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Dataset for "Applicability of Control Materials To Support Gene Promoter Characterization and Expression in Engineered Cells Using Digital PCR"
<p>This dataset constitutes the pre-processed data contained in the publication 'Applicability of Control Materials To Support Gene Promoter Characterization and Expression in Engineered Cells Using Digital PCR' Analytical Chemistry (2022) 94(14):5566-5574 (DOI: 10.1021/acs.analchem.1c05134).</p>
Supporting data sets for "Estimating Carbon Fixation of Plant Organs for Afforestation Monitoring using a Process-based Ecosystem Model and Ecophysiological Parameter Optimization". (the survey of tree breast diameter and tree height in 11-year old Eucommia ulmoides plantation, values of simulation results used in figures and tables.)
<p>Supporting data sets for Miyauchi et al., Ecology and Evolution, 2019 (accepted).</p> <p>The files store: </p> <p>(1) The survey of tree breast diameter and tree height in <em>Eucommia ulmoides</em> plantation<em>.</em> The ring and stem analysis and dry weight of seven harvested sample trees in the plantation.</p> <p>(2) Values of optimization result used fig.7.</p> <p>(3) Values of prediction result used fig.8. and table 4.</p> <p>(4) Values of optimized parameters by optimization methods, parameter range and constrain.</p>
Supporting data: Land-use change alters the mechanisms assembling rainforest mammal communities in Borneo
<p>These supporting data files were used in the analyses for a forthcoming<em> </em>paper (DOI to be confirmed). The two files consist of: </p> <p>1. Combined camera trap and live trap species-abundance matrix. Each row corresponds to a separate location, with species in different columns. Old-growth forest, logged forest and oil palm plantation locations have the prefixes "Old", "Log" and "Palm", respectively. Values in each cell are the number of independent captures (as defined in the paper) per seven days summed over the camera- and live-trapping protocols.</p> <p>2. Covariate data for each location, covering habitat structure, topography and local landscape context (covariates as defined in the paper). </p>
Fig. 6 in Molecular phylogenies support taxonomic revision of three species of Laurencia (Rhodomelaceae, Rhodophyta), with the description of a new genus
Fig. 6. Scenario of Osmundea biogeography using frame from Scotese animation (www.scotese.com/) at 100 Ma. South America and Africa are starting to move apart. Hypothetical ancestors (A) of widespread and diverse Osmundea Fora along Tethyan shores of which Osmundea caspica (A.D.Zinova & Zaberzhinskaya) comb. nov. (b) is almost certainly a relic. Since 100 Ma, Osmundea has colonized the Atlantic Ocean (c, e). Brazilian O. sanctarum M.T.Fujii & Cord.-Mar. (c) is likely a relic of the lineage at the origin of the clade that entered the Pacifc (d) and colonized the Americas.
Fig. 5 in Molecular phylogenies support taxonomic revision of three species of Laurencia (Rhodomelaceae, Rhodophyta), with the description of a new genus
Fig. 5. Osmundea caspica (A.D.Zinova & Zaberzhinskaya). Sangachal Bay, Azerbaijan, September 2003, dredged. A. Habit of herbarium voucher specimen with epiphytic acrochaetioid red algae. B. Outer cortical cells in surface view, elongated along the thallus axis, lacking secondary pit connections. C. Longitudinal section through outer cortex (scale bar as in B). D. Part of mature spermatangial receptacle, showing spermatangial flaments each terminating in a large rounded sterile cell. E. Spermatangial flament bearing mature spermatia and terminating in a rounded sterile cell.
Fig. 4 in Molecular phylogenies support taxonomic revision of three species of Laurencia (Rhodomelaceae, Rhodophyta), with the description of a new genus
Fig. 4. Palisada crustiformans (McDermid). A. Voucher specimen of P. crustiformans (ARS03327/BISH 766726) collected at Hawaii Island (type locality). B. Outer cortical cells in surface view. C. Detail of outermost cortical cell showing small spherical structure similar to 'corps en cerise' and secondary pit connections with adjacent cells.
