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695 results for “decomposition”
Decomposition, porewater, plant and animal collection, and soil temperature data in Airport Marsh, Sapelo Island, 7/2019-7/2020
Environmental gradients can affect organic matter decay within and across wetlands and contribute to spatial heterogeneity in soil carbon stocks. We tested the sensitivity of decay rates to tidal flooding and soil depth in a minerogenic salt marsh using the tea bag index (TBI). Tea bags were buried at 10- and 50- cm along transects sited at lower, middle, and higher elevations that paralleled a headward eroding tidal creek. Plant and animal communities and soil properties were characterized once while replicate tea bags and porewaters were collected 3 and 4 times respectively over one year.
Decomposition of soil and permafrost organic matter eroding into the Beaufort Sea near Drew Point, Alaska
Arctic coastal erosion mobilizes large quantities of permafrost organic matter to the Arctic Ocean, where it may be decomposed, releasing carbon dioxide. To quantify the biodegradability of this eroding material, we designed an aerobic bottle incubation experiment to measure CO2 production from coastal soils/sediments submerged in seawater. Seasonally thawed active layer soils and permafrost were sampled near Drew Point along the Alaska Beaufort Sea coast. Cores were taken from three surface geomorphic classifications common in this area: primary surface that has not been reworked by thaw-lake cycles, a young drained lake basin, and an ancient drained lake basin. Core subsamples were chosen to represent three distinct horizons present in eroding bluffs at Drew Point: seasonally thawed active layer soils near the tundra surface, Holocene-age terrestrial soils and/or lake sediments, and late-Pleistocene age relict marine sediments. Soil/sediment subsamples were mixed with Beaufort Sea surface water and incubated in triplicate at 4C and 16C for 40 days. In addition, a subset of soil/sediment samples were incubated with and without seawater at 16C for 40 days. The data reported here summarizes the results of the incubation experiment for each soil sample: cumulative CO2-C production over 40 days normalized to dry weight and to organic carbon content (OC), average rate of CO2 production normalized to dry weight and to TOC, and the percent of OC remineralized over 40 days.
Decomposition Dynamics in the Sarracenia Purpurea Microecosystem at Harvard Forest 2010
Ecological communities show great variation in species richness, composition and food web structure across similar and diverse ecosystems. Knowledge of how this biodiversity relates to ecosystem functioning is important for understanding the maintenance of diversity and the potential effects of species losses or gains on ecosystems. While research often focuses on how variation in species richness influences ecosystem processes, assessing species richness in a food web context can provide further insight into the relationship between diversity and ecosystem functioning and provide potential mechanisms underpinning this relationship. Here, we assessed how species richness and trophic diversity affect decomposition rates in a complete aquatic food web: the five trophic level web that occurs within water-filled leaves of the northern pitcher plant, Sarracenia purpurea. We identified a trophic cascade in which top-predators - larvae of the pitcher-plant mosquito - indirectly increased bacterial decomposition by preying on bactivorous protozoa. Our data also revealed a facultative relationship in which larvae of the pitcher-plant midge increased bacterial decomposition by shredding detritus. These important interactions occur only in food webs with high trophic diversity, which in turn only occurs in food webs with high species richness. We show that species richness and trophic diversity underlie strong linkages between food web structure and dynamics that influence ecosystem functioning. The importance of trophic diversity and species interactions in determining how biodiversity relates to ecosystem functioning suggests that simply focusing on species richness does not give a complete picture as to how ecosystems may change with the loss or gain of species.
Litter Decomposition in Response to Nitrogen Addition and Soil Warming at Harvard Forest 2010-2012
The purpose of this study is to examine whether two environmental change stressors (warming and nitrogen deposition) differentially impact litter decomposition. We investigated this using a two year litterbag decomposition experiment at the chronic N amendment experiment and the Barre Woods Soil warming experiment, and measured litter decay dynamics, enzyme activities and litter chemistry. In both years mass loss of the mixed litter was suppressed under N addition, with most of the mass loss observed in the first year compared to the second year (70% and 30% of total mass loss, respectively). Both years showed either increased activity for some hydrolytic enzymes (e.g. cellobiohydrolase) or no difference (e.g. ß-N-acetylglucosaminidase) with increased N. The lignolytic enzymes (e.g. peroxidases) showed no difference in activity in the first year, but had a highly reduced activity in year 2 under elevated N conditions. Soil warming did not significantly affect litter mass loss, and only had an effect on the activity of a few enzymes. In the oak reciprocal litterbag study, decay of oak litter originating from the highest N addition plot was negatively affected by simulated N deposition in the first year of decomposition, while after two years, simulated N deposition negatively affected all litter, and litter originating from the highest N addition plot decayed more slowly than control litter even without added N (i.e. in the control plot). In addition, in the first year of decomposition lignolytic enzyme activities were suppressed in litter originating from the N addition treatments, but due to simulated N deposition in year two.
Warming Effects on Microbial Structure and Decomposition at Harvard Forest 2011
Because microorganisms are sensitive to temperature, ongoing global warming is predicted to influence microbial community structure and function. We used large-scale warming experiments established at two sites near the northern and southern boundaries of US eastern deciduous forests to explore how microbial communities and their function respond to warming at sites with differing climatic regimes. Soil microbial community structure and function responded to warming at the southern but not the northern site. However, changes in microbial community structure and function at the southern site did not result in changes in cellulose decomposition rates. While most global change models rest on the assumption that taxa will respond similarly to warming across sites and their ranges, these results suggest that the responses of microorganisms to warming may be mediated by differences across the geographic boundaries of ecosystems.
