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51 results for “Sampling Standardization”
Clumped Isotope Data from Gar Scale Bioapatite Samples and Lab Standards
<p>We measured the clumped isotope, Δ<sub>47</sub>, composition of the carbonate in the bioapatite of modern gar scales from 19 specimens collected from eight locations in North America, with mean water temperatures ranging from 11.3 to 27.0 °C. Samples were reacted at 90 °C and standards were reacted at both 25 °C and 90 °C. Samples and standards were run on a MAT 253 dual-inlet gas-source isotope ratio mass spectrometer (Thermo Scientific, USA) housed at the Yale University Analytical and Stable Isotope Center. Raw data was processed using the most up-to-date methods (Petersen et al., 2019). Our sample reproducibility was 0.021‰ (1 SD). Our standards included cylinder CO<sub>2 </sub>(Airgas, USA), CO<sub>2</sub> equilibrated with water at 25 °C and 50 °C, Carrara marble, and the ETH carbonate anchors. Δ<sub>47</sub> data is presented as ‰, InterCarb-Carbon Dioxide Equilibrium Scale (I-CDES) 90 °C for samples and standards reacted at 90 °C and I-CDES 25 °C for standards reacted at 25 °C.</p> <p> </p> <p>We use modern climate data from Daymet V4 (Thornton et al., 2022). We convert variable water temperatures into an effective temperature <em>T</em><sub>e</sub>, which accounts for the influence of temperature on growth rate. <em>T</em><sub>e</sub> for our gar samples ranges from 13.8 to 27.1 °C. We used this dataset to create a new calibration relating temperature to the Δ<sub>47</sub> in gar scale bioapatite. The resulting calibration curve is: Δ<sub>47</sub> = (0.1206 ± 0.0171) x 10<sup>6</sup>/<em>T</em><sub>e</sub><sup>2</sup> – (0.7429 ± 0.0587) (1 SE), with <em>R</em><sup>2</sup> = 0.75.</p>
Standard Cross-Cultural Sample of Religion
<p>The Standard Cross-Cultural Sample of Religion is a product of the <a href="https://religiondatabase.org/" rel="nofollow">Database of Religious History (DRH)</a>. The DRH is a qualitative-quantitative encyclopedic database of historical religious data across time and space. Data are contributed to the project by academic <a href="https://religiondatabase.org/landing/about/people/experts" rel="nofollow">experts</a> and overseen by a panel of <a href="https://religiondatabase.org/landing/about/people/editors" rel="nofollow">editors</a>. The data take the form of answers (provided by experts) to a long list of standard questions grounded in time and space.</p> <p>The Standard Cross-Cultural Sample of Religion is “standard” in a different way than its namesake, The Standard Cross-Cultural Sample (SCCS). The SCCS was designed to control for region and cultural relatedness. Because of our mostly bottom-up, expert-driven data gathering method, DRH data is heavily overweighted in certain time/space regions. Analysts will have to control for this as they see fit.</p> <p>On the other hand, DRH data is “standard” in the sense that whatever Group, Place of Text is being portrayed, experts are answering a standardized set of questions, allowing a degree of comparison and quantitative analysis that has simply never been possible before. As the DRH grows, top-down data-gathering pushes will be targeted at underrepresented regions of the world, with the goal of making future versions of the SCCSR more and more comprehensive.</p> <p>The Standard Cross-Cultural Sample of Religion (SCCSR.v2) is provided under CC-BY-4.0 license.</p>
16S RC-PCR control samples and mock standards
<p>16S RC-PCR datasets of control samples and mock standards.<br> Produced during the 16S RC-PCR study.</p> <p>Datasets are produced using 2x150 Illumina MiniSeq.<br> A short description of each sample is given in the METADATA_zenodo file.</p>
MetaBAT 2.12.1 genome binning of the CAMI 2 Mouse Gut Toy data set, samples 0-63, gold standard pooled assembly
Genome binning of the gold standard pooled assembly <br><strong>Software: </strong>MetaBAT<br><strong>SoftwareVersion: </strong>2.12.1<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://bitbucket.org/berkeleylab/metabat<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandUsed:</strong> bowtie2-build anonymous_gsa_pooled.fasta anonymous_gsa_pooled.fasta<br>for i in {0..63}; do bowtie2 -q --threads 30 --fr -x anonymous_gsa_pooled.fasta --interleaved sample_${i}/anonymous_reads.fq -S anonymous_reads_sample_${i}.sam ; done<br>for i in {0..63}; do samtools view -b sample_${i}.sam -o anonymous_reads_sample_${i}.bam & done<br>for i in {0..63}; do samtools sort anonymous_reads_sample_${i}.bam -o anonymous_reads_sample_${i}.sorted.bam ; done<br>for i in {0..63}; do samtools index anonymous_reads_sample_${i}.sorted.bam ; done<br>runMetaBat.sh -l anonymous_gsa_pooled.fasta anonymous_reads_sample_*.sorted.bam
