinfection (D, E). A–C. Bacteria on the glochidia surface (Nodularia douglasiae, Ilistaya River, Khanka Lake basin, Primorsky Krai). A. Glochidium with the main pollution on the valve in the upper part of the photo. B. Hook with the main pollution on the left side of the photo. C. Interior valve. D. Bacteria on the interior valve (Kunashiria japonica, Utinoe Lake, Zeliony Island, Kuril Islands). E. Fungal hyphae on the pore of exterior valve (Beringiana beringiana, Azabachye Lake, Kamchatka). Scale bars 50 μm (A, C), 10 μm (B, D), 1 μm (E). Zeiss MERLIN (A, B, C, E) and Zeiss EVO 40 (D) microscopes, sputter coating with chromium (A–C), gold (D), and carbon (E). in Методика подготовки раковин глохидиев (Bivalvia, Unionidae) длЯ работы на сканируюЩем Электронном микроскопе
РИС. 5. ЗагрЯЗнение готовых обраЗцов длЯ СЭМ при длительном хранении в негерметичных условиЯх (A–C) либо при хранении проШедШих процедуру мацерированиЯ беЗ последуюЩего обеЗЗараживаниЯ (D, E). A–С. Бактерии на поверхности глохидиев (Nodularia douglasiae, р. ИлистаЯ, бассейн оЗ. Ханка, Приморский кр.). А. ВнеШний вид глохидиЯ, основное ЗагрЯЗнение на створке в верхней части фото. В. Крючок глохидиЯ, основное ЗагрЯЗнение в левой части фото. С. Створка, вид иЗнутри. D. Единичные бактерии на створке глохидиЯ, вид иЗнутри (Kunashiria japonica, оЗ. Утиное, о-в Зелёный, Курильские о-ва). E. Гифы гриба на створке глохидиЯ, вид на наружную пору (Beringiana beringiana, оЗ. АЗабачье, Камчатка). МасШтаб 50 мкм (А, C), 10 мкм (В, D), 1 мкм (Е). Микроскопы Zeiss MERLIN (А, B, C, E), Zeiss EVO 40 (D), напыление хромом (А–С), Золотом (D), углеродом (Е). FIG. 5. Contamination of the SEM ready-made samples during long-term storage under unsealed conditions (A–C) or during storage the samples that have passed the maceration procedure without subsequent disinfection (D, E). A–C. Bacteria on the glochidia surface (Nodularia douglasiae, Ilistaya River, Khanka Lake basin, Primorsky Krai). A. Glochidium with the main pollution on the valve in the upper part of the photo. B. Hook with the main pollution on the left side of the photo. C. Interior valve. D. Bacteria on the interior valve (Kunashiria japonica, Utinoe Lake, Zeliony Island, Kuril Islands). E. Fungal hyphae on the pore of exterior valve (Beringiana beringiana, Azabachye Lake, Kamchatka). Scale bars 50 μm (A, C), 10 μm (B, D), 1 μm (E). Zeiss MERLIN (A, B, C, E) and Zeiss EVO 40 (D) microscopes, sputter coating with chromium (A–C), gold (D), and carbon (E).
A comparative study of commercially available, minimally invasive, sampling methods on Early Neolithic humeri analysed via palaeoproteomics
Open the record for dataset details and reuse information.