Fig. 3 in Molecular phylogenies support taxonomic revision of three species of Laurencia (Rhodomelaceae, Rhodophyta), with the description of a new genus
Fig. 3. Ohelopapa fexilis (Setch.). A. Voucher specimen (01A07 / UPF4223) collected at Tahiti (type locality). B. Transversal section through outer cortex showing a translucent outermost cortical layer lacking secondary pit connections between cells.
Fig. 1 in Molecular phylogenies support taxonomic revision of three species of Laurencia (Rhodomelaceae, Rhodophyta), with the description of a new genus
Fig. 1. Tree inferred from rbcL using Bayesian analysis (BI) and including 111 specimens of members of the Laurencia complex and six outgroup taxa. The numbers above branches indicate Bayesian posterior probabilities (pp) and below branches indicate bootstrap values (bp) inferred from 1000 ML bootstrap replicates (ML); pp <0.95 and bp <75% are not shown. Taxa for which new sequences were produced are indicated in bold.
Fig. 2. Tree inferred from COI-5P in Molecular phylogenies support taxonomic revision of three species of Laurencia (Rhodomelaceae, Rhodophyta), with the description of a new genus
Fig. 2. Tree inferred from COI-5P + rbcL + LSU using Bayesian analysis (BI) and including 30 specimens of the Laurencia complex and two outgroup taxa. The numbers above branches indicate Bayesian posterior probabilities (pp) and below branches indicate bootstrap values (bp) inferred from 1 000 ML bootstrap replicates (ML); pp <0.95 and bp <75% are not shown.
Research data supporting "Self-assembled 2D free-standing Janus nanosheets with single-layer thickness"
<p>Raw research data supporting the publication Lin Y. et al., JACS, 2017, DOI: http://dx.doi.org/10.1021/jacs.7b06591</p>
WRF-Chem output supporting manuscript "Sources of Black Carbon Deposition to the Himalayan Glaciers in Current and Future Climates"
<p>Selected output from WRF-Chem v3.6.1 on black carbon deposition, rainfall, and snowfall over Southeast Asia. These files were used to prepare the figures and tables in the manuscript "Sources of Black Carbon Deposition to the Himalayan Glaciers in Current and Future Climates" by these authors. Files are in NetCDF format and contain metadata describing their contents. The naming convention is:</p> <p>YYYY_MM_EXT_daily_12km_dustfix.nc</p> <p>where YYYY_MM is the simulated year and month and the extensions are:</p> <p>NFC - No Further Control emission scenario</p> <p>MIT - Mitigation emission scenario</p> <p>EN - Simulated El Nino year</p> <p>LN - Simulated La Nina year</p> <p> </p>
Research data supporting "Raman spectroscopy imaging reveals interplay between atherosclerosis and medial calcification in human aorta"
<p>Research data supporting the publication:</p> <p>You, A. Y. F. <em>et al.</em>, 2017, "Raman spectroscopy imaging reveals interplay between atherosclerosis and medial calcification in human aorta", Science Advances, DOI: 10.1126/sciadv.1701156.</p>
Research data supporting "Correlated heterospectral lipidomics for biomolecular profiling of remyelination in multiple sclerosis"
<p>Research data supporting the paper:</p> <p>Bergholt, M.S. et al., "Correlated heterospectral lipidomics for biomolecular profiling of remyelination in multiple sclerosis", ACS Central Science, 2017, DOI: 10.1021/acscentsci.7b00367.</p>
Research data supporting "Platinum Nanocatalyst Amplification: Redefining the Gold Standard for Lateral Flow Immunoassays with Ultra-Broad Dynamic Range"
<p>Research data supporting the publication: Loynachan C. N., et al., 2017, ACS Nano, DOI: http://dx.doi.org/10.1021/acsnano.7b06229.</p>
A Pelagic Size Structure database (PSSdb) to support biogeochemical modeling: third update to first release of PSSdb-bulk