Sampled ΔH/Δλ and ΔH data from ABFE calculations (using standard atomic masses) of 10 ligands bound to Cyclophilin D
<p>Supplementary Information: "Evaluating the use of absolute binding free energy in the fragment optimization process"</p> <p>Included are the ABFE raw free energy samples for multiple replicates (labelled by `run` number) of 10 ligands to bound Cyclophilin D. These ligands are originally detailed by Grädler et al. (https://doi.org/10.1016/j.bmcl.2019.126717). Unlike other datasets in this work, which employed hydrogen mass repartitioning, the ligands here were calculated using standard atomic masses.</p> <p>All samples are provided as a set of `.xvg` files as generated by GROMACS 2021 (https://doi.org/10.5281/zenodo.5849961). The `.xvg` files are labelled as dhdl.N.xvg where N represents the λ state the free energy values were sampled from. The `.xvg` files contain both ΔH/Δλ and ΔH values, please see the header of each files for more information.</p> <p>Samples detailing the partial decoupling of the ligand from the protein-ligand complex are contained within the `complex` folder. These consist of an orientational restraint addition step (found within the `restraints-xvg` folders), charge annihilation step (found within the `coul-xvg` folders), and Van der Waals decoupling step (found within the `vdw-xvg` folders).</p> <p>Samples detailing the partial decoupling of the ligand from solvent are contained within the `ligand` folder and consist of a charge annihilation step (found within the individual `coul-xvg` folders) and a Van der Waals decoupling step (found within the individual `vdw-xvg` folders).</p>
Mean, standard deviation, and percentiles of the schema and domain scores of the German version of the Young Schema Questionnaire - Short Form 3 (YSQ-S3) in a German opportunity sample (n=1,150)
<p>Mean, standard deviation, and percentiles of the schema and domain scores of the German version of the Young Schema Questionnaire - Short Form 3 (YSQ-S3) in a German opportunity sample (n=1,150). Details are reported in: Kriston L, Schäfer J, Jacob GA, Härter M, Hölzel LP. Reliability and validity of the German version of the Young Schema Questionnaire - Short Form 3 (YSQ-S3). <em>Eur J Psychol Assess</em> 2013; 29: 205-212.</p> <p>IMPORTANT: This is an opportunity sample that is not representative of any well-defined population. Accordingly, the values should not be used as reference or norm values for the German version of the YSQ-S3.</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>
Standard Sample Description V1 Structural Metadata
<p>Standard Sample Description V1 is a specification aimed at harmonising the collection of analytical measurement data for the presence of harmful or beneficial chemical substances in food, feed and water. The specification is a list of standardised data elements (items describing characteristics of samples or analytical results such as country of origin, product, analytical method, limit of detection, result, etc.), linked to controlled terminologies. This file has been prepared to support the publication of data and interoperability. This file indicates which data elements from the specification will not be published to ensure full protection of confidential/sensitive information, for example personal data in accordance with Regulation (EC) No 45/2001 and to protect commercial interests, including intellectual property as specified in Article 4(2), first indent, of Regulation (EC) No 1049/2001.</p> <p>The Excel table contains information about the structural metadata elements of the data collection and their fact tables.<br> <br> The column <em>name</em> shows the name of the element (e.g. localOrg).<br> The column <em>description</em> describes how the content has to be interpreted.<br> The column <em>code</em> expresses the corresponding code of the structural metadata element.<br> The column <em>optional</em> says whether the structural metadata element is optional or not (then it is mandatory).<br> The column <em>dataType</em> contains the type which can be used to fill the structural metadata element and the possible maximal length of the field. The possible types are: text or number. <br> The column <em>catalogue</em> contains the name of the catalogue where the content of the structural metadata element has to be picked from (e.g. COUNTRY).<br> The column <em>data</em> <em>protection</em> contains whether the structural metadata element will be published or not (yes = will not be published, no = will be published).</p>
