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.
41
datasets available to search
ShareScore release 0.9.0
Dataset results
41 results for “organic layer”
Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction
<p>The data consists of Illumina raw reads obtained from prokaryotes from coastal Mediterranean seawaters in Alicante, Spain, over the course of one year. Illumina DNA prep kit (IDT, Ref. 20026930) was used following the manufacturer's protocol to generate and sequence the libraries for the obtained DNA samples from prokaryotic metagenomes. Subsequently, these libraries were sequenced using HiSeq X technology (150 PE, 1 lane) by two different facilities: Macrogen in Seoul, Republic of Korea, and the Genomics Unit of the Center for Genomic Regulation (CRG) in Barcelona, Spain. The dataset was used in order to ascertain the main microbial producers involved in the release of DNA packaged in EVs in our annual sampling.</p> <p>The samples are named according to the convention 'prokmeta[Month], where 'prokmeta' signifies prokaryotic metagenome, '[Month]' indicates the month of sampling, and "1" represents the forward , while (2) represent the reverse read. For instance, 'prokmetaDEC.1' denotes the prokaryotic metagenome for sample of December, with '1' indicating the forward read, while 'prokmetaDEC.2' represents the corresponding reverse read. This naming scheme is consistent across all samples in the dataset from sample of November 2021 to sample of June 2022.</p> <p> </p>
Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction.
<p>The dataset consists of third part of data for the same paper " Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction." Data were uploaded separately due to the limited space available for uploading which is f 50.00 GB and since i have more I could not upload them together.</p> <p>The data are Illumina raw reads obtained from vesicle fractions of 20%, isolated from Coastal Mediterranean seawaters in Alicante, Spain, over the course of one year. Illumina DNA prep kit (IDT, Ref. 20026930) was used following the manufacturer's protocol to generate and sequence the libraries for the obtained DNA samples from EVs 20% fraction metagenomes. Subsequently, these libraries were sequenced using HiSeq X technology (150 PE, 1 lane) by two different facilities: Macrogen in Seoul, Republic of Korea, and the Genomics Unit of the Center for Genomic Regulation (CRG) in Barcelona, Spain. The dataset was used in order to ascertain the main microbial organisms involved in the production of EVs and packaging of DNA into those EVs in our annual sampling and the types of genes that were packaged in vessicles isolated from the Coastal Mediterranean seawaters (Functional annotation of genes packaged in vesicles).</p> <p> The data are labeled as "EVs_20_DEC_1.fastq" which denotes the vesicle 20% Optiprep fraction for sample collected in December, with "1" indicating the forward read, while "EVs_20_DEC_2.fastq" represents the corresponding reverse read. This naming scheme is consistent across all vesicle fraction samples in the dataset.</p>
Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction
<p>The data consists second part of data for the same paper " Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction." Data were uploaded separately due to the limited space available for uploading.</p> <p>The data are Illumina raw reads obtained from vesicle fractions of 25%, isolated from coastal Mediterranean seawaters in Alicante, Spain, over the course of one year. Illumina DNA prep kit (IDT, Ref. 20026930) was used following the manufacturer's protocol to generate and sequence the libraries for the obtained DNA samples from EVs fractions metagenomes. Subsequently, these libraries were sequenced using HiSeq X technology (150 PE, 1 lane) by two different facilities: Macrogen in Seoul, Republic of Korea, and the Genomics Unit of the Center for Genomic Regulation (CRG) in Barcelona, Spain. The dataset was used in order to ascertain the main microbial organisms involved in the production of EVs and packaging of DNA into those EVs in our annual sampling and the types of genes that were packaged in vessicles isolated from the Coastal Mediterranean seawaters (Functional annotation of genes packaged in vesicles).</p> <p> The data are labeled as "EVs_25_DEC_1.fastq" which denotes the vesicle 25% Optiprep fraction for sample collected in December, with "1" indicating the forward read, while "EVs_25_DEC_2.fastq" represents the corresponding reverse read. This naming scheme is consistent across all vesicle fraction samples in the dataset. </p>
Data for "Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas"
<p>Data for "Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas". Preprint: https://www.biorxiv.org/content/10.1101/507293v2.</p> <p>Raw sequencing data is available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134378.</p>
Chemical Design and Magnetic Ordering in Thin Layers of 2D Metal–Organic Frameworks (MOFs)
<p>Relevant data for publication with DOI: <a title="DOI URL" href="https://doi.org/10.1021/jacs.1c07802">10.1021/jacs.1c07802</a></p>
Data for "Evaluating Metal-Organic Precursors for Focused Ion Beam Induced Deposition through Solid-Layer Decomposition Analysis"
<p>Experimental Data for "Evaluating Metal-Organic Precursors for Focused Ion Beam Induced Deposition through Solid-Layer Decomposition Analysis"</p> <p> </p> <p> </p> <p>The collected experimental data for tested four different metal-organic precursor layers:</p> <p> </p> <p>1 - [Cu<sub>2</sub>(μ-O<sub>2</sub>C<sup>t</sup>Bu)<sub>4]n</sub></p> <p>2 - [Cu<sub>2</sub>(NH<sub>2</sub>(NH=)CC<sub>2</sub>F<sub>5</sub>)<sub>2</sub>(µ-O<sub>2</sub>CC<sub>2</sub>F<sub>5</sub>)<sub>4</sub>]</p> <p>3 - [Cu<sub>2</sub>(µ-O<sub>2</sub>CC<sub>2</sub>F<sub>5</sub>)<sub>4</sub>]</p> <p>4 - [Ag<sub>2</sub>(μ-O<sub>2</sub>CC<sub>2</sub>F<sub>5</sub>)<sub>2</sub>]</p> <p> </p> <p>BSE.zip - includes SEM BSE images of the precursor layers</p> <p>EDX.zip - inludes SEM EDX results of the precursor layers together with exemplary python jupyter notebook to analyze EDX hyperspectral data</p>
Nanoscale probing of the organic binder in artists' paint layers: organic phases and chemical dynamics - Dataset
Open the record for dataset details and reuse information.
Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction
<p>The dataset includes additional data for the same paper, <em>"Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction."</em> The data were uploaded separately due to the 50.00 GB limit on available upload space.</p> <p>The gene sequences contained within EVs were annotated using the DRAM program within the Kbase platform, enabling us to identify the specific types and functions of genes packaged within EV fractions. This annotation process provided a comprehensive overview of the genetic cargo potentially encapsulated in EV fractions 25% and 20%.</p> <p>The provided Excel files represent the output from DRAM annotation and contain the detected genes in EV fractions 25% and 20%. Although the gene names in the files are labeled as 30%, 25%, and 20%, this is a naming error. The file labeled "annotation for EVs 25%" contains genes detected in the 25% EV fraction, while the file labeled "annotation for EVs 20%" corresponds to genes from the 20% EV fraction.</p> <p>There are two replicates for the 20% EV fraction, denoted as rep1 and rep2, and both samples were analyzed. The gene abundance was calculated as the mean percentage of the two replicates, corrected with standard error.</p> <p> </p>
Tree regeneration after fire: Aspen removal experiment, organic layer depth measurments 2001, 2002
This research was intended to address the general question of whether asexual stem regeneration of trembling aspen (Populus tremuloides Michx.) reduces rates of establishment and growth of potential invading conifer species during the initial years following fire. Interactions between aspen and conifers were studied under natural conditions in a burned aspen stand with a high potential for aspen re-sprouting. The study contributes to our understanding of whether competitive interactions between tree seedlings are likely to help maintain deciduous stands across disturbance cycles by reducing the potential for successful conifer establishment. Data are organic layer depths (cm) sampled with a hand corer on 20 July 2001 and 12 July 2002. Measurements made on 16 August 2002 have been omitted because they are inconsistent with earlier measurements and probably reflect observer bias.
Data for figures in "Quantum spin-liquid states in an organic magnetic layer and molecular rotor hybrid"
<p>Data used to produce the figures in the work entitled "Quantum spin-liquid states in an organic magnetic layer and molecular rotor hybrid"</p>
Analysis of The Use of Organic Materials as Subgrade Layer of Rigid Pavement (Case Study: Semarang – Demak Toll Road Section 1)
Open the record for dataset details and reuse information.
Codes and datasets for "Cell cycle-dependent organization of a bacterial centromere through multi-layered regulation of the ParABS system"
<p>Matlab and R codes and datasets related to the publication entitled "Cell cycle-dependent organization of a bacterial centromere through multi-layered regulation of the ParABS system"</p>
Post-fire Recovery of Soil Organic Layer Carbon in Canadian Boreal Forests, 2015-2018
This dataset provides site moisture, soil organic layer thickness, soil organic carbon, nonvascular plant functional group, stand dominance, ecozone, time-after-fire, jack pine proportion, and deciduous proportion for 511 forested plots spanning ~140,000 km2 across two ecozones of the Northwest Territories, Canada (NWT). The plots were established during 2015-2018 across 41 wildfire scars and unburned areas (no burn history prior to 1965), with 317 plots in the Plains and 194 plots in the Shield regions. At each plot, two adjacent 30-m transects were established 2 m apart, running north from the plot origin. Soil organic layer (SOL) depth (cm) was measured every 3 m and the mean was taken from the 10 measurements to calculate a plot-level SOL thickness. Three soil organic layer profiles were destructively sampled at 0, 12, and 24 m using a corer that was custom designed for NWT soils. Within the transects, all stems taller than 1.37 m were identified to species to calculate tree density (stems / m2). Nonvascular plant percent cover was identified to functional group at five, 1-m2 quadrats spaced 6 m apart along the belt transect. A subset of 2,067 of 5,137 total increments from 1,803 profiles from 421 plots were analyzed for total percent C using a CHN analyzer. Time-after-fire was established using fire history records. For older plots where no known fire history is recorded, tree age was used. Data are for the period 2015-06-11 to 2018-08-24 and are provided in comma-separated values (CSV) format.
Neocortical layer 4 in adult mouse differs in major cell types and circuit organization between primary sensory areas
GEO Series GSE134378. Mus musculus. 118 samples. Type: Expression profiling by high throughput sequencing.
Integrative spatial analysis reveals a multi-layered organization of glioblastoma
GEO Series GSE237183. Homo sapiens. 19 samples. Type: Other.
Nucleosome organizations in induced pluripotent stem cells reprogrammed from somatic cells belonging to three different germ layers
GEO Series GSE46716. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
HiCrayon reveals distinct layers of multi-state 3D chromatin organization
GEO Series GSE255264. Homo sapiens. 91 samples. Type: Other; Third-party reanalysis.
Data and code for figures of "Electro-optic spatial light modulator from an engineered organic layer" by Benea-Chelmus et al.
<p>This upload contains the main dataset represented in the figures of the following publication:</p> <p>"Electro-optic spatial light modulator from an engineered organic layer" by Benea-Chelmus et al.</p>
Chromosome pairing, stress response, and the interwoven layers of 3D chromatin organization
GEO Series GSE213553. Drosophila melanogaster. 54 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Other.
Notch coordinates self-organization of germ layers and axial polarity in cnidarian gastruloids
GEO Series GSE307733. Nematostella vectensis. 9 samples. Type: Expression profiling by high throughput sequencing.
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.