<p>This dataset represents the third update to the first release of the Pelagic Size Structure database (PSSdb, <a href="https://pssdb.net">https://pssdb.net</a>) scientific project, investigating the global particle size distributions measured from multiple pelagicǂ imaging systems. These devices include the Imaging Flow Cytobot (Olson and Sosik 2007), benchtop scanners like the ZooScan (Gorsky et al. 2010), and the Underwater Vision Profiler (Picheral et al. 2010). The data sources originate from Ecotaxa (<a href="https://ecotaxa.obs-vlfr.fr/">https://ecotaxa.obs-vlfr.fr/</a>), Ecopart (<a href="https://ecopart.obs-vlfr.fr/">https://ecopart.obs-vlfr.fr/</a>), and Imaging FlowCytobot dashboards (<a href="https://ifcb.caloos.org/dashboard">https://ifcb.caloos.org/dashboard</a> and <a href="https://ifcb-data.whoi.edu/dashboard">https://ifcb-data.whoi.edu/dashboard</a>). Links to the PSSdb code and documentation are available on the PSSdb webpage (<a href="https://pssdb.net">https://pssdb.net</a>). </p> <p>This <em>updated version</em> includes the following changes: </p> <p>● Duplicate data entries and NaN values have been removed.<br>● Data products now include Normalized Biomass Size Spectra (NBSS), and Particle Size Distribution (PSD), two widely used methods to represent plankton and particles size distribution in marine ecology and biogeochemistry (Jonasz and Fournier 1996, San Martin et al. 2006).<br>● Linear regressions are now performed with log10 transformations of the normalized biovolume/abundance and the size classes.<br>● Inclusion of UVP6 and other benchtop plankton Scanner datasets from net tows, which expand the temporal and spatial coverage of the data products.<br>● Unbiased portion of the size spectra is selected by a new thresholding method that accounts for both uncertainties on particle sizes, limited by the camera resolution, and particle count (Schartau et al. 2010), so that only size classes with less than 20% uncertainty are retained, in addition to gaps in the size spectra.</p> <p>Added in this version (March, 2024):<br>● An error in the thresholding function (scripts/funcs_NBS.py) was corrected.<br>● A quality control function was implemented (scripts/funcs_NBS.py) to flag size spectra calculations in products 1a and 1b.</p> <p>Added in this version (April, 2024):</p> <p><span><span>●<span> </span></span></span><span>An error in the size classes defined in the <a href="https://github.com/jessluo/PSSdb/blob/main/ancillary/ecopart_size_bins.tsv"><span>ecopart_size_bins.tsv</span></a> used by the size binning function (<a href="https://github.com/jessluo/PSSdb/commit/9e3c52179b2d3333971028a4a023a06e48444283#diff-28c0193fe5dad62dccba0020363d6cc496a8921fccb723a5e4bf3608d3dd0879"><span>scripts/funcs_NBS.py</span></a>) was corrected, the size ratio between consecutive size bins is the same across all the size ranges now.</span></p> <p> </p> <p>This PSSdb dataset is composed of two products, specific to each imaging device: </p> <ul> <li><strong>Product </strong>1a includes the size distribution , computed from normalized biovolume, for NBSS, and normalized abundance, for PSD, of plankton and particles within a set of pre-defined size classes (expressed in both biovolume and equivalent circular diameter), averaged by year and month, and in 1-degree longitude/latitude grid cells.</li> <li><strong>Product 1b</strong> includes the results of NBSS and PSD regression fit parameters, slopes, intercept, and coefficient of determination (R2), averaged by year and month, and in 1-degree longitude/latitude grid cells. The regression parameters are defined using ordinary least squares linear regressions applied to a log10 transformed normalized biovolume/normalized abundance and biovolume/ diameter size class values.</li> </ul> <p>Size spectra parameters were averaged over a maximum of 16 spatial and temporal subsets (0.5°x0.5°x1 week) to avoid over-representation of repeated sampling events (e.g., time-series datasets) within a grid cell. Linear regressions were performed on the linear portion of the log10-transformed NBSS and PSD estimates, between the size classes with a size measurement or particle count uncertainty greater than 20% (Schartau et al. 2010) , and where the maximum NB/PSD is observed and the largest size class before three empty consecutive size classes.</p> <p><em> <strong> For additional information, please see the PDF documentation available below ...</strong></em></p> <p> </p>