Standard Sample Description V2 Structural Metadata
<p>Standard Sample Description V2 is a specification aimed at harmonising the collection of analytical measurement data for the presence of harmful or beneficial chemical substances in food, feed and water. The specification is a list of standardised data elements (items describing characteristics of samples or analytical results such as country of origin, product, analytical method, limit of detection, result, etc.), linked to controlled terminologies. This specification uses EFSA FoodEx2 to describe sampled foods.</p> <p>This file has been prepared to support the publication of data and interoperability. This file indicates which data elements from the specification will not be published to ensure full protection of confidential/sensitive information, for example personal data in accordance with Regulation (EC) No 45/2001 and to protect commercial interests, including intellectual property as specified in Article 4(2), first indent, of Regulation (EC) No 1049/2001.</p> <p>The Excel table contains information about the structural metadata elements of the data collection and their fact tables.<br> <br> The column <em>name</em> shows the name of the element (e.g. localOrg).<br> The column <em>description</em> describes how the content has to be interpreted.<br> The column <em>code</em> expresses the corresponding code of the structural metadata element.<br> The column <em>optional</em> says whether the structural metadata element is optional or not (then it is mandatory).<br> The column <em>dataType</em> contains the type which can be used to fill the structural metadata element and the possible maximal length of the field. The possible types are: text or number. <br> The column <em>catalogue</em> contains the name of the catalogue where the content of the structural metadata element has to be picked from (e.g. COUNTRY).<br> The column <em>data</em> <em>protection</em> contains whether the structural metadata element will be published or not (yes = will not be published, no = will be published).</p>
D-PLACE dataset derived from Murdock and White 1969 'Standard Cross-Cultural Sample'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Murdock GP & White DR. 1969. Standard Cross-Cultural Sample. Ethnology. 9:329–369.</p> </blockquote>
Linked collectors and determiners for: NEON Biorepository Mammal Collection (Vouchers [Standard Sampling]).
Natural history specimen data linked to collectors and determiners held within, "NEON Biorepository Mammal Collection (Vouchers [Standard Sampling])". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/f28219f3-49cd-4b55-99ad-39f3ced87ddd">https://bionomia.net/dataset/f28219f3-49cd-4b55-99ad-39f3ced87ddd</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/f28219f3-49cd-4b55-99ad-39f3ced87ddd">https://gbif.org/dataset/f28219f3-49cd-4b55-99ad-39f3ced87ddd</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: NEON Biorepository Mammal Collection (Vouchers [Outside of Standard Sampling]).
Natural history specimen data linked to collectors and determiners held within, "NEON Biorepository Mammal Collection (Vouchers [Outside of Standard Sampling])". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/9f5e2bfa-8ea0-46c5-bc6b-909a82f67af4">https://bionomia.net/dataset/9f5e2bfa-8ea0-46c5-bc6b-909a82f67af4</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/9f5e2bfa-8ea0-46c5-bc6b-909a82f67af4">https://gbif.org/dataset/9f5e2bfa-8ea0-46c5-bc6b-909a82f67af4</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: NEON Biorepository Aquatic Plant, Bryophyte, Lichen and Macroalgae Collection (Herbarium Vouchers [Standard Sampling]).
Natural history specimen data linked to collectors and determiners held within, "NEON Biorepository Aquatic Plant, Bryophyte, Lichen and Macroalgae Collection (Herbarium Vouchers [Standard Sampling])". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/dd1b2dbc-1a38-4212-a3d9-893a160da252">https://bionomia.net/dataset/dd1b2dbc-1a38-4212-a3d9-893a160da252</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/dd1b2dbc-1a38-4212-a3d9-893a160da252">https://gbif.org/dataset/dd1b2dbc-1a38-4212-a3d9-893a160da252</a>. Formatted as a Frictionless Data package.
