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ShareScore release 0.9.0
Dataset results
71 results for “bulk sample”
Supplementary material 5 from: Majaneva M, Diserud OH, Eagle SHC, Hajibabaei M, Ekrem T (2018) Choice of DNA extraction method affects DNA metabarcoding of unsorted invertebrate bulk samples. Metabarcoding and Metagenomics 2: e26664. https://doi.org/10.3897/mbmg.2.26664
Methods (Scripts) :
Supplementary material 3 from: Majaneva M, Diserud OH, Eagle SHC, Hajibabaei M, Ekrem T (2018) Choice of DNA extraction method affects DNA metabarcoding of unsorted invertebrate bulk samples. Metabarcoding and Metagenomics 2: e26664. https://doi.org/10.3897/mbmg.2.26664
Number of reads per sample. :
Supplementary material 4 from: Majaneva M, Diserud OH, Eagle SHC, Hajibabaei M, Ekrem T (2018) Choice of DNA extraction method affects DNA metabarcoding of unsorted invertebrate bulk samples. Metabarcoding and Metagenomics 2: e26664. https://doi.org/10.3897/mbmg.2.26664
Diversity analyses. :
Supplementary material 1 from: Majaneva M, Diserud OH, Eagle SHC, Hajibabaei M, Ekrem T (2018) Choice of DNA extraction method affects DNA metabarcoding of unsorted invertebrate bulk samples. Metabarcoding and Metagenomics 2: e26664. https://doi.org/10.3897/mbmg.2.26664
Fragment and working PCR conditions. :
Supplementary material 4 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Table S3
Supplementary material 6 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Figure S1. Pictures were taken with a digital microscope (Keyence VHX-6000, Keyence, Osaka, Japan)
Supplementary material 1 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Protocol 1 – DIY-DS
Supplementary material 3 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Table S2. Raw read table
Supplementary material 2 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Table S1. PCR primers used in this study
Supplementary material 8 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Figure S3
Supplementary material 5 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Script 1
Bulk chemical composition of rock samples used to calculate their seismic velocity at lower crustal conditions
<p>These data are linked to a published study, whose aim is to compare the seismic velocity variations, derived from tomographic models (here the CIFALPS profile in the Alps, derived from Nouibat et al., 2022) and interpreted as rock transformations, with the seismic velocities of field samples. One way to predict seismic velocity at lower crustal conditions is to consider natural rocks as isotropic and to calculate their seismic properties from the relative abundance of mineral phases using their acknowledged properties (Abers and Hacker, 2016). In this process, the bulk chemical composition of the samples constitutes the starting data for this study. The subsequent work is based solely on thermodynamic models (here mainly using Holland and Powel, 1998 database) and the physical properties of the mineral phases (database from Abers and Hacker, 2016).</p>
Set7: ERCC bulk short-read RNA-seq by Illumina NextSeq platform, additional samples
GEO Series GSE269801. synthetic construct. 9 samples. Type: Expression profiling by high throughput sequencing.
Bulk RNA-seq profiles of primary AML samples corresponding to existing scRNA-seq data from GSE116256
GEO Series GSE199453. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Bulk RNA-seq detects cMPCs HO signature in B/T mouse blood samples - dataset #2
GEO Series GSE267143. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Bulk RNA-seq detects cMPCs HO signature in B/T mouse blood samples - dataset #1
GEO Series GSE267142. Mus musculus. 33 samples. Type: Expression profiling by high throughput sequencing.
Single-cell and bulk RNA-seq of human tumor-benign prostate adenocarinoma samples
GEO Series GSE193337. Homo sapiens. 27 samples. Type: Expression profiling by high throughput sequencing.
Set6: ERCC bulk short-read RNA-seq by Element Biosciences AVITI platform, additional samples
GEO Series GSE269800. synthetic construct. 4 samples. Type: Expression profiling by high throughput sequencing.
Transcriptional profiling of bulk tumor samples from implanted T cell low and T cell high mouse pancreatic tumors
GEO Series GSE109971. Mus musculus. 37 samples. Type: Expression profiling by high throughput sequencing.
Bulk segregant RNA seq samples from young leaf tissue of sesame seedlings with either SiCD-A or SiCD-B allele
GEO Series GSE164511. Sesamum indicum. 6 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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