PSSdb-Taxa: A Pelagic Size Structure database taxa-specific product to support biogeochemical modeling: Update to first release of taxa-specific products
<p>This dataset represents the first update to the first release of taxa-specific products from the Pelagic Size Structure database (PSSdb,<a href="https://pssdb.net/"> </a><a href="https://pssdb.net">https://pssdb.net</a>), a scientific project investigating the global particle size distributions measured from multiple pelagic imaging systems. These devices include the Imaging Flow Cytobot (Olson and Sosik 2007), benchtop scanners like the ZooScan (Gorsky et al. 2010), and the Underwater Vision Profiler (Picheral et al. 2010). The data sources originate from Ecotaxa (<a href="https://ecotaxa.obs-vlfr.fr/">https://ecotaxa.obs-vlfr.fr/</a>), Ecopart (<a href="https://ecopart.obs-vlfr.fr/">https://ecopart.obs-vlfr.fr/</a>), and Imaging FlowCytobot dashboards (<a href="https://ifcb.caloos.org/dashboard">https://ifcb.caloos.org/dashboard</a> and<a href="https://ifcb-data.whoi.edu/dashboard"> </a><a href="https://ifcb-data.whoi.edu/dashboard">https://ifcb-data.whoi.edu/dashboard</a>).</p> <p>Taxa-specific products were generated after standardization of the automated or manual taxonomic annotations assigned to individual particles following the recent guidelines of Neeley et al. (2021). We used the World Register of Marine Species (WoRMS, <a href="https://www.marinespecies.org/">https://www.marinespecies.org/</a>) to assign each particle its final taxonomic annotation, and published group-specific relationships linking biovolume to carbon biomass or dry weight.</p> <p>The herein taxa-specific products include both taxonomic class-specific data, obtained after grouping all particles in a given taxonomic class<strong><sup>§</sup></strong>, and broad plankton functional type (PFT) data<strong><sup>Ɨ</sup></strong>. Detrital materials were also separated based on common categories (marine snow, aggregate, fecal pellet) and known biovolume-to-biomass conversion factors (Durkin et al. 2021). Links to the PSSdb code (including the taxonomic and allometric look-up tables) and documentation are available on the PSSdb webpage (<a href="https://pssdb.net">https://pssdb.net</a>).</p> <p> </p> <p>This <em>updated version</em> includes the following changes (modified in April, 2024):</p> <p>● An error in the size classes defined in the <a href="https://github.com/jessluo/PSSdb/blob/main/ancillary/ecopart_size_bins.tsv">ecopart_size_bins.tsv</a> used by the script generating taxa-specific products (<a href="https://github.com/jessluo/PSSdb/commit/b5725df2f32f0300ab64b0a630136c7f5a6857c7">scripts/5_compute_taxa_NBSS.py</a>) was corrected, the size ratio between consecutive size bins is the same across all the size range.</p> <p> </p> <p><em><strong> For additional information, please see the PDF documentation available below ...</strong></em></p>
[Supporting Information] Are Peruvians moving towards healthier diets with lower environmental burden? Household consumption trends for the period 2008-2021