Text-fig. 8. Plot of discriminant scores (R1/R2) of individual m1 of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (standardized discrimination scores based on nine most significant variables). in Genus Apodemus In The Pleistocene Of Central Europe: When Did The Extant Taxa Appear?
Text-fig. 8. Plot of discriminant scores (R1/R2) of individual m1 of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (standardized discrimination scores based on nine most significant variables).
ECCO v4 standard analysis sample (v4r3)
<p>Generated from the ECCO output (version 4, release 3) available to download from <a href="https://ecco-group.org/products.htm">ecco-group.org</a> using a Julia workflow available from <a href="https://github.com/gaelforget/OceanStateEstimation.jl">OceanStateEstimation.jl</a> (see <a href="https://gaelforget.github.io/OceanStateEstimation.jl/dev/">the docs</a>).</p>
ECCO v4 standard analysis sample
<p>Generated from the ECCO output (version 4, release 2) available to download from <a href="https://ecco-group.org/products.htm">ecco-group.org</a> using a Julia workflow available from <a href="https://github.com/gaelforget/OceanStateEstimation.jl">OceanStateEstimation.jl</a> (see <a href="https://gaelforget.github.io/OceanStateEstimation.jl/dev/">the docs</a>).</p> <p>The ECCO output (version 4, release 2) used here is archived @ <a href="https://doi.org/10.7910/DVN/NXYKDW">https://doi.org/10.7910/DVN/NXYKDW</a></p>
ECCO v4 standard analysis sample (v4r4)
<p>Generated from the ECCO output (version 4, release 4) available to download from <a href="https://ecco-group.org/products.htm">ecco-group.org</a> using a Julia workflow available from <a href="https://github.com/gaelforget/OceanStateEstimation.jl">OceanStateEstimation.jl</a> (see <a href="https://gaelforget.github.io/OceanStateEstimation.jl/dev/">the docs</a>).</p> <p> </p>
Chains from Ni58 Standard Sample
<p>Markov chain Monte Carlo chains from the analysis of neutron reflectometry data form a Ni58 sample. This has been used in the ISIS Neutron and Muon Source Training School multiple times. </p>
Standardizing ID-Labels for seaweed samples used for chemical composition analyses and refinery processes in Nordic and European research projects
<p><strong>Introduction</strong></p> <p>Seaweed samples can be divided into two groups:</p> <ol> <li>Small samples (½-3 kg wet weight (ww)) often used for chemical content analyses including seasonal variation and testing different cultivation conditions or preliminary lab scale experiments on storage, extraction, separation, fermentation, etc.</li> <li>Larger samples (>3 kg ww) for lab- or pilot scale experiments on storage, extraction, separation, fermentation, etc.</li> </ol> <p>Seaweed samples will always have the following information-tracks:</p> <ol> <li><strong>Sample Code: </strong>A ID containing the most important information and the sample code will follow the sampled biomass from harvest to final research results.</li> <li><strong>Seaweed Processing Code:</strong> The sample code will be extended with 8 digits and 1 letter if processing of biomass occurs.</li> <li><strong>Batch Number:</strong> A code describing details about the harvest and origin of the seaweed.</li> <li><strong>Sample Overview:</strong> An Excel file describing all details about the sample: first <strong>sample code</strong>, then species, grinding, freezing/drying specifications, seeding and harvesting information, planed aim of the sample (e.g. polysaccharides), place stored, seaweed processing details, analyse results, etc. Maintained by the sample provider.</li> </ol>
DAS Tool 1.1.2 genome binning of the CAMI 2 Mouse Gut Toy data set, samples 0-63, gold standard pooled assembly
Genome binning of the gold standard pooled assembly. Refinement of the binning output of MaxBin 2.2.7, MetaBAT 2.12.1, CONCOCT 1.0.0, and DAS Tool 1.1.2.<br><strong>Software: </strong>DAS Tool<br><strong>SoftwareVersion: </strong>1.1.2<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://github.com/cmks/DAS_Tool<br><strong>DockerImage:</strong> cami/das_tool:1.1.2<br><strong>IsBiobox:</strong> No<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandUsed:</strong> DAS_Tool -i binning_concoct1.0.0,binning_maxbin2.2.7,binning_metabat2.12.1 -c anonymous_gsa_pooled.fasta -o output --search_engine diamond
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