Discrete Flow Cytometry of Depth Profile Samples from the Gradients 5 (2023) Cruise Using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations from discrete flow cytometry data collected during the Gradients 2023 (Gradients 5/TN412) oceanographic research cruise in the equatorial Pacific Ocean. The data consists of cell abundance, cell size (equivalent spherical diameter), carbon quota, and carbon biomass for heterotrophic bacteria, picophytoplankton populations, namely the cyanobacteria Prochlorococcus and Synechococcus, and small eukaryotic phytoplankton (<5 μm ESD). Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton</p>
Data from: Global Spore Sampling Project: A global, standardized dataset of airborne fungal DNA
<p><span>Novel methods for sampling and characterizing biodiversity hold great promise for re-evaluating patterns of life across the planet. The sampling of airborne spores with a cyclone sampler, and the sequencing of their DNA, have been suggested as an efficient and well-calibrated tool for surveying fungal diversity across various environments. Here we present data originating from the Global Spore Sampling Project, comprising 2,768 samples collected during two years at 47 outdoor locations across the world. Each sample represents fungal DNA extracted from 24 m<sup>3</sup> of air. We applied a conservative bioinformatics pipeline that filtered out sequences that did not show strong evidence of representing a fungal species. The pipeline yielded 27,954 species-level operational taxonomic units (OTUs). Each OTU is accompanied by a probabilistic taxonomic classification, validated through comparison with expert evaluations. To examine the potential of the data for ecological analyses, we partitioned the variation in species distributions into spatial and seasonal components, showing a strong effect of the annual mean temperature on community composition.</span></p> <p><span>The database is organized in five datasets in a csv format (columns separated by commas): (1) metadata providing the location, date, and time for each sample, along with sequencing depth and other essential information (metadata.csv); (2) species-level OTU tables per sample describing the number of sequences assigned to each species (otu.table.csv 3); (3) taxonomic classification of each species-level OTU (taxonomy.csv); (4) closest matching sequences and their taxonomy for ASVs in putatively fungal pseudophyla, which are included in (2) and (3) (fungi_pseudophyla.csv); and (5) closest matching sequences and their taxonomy for ASVs in putatively non-fungal pseudophyla, which are not included in the other datasets (nonfungi_pseudophyla.csv). The first four datasets can be linked to each other using the unique sample codes and the unique identifiers for species-level OTUs. </span><span>The three first datafiles are also provided in allData.RData which can be read into R as load("allData.RData").</span></p>
Historical time-series reconstruction benchmark dataset of Landsat bi-monthly aggregates from GLAD ARD-2 at 30-m resolution with stratified sampling based on ESA CCI
<h2>Description</h2> <p>Historical time-series reconstruction benchmark dataset presented here is designed for evaluating and comparing the performance of time series reconstruction methods in the context of land cover change detection. The dataset is based on the European Space Agency Climate Change Initiative (ESA CCI) land cover dataset, which has been aggregated into 18 classes to facilitate analysis. The dataset includes information on land cover dynamics from 2000 to 2020, focusing on identifying and characterizing changes in land cover over time.</p> <h3><strong>Data Collection and Processing:</strong></h3> <p>The dataset is derived from the ESA CCI land cover dataset, which provides information on land cover classes at a global scale. The original dataset, containing 37 land cover classes, was aggregated into 18 classes based on similarity. Pixels with stable land cover over the study period and pixels with one or multiple land cover changes were identified and grouped into strata for sampling purposes.</p> <p>Sampling points were selected using a stratified sampling design, ensuring representation across different land cover classes and change scenarios. Approximately 2600 points were selected from each stratum, resulting in a total of 51,978 sampling points. The selected points were uniformly distributed along the strata, with spatial variations accounted for.</p> <p>Bimonthly time series data were extracted for each sampling point from 1997 to 2022, capturing temporal dynamics in land cover. Artificial gaps were introduced into the time series data to simulate real-world data loss, allowing for the evaluation of time series reconstruction methods under varying gap densities.</p> <p>The time series values were extracted from Landsat GLAD imagery using the specified spectral bands, including blue, green, red, NIR, SWIR1, SWIR2, and thermal bands. Additionally, a clear quality band was also extracted.</p> <h3>Data Details</h3> <ul> <li><strong>Time Period:</strong> 1997-01-01 to 2022-12-31</li> <li><strong>Type of Data: </strong>R data frame / Geopackage points.</li> <li><strong>Collection/Derivation:</strong> Derived from Landsat ARD v2, processed with Scikit-map.