<p>Supporting information from the manuscript: <em>Are Peruvians moving towards healthier diets with lower environmental burden? Household consumption trends for the period 2008-2021</em>. The main goal of this study was to comprehensively analyze the evolution in diet quality in Peru in the period 2008-2021 based on apparent household purchases extracted from the National Household Survey (ENAHO, by its acronym in Spanish). Furthermore, this study identified patterns in the temporal and spatial variability of food consumption, differences in consumption based on poverty levels, and gaps in achieving consumption levels of macronutrients and calories recommended by international nutritional authorities.</p> <p>dataset1: contains the consumption of 96 food products in kg/person/year per household, for a time horizon from 2008 to 2021.</p> <p>dataset2: contains the caloric and macronutrient content in kcal or g macronutrient per 100g of 92 food items.</p> <p>dataset3: contains the consumption of calories, and macronutrients in g/person/day per household, for a time horizon from 2008 to 2021.</p> <p>dataset1_labels: contains the data dictionary of dataset1</p> <p>dataset2_labels: contains the data dictionary of dataset2</p> <p>dataset3_labels: contains the data dictionary of dataset3</p> <p> </p>
Рис. 6. Карта распоΛожения гнезΑ ΑаΛьневосточного аиста в Амурской обΛасти в 2018– 2019 гг. по типам гнезΑовых опор Fig. 6. Map of Oriental stork nests in the Amur region in 2018–2019 by types of nesting supports in Oriental stork (Ciconia boyciana Swinhoe) breeding population survey in the Amur region in 2018-2019
Рис. 6. Карта распоΛожения гнезΑ ΑаΛьневосточного аиста в Амурской обΛасти в 2018– 2019 гг. по типам гнезΑовых опор Fig. 6. Map of Oriental stork nests in the Amur region in 2018–2019 by types of nesting supports
Рис.5. СоотношениеразΛичныхгнезΑовыхтиповопорв 2018–2019 гг. поаΑминистративным районам (Λевый график) и в цеΛом по Амурской обΛасти (правый график) Fig. 5. The ratio of different types of nesting supports in 2018–2019 by administrative districts (right graph) and in general in the Amur region (left graph) in Oriental stork (Ciconia boyciana Swinhoe) breeding population survey in the Amur region in 2018-2019
Рис.5. СоотношениеразΛичныхгнезΑовыхтиповопорв 2018–2019 гг. поаΑминистративным районам (Λевый график) и в цеΛом по Амурской обΛасти (правый график) Fig. 5. The ratio of different types of nesting supports in 2018–2019 by administrative districts (right graph) and in general in the Amur region (left graph)
Рис. 5. Дополнительные структуры, служаЩие укреплению Замочного краЯ и раковины у Laternula elliptica: А – дополнительнаЯ поддерживаюЩаЯ пластинка прикрывает макушечную Щель; Б – утолЩение ранее поврежденного краЯ раковины; В – пример воЗникновениЯ двух поддерживаюЩих пластинок. ОбоЗначениЯ: мщ – макушечнаЯ Щель; хр – хондрофор; ппЛ – поддерживаюЩаЯ пластинка; дпЛ – дополнительные пластинки; уКр – утолЩениЯ Задне-дорсального краЯ и краЯ сифонального ЗиЯниЯ. Fig. 5. Additional structures serving for consolidation of hinge margin and for restoration of shell edge in Laternula elliptica: А – the umbonal crack covered by additional buttress; Б – thickening of damaged edge; В – appearance of two supporting plates. Notes: мщ – umbonal crack; хр – chondrophore; ппЛ – buttress; дпЛ – additional supporting plate; уКр – thickening of posterior-dorsal margin. in Species of warm-water origin Laternula elliptica (King, 1832) (Mollusca: Bivalvia: Laternulidae), a widespread mollusk in recent Antarctica
Рис. 5. Дополнительные структуры, служаЩие укреплению Замочного краЯ и раковины у Laternula elliptica: А – дополнительнаЯ поддерживаюЩаЯ пластинка прикрывает макушечную Щель; Б – утолЩение ранее поврежденного краЯ раковины; В – пример воЗникновениЯ двух поддерживаюЩих пластинок. ОбоЗначениЯ: мщ – макушечнаЯ Щель; хр – хондрофор; ппЛ – поддерживаюЩаЯ пластинка; дпЛ – дополнительные пластинки; уКр – утолЩениЯ Задне-дорсального краЯ и краЯ сифонального ЗиЯниЯ. Fig. 5. Additional structures serving for consolidation of hinge margin and for restoration of shell edge in Laternula elliptica: А – the umbonal crack covered by additional buttress; Б – thickening of damaged edge; В – appearance of two supporting plates. Notes: мщ – umbonal crack; хр – chondrophore; ппЛ – buttress; дпЛ – additional supporting plate; уКр – thickening of posterior-dorsal margin.
ScienceDex guides
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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.