</li> <li><strong>Coordinate Reference System:</strong> EPSG:4326</li> <li><strong>Bounding Box:</strong> All the globe</li> <li><strong>File Format:</strong> RDS</li> </ul> <p> </p> <h3><strong>Reclassified Classes of ESA CCI Land Cover Dataset</strong></h3> <table> <tbody> <tr> <td> <div> <div> <p><strong>Aggregated Class Code</strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Aggregated Class Label</strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Original ESA CCI Classes</strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>10</p> </div> </div> </td> <td> <div> <div> <p>Cropland rainfed</p> </div> </div> </td> <td> <div> <div> <p>10, 11, 12</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>30</p> </div> </div> </td> <td> <div> <div> <p>Mosaic cropland | natural vegetation</p> </div> </div> </td> <td> <div> <div> <p>30, 40</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>50</p> </div> </div> </td> <td> <div> <div> <p>Tree cover broadleaved evergreen</p> </div> </div> </td> <td> <div> <div> <p>50</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>60</p> </div> </div> </td> <td> <div> <div> <p>Tree cover broadleaved deciduous</p> </div> </div> </td> <td> <div> <div> <p>60, 61, 62</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>70</p> </div> </div> </td> <td> <div> <div> <p>Tree cover needleleaved evergreen</p> </div> </div> </td> <td> <div> <div> <p>70, 71, 72</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>80</p> </div> </div> </td> <td> <div> <div> <p>Tree cover needleleaved deciduous</p> </div> </div> </td> <td> <div> <div> <p>80, 81, 82</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>90</p> </div> </div> </td> <td> <div> <div> <p>Tree cover mixed leaf type</p> </div> </div> </td> <td> <div> <div> <p>90</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>100</p> </div> </div> </td> <td> <div> <div> <p>Mosaic tree and shrub | herbaceous cover</p> </div> </div> </td> <td> <div> <div> <p>100, 110</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>120</p> </div> </div> </td> <td> <div> <div> <p>Shrubland</p> </div> </div> </td> <td> <div> <div> <p>120, 121, 122</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>150</p> </div> </div> </td> <td> <div> <div> <p>Sparse vegetation</p> </div> </div> </td> <td> <div> <div> <p>150, 151, 152, 153</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>160</p> </div> </div> </td> <td> <div> <div> <p>Tree cover flooded</p> </div> </div> </td> <td> <div> <div> <p>160, 170</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>180</p> </div> </div> </td> <td> <div> <div> <p>Shrub or herbaceous cover flooded</p> </div> </div> </td> <td> <div> <div> <p>180</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>200</p> </div> </div> </td> <td> <div> <div> <p>Bare areas</p> </div> </div> </td> <td> <div> <div> <p>200, 201, 202</p> </div> </div> </td> </tr> </tbody> </table> <p>In the table, each row represents a reclassified land cover class, identified by a unique code. The 'Original ESA CCI Classes' column lists the specific land cover classes from the European Space Agency Climate Change Initiative dataset that are grouped together to form each broader category. Note that land cover classes not listed in this table were retained in their original value and were not reclassified.</p> <h3><strong>File Format</strong></h3> <p>The dataset comprises observations spanning from January 1997 to November 2022, capturing data for 51,978 samples.</p> <ul> <li>blue.rds: Time series data for the blue spectral band.</li> <li>green.rds: Time series data for the green spectral band.</li> <li>red.rds: Time series data for the red spectral band.</li> <li>nir.rds: Time series data for the near-infrared (NIR) spectral band.</li> <li>swir1.rds: Time series data for the shortwave infrared 1 (SWIR1) spectral band.</li> <li>swir2.rds: Time series data for the shortwave infrared 2 (SWIR2) spectral band.</li> <li>thermal.rds: Time series data for the thermal infrared band.</li> <li>clear.rds: Time series data for the clear quality band, used for masking out cloudy observations.</li> </ul> <p>How open the files in R:</p> <p><code>blue <- readRDS("blue.rds")</code></p> <p>To open the files in Python, you need to the <code>pyreadr</code> library:</p> <p><code>import pyreadr</code><br><code>blue = pyreadr.read_r('blue.rds')</code></p> <p> </p>
GEO gene expression dataset recompute for selected tumor samples
<div> <p>We aligned and quantified RNA-Seq data present in GEO with a standardized pipeline to homogenize data preprocessing for downstream applications.</p> <p>All uploaded files are UTF-8, <code>.csv</code>-formatted matrices. The <code>*_expected_count.csv.gz</code> files are unlogged, raw expression counts as reported by <code>rsem-quantify-expression</code> (see details below). The associated <code>*_metadata.csv.gz</code> files contain metadata pertinent to each column of the corresponding expression matrix.<br>Some metadata files may have more rows than the associated number of columns. This is for series that were only partially RNA-Seq based (e.g. combinated RNA-Seq plus miRNA-Seq samples in the same GEO accession ID).</p> <p>Metadata columns are derived from GEO series files, and follow their definitions. See each GEO entry directly to determine metadata meaning.</p> <p>Each recompute has at least the <code>gene_id</code> column holding Ensembl Gene IDs. The remaining columns are ENA run accession IDs of the specific recomputed samples.<br>Each associated metadata has at least the following columns:</p> <ul> <li><code>geo_accession</code>: The GEO sample ID of the sample.</li> <li><code>ena_sample</code>: The ENA sample ID of the sample.</li> <li><code>ena_run</code>: The ENA run accession ID of the sample, to be cross-referenced with the expression matrices.</li> </ul> <p>The remaining columns are derived from GEO metadata files and other ENA-provided data. Please refer to the <code>x.FASTQ</code> package for more information.</p> <h3>Pipeline Details</h3> <p>The alignment and quantification was made with the <code>x.FASTQ</code> tool available <a href="https://github.com/TCP-Lab/x.FASTQ">on Github</a> installed locally on an Arch Linux machine on commit <code>3a93dd77a70df59c74f7b15216c26f12cd918e81</code> running the Linux <code>6.7.8-zen1-1-zen</code> kernel with a <code>11th Gen Intel i7-1185G7 (8)</code> CPU and a <code>Intel TigerLake-LP GT2 [Iris Xe Graphics]</code> GPU. Please note that no sample filtering or omissions were done based on sample quality or sequencing depth. However, sensible trimming (e.g. low-quality bases and common adapters) was performed on all the samples.</p> <p>Reference genome was downloaded from Ensembl, version <code>hg38</code>. STAR was used to create the index genome with overhang set to <code>149</code>.</p> </div>
Рис. 2. РаспреΔеΛение среΔних почвенных образцов по коΛичеству жизнеспособных цист Heterodera glycines Fig. 2. Distribution of average soil samples by the number of viable cysts of Heterodera glycines in Reproductive potential of Soybean Cyst Nematode Heterodera glycines - quarantine pest of soybean - in Primorsky Region conditions
Рис. 2. РаспреΔеΛение среΔних почвенных образцов по коΛичеству жизнеспособных цист Heterodera glycines Fig. 2. Distribution of average soil samples by the number of viable cysts of Heterodera glycines
Storage time and temperature affect plasma osmolality values in field-collected blood samples
<p>Data and code for a study assessing the effect of storage conditions on blood plasma samples, published in Comparative Biochemistry and Physiology in 2024.</p>
Single-cell and single-nucleus RNA-sequencing from paired normal-adenocarcinoma lung samples provides both common and discordant biological insights
<p>The datasets generated by <em>Cellranger </em>for all 24 samples (.h5 format).<br><br></p>
Cancer cell line CNV samples
<p>This tab separated file includes all cancer cell line samples used for cell line heterogeneity analysis.</p> <p>Columns: sample ID, original NCIT code, cellosaurus ID, bins.</p> <p>All bins, duplications (1) and deletions (2), are merged while keeping their original order. Bin size: 5 Mb.</p> <p>This dataset belongs to the publication: https://doi.org/10.1101/2024.05.15.594310</p> <p> </p>
Linked collectors and determiners for: Marine Biological Sample Database, JAMSTEC.
Natural history specimen data linked to collectors and determiners held within, "Marine Biological Sample Database, JAMSTEC". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="http://bionomia.net/dataset/3c1a56f8-b63a-492f-bb77-babb497204fb">https://bionomia.net/dataset/3c1a56f8-b63a-492f-bb77-babb497204fb</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/3c1a56f8-b63a-492f-bb77-babb497204fb">https://gbif.org/dataset/3c1a56f8-b63a-492f-bb77-babb497204fb</a>. Formatted as a Frictionless Data package.
Results of elemental analyses of brain and liver human tissue samples performed by inductively coupled plasma mass spectrometry
<p>Human tissue samples of brain and liver were obtained after min. 24 h postmortem from the Department of Forensic Medicine, University of Lublin. Tissue samples were collected from typical anatomical locations intended for histopathological examination: A—polus frontalis (frontal pole), B—gyrus precentralis (precentral gyrus), C—gyrus postcentralis (postcentral gyrus), D—cortex cingularis (gyrus cinguli cingulate gyrus), E—hippocampus (hippocampus), F—caput nuclei caudati (head of caudate nucleus), G—fasciculus longitudinalis superior cerebri (superior longitudinal fasciculus of brain, SLF), H—fasciculus longitudinalis inferior cerebri (inferior longitudinal fasciculus of brain, ILF), I—thalamus dorsalis (dorsal thalamus), J—nucleus accumbens septi (nucleus accumbens septi, NAc), K—insula (insula), L—hepar (liver). Samples were taken with the consent of the prosecutor and the Local Bioethics Committee (Medical University of Lublin, Poland, KE-0254/152/2021, approval date 24 June 2021). The study was conducted in accordance with the World Medical Association Code of Ethics, Declaration of Helsinki, for experiments involving human subjects. The samples were mineralized to remove the organic matrix using microwave minerali-zation with nitric acid (69% suprapur HNO3, Baker, Radnor, PA, USA) in the microwave mineralization system Multiwave 5000 (Anton Paar, Graz, Austria). After mineralization step, HCl (Merck, Darmstadt, Germany) was added and diluted by ultrapure water. The elemental analysis was performed using the inductively coupled plasma mass spectrometer Agilent 8900 ICP-MS Triple Quad (Agilent, Santa Clara, CA, USA). </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.