Short example of Biceps Brachii muscle surface HDEMG decomposition using the DEMUSE Tool
<p>This dataset contains 4 examples of synthetic high density surface EMG signals of the Biceps Brachii muscle and results of their decomposition into separate motor unit activity. It is intended as a demonstration of the DEMUSE Tool software for sEMG decomposition and as a basis for practical example of dataset preparation for the HybridNeuro project webinar on Data management and ethics (<a href="https://www.hybridneuro.feri.um.si/results.html#webinars">https://www.hybridneuro.feri.um.si/results.html#webinars</a>). Two sets of data are included: the raw simulated sEMG signals and the results of decomposition of those signals with the DEMUSE Tool.</p>
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra regrowth after a thermal erosion event: Simulation J - doubled Phase II decomposition
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 100 years of tussock tundra recovery after a thermal erosion event. This simulation is of TEF recovery with doubled Phase II soil organic matter decomposition compared to the base simulation. Data is presented for day 250 of each year.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra regrowth after a thermal erosion event: Simulation I - doubled Phase I decomposition
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 100 years of tussock tundra recovery after a thermal erosion event. This simulation is of TEF recovery with doubled Phase I soil organic matter decomposition compared to the base simulation. Data is presented for day 250 of each year.
Long-Term Decomposition Plots at Harvard Forest 1990-2009
Mass loss (estimated by decreases in density) and total nitrogen content (TKN) is measured in decaying logs (1.2 m long and diameter range: 6-50 cm) and sticks (10-30 cm long and diameter less than 5 cm) of red pine (Pinus resinosa Ait.) and red maple (Acer rubrum L.) Logs and sticks were collected from freshly cut trees and placed on the forest floor in a red pine plantation (pine only) or a nearby red maple dominated deciduous forest (maple only) in the Prospect Hill tract. Maple logs and sticks lost density faster than did pine. Total Kjeldahl Nitrogen is measured as a percent of organic matter in red pine and red maple logs. N content differed significantly according to state of decay (p=0.002) but not between species. Total organic phosphorus in Kjeldahl digests is measured as a percent of organic matter in red pine and red maple logs. P content differed significantly according to state of decay (p=0.0008) but not between species.
Dataset for Accuracy of Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and CNN-LSTM Model
<p>This research paper introduces a deep learning hybrid model employing Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting.The proposed method integrates the Variational Mode Decomposition (VMD) algo-rithm with the CNN-LSTM model to predict PV power generation from a solar farm in Boussada, Algeria, from January 1, 2019, to December 31, 2020. The performance of the developed model is benchmarked against other deep learning models (VMD-CNN, VMD-LSTM, CNN-LSTM) across various time horizons (15, 30, and 60 minutes) to provide a comprehensive evaluation. Our findings exhibit greater performance of the developed model compared to other architectures, showcasing promising results in solar power forecasting. This research contributes to the main goal of enhancing EMS by providing accurate solar energy forecasts.</p>
Dataset of the manuscript "Assessing the influence of Eisenia andrei on the decomposition of Casuarina equisetifolia litter in vermicompost."
<p>Data generated during an experiment of decomposition of <em>Casuarina equisetifolia</em> litter by the application of vermicompost (VC) or the combination vermicompost + the earthworm <em>Eisenia andrei</em> (E).</p> <p>Six files are included:</p> <p>"<strong>readme.csv</strong>" is a file where we explain the meaning of each column (and in which units is expressed) in each of the other five files.</p> <p>"<strong>earthworm_N_biomass.csv</strong>" is a table with the number of <em>Eisenia andrei</em> individuals and the total earthworm fresh weight in each of the experimental units we sampled</p> <p><strong>"FTIR_spectra.csv" </strong>is a file with the raw spectral data we obtained from the litter by Fourier Transform Infrared spectroscopy combined with Attenuated Total Reflectance (FTIR-ATR). First column indicate the wavenumber (cm-1) and the other columns indicate the absorbance values of each litter sample for each wavenumber.</p> <p><strong>"litter_chemical_composition.csv"</strong> is a file with the raw data of the concentrations of different chemical elements measured in <em>C. equisetifolia</em> litter collected at different decomposition times.</p> <p><strong>"litter_mass_loss.csv"</strong> contains the dry weight data of the litter at time 0 and after each collection time, as well as the percentage of litter mass loss with time. .</p> <p>"<strong>mesofaunal_com.</strong><strong>csv</strong>" are the numbers of individuals of several groups of mesofaunal organisms (collembolans, mites, and others) we recovered in each of our experimental units.</p>
Oak litter decomposition parameters across 19 Nutrient Network grassland sites in response to NPK and herbivore exclusion treatments
These data are from a study examining how herbivory and nutrient supply affect long-term aboveground decomposition. A novel oak litter was decomposed across 19 grassland sites of the Nutrient Network distributed experiment. At each site, a full-factorial experiment of combined nitrogen, phosphorus, and potassium plus micronutrients ('control' or 'NPK') and mammalian herbivore exclusion ('fencing') was carried out in a randomized block design. The duration of the decomposition experiment varied by site but litter bags were harvested at approximately annual intervals for up to seven years. Litter decay parameters were calculated using four alternative statistical models of litter decomposition. Covariate data describing site and plot-level abiotic and biotic characteristics were also measured, including climate, atmospheric nitrogen deposition, live and dead aboveground biomass, and percent cover.
Decomposition and lignin content of wooden dowels deployed in a saltmarsh for 3, 5, or 7 years at Goat Island, North Inlet, Georgetown SC, 2017-2024.
Wooden dowels were used to examine organic matter decomposition in saltmarsh soils. Dowels were inserted to a depth of 45cm in control and fertilized marsh plots. They were harvested after 3, 5 or 7 years and sectioned into 5-cm segments. Weight loss of the dowel segments was used to quantify the fate of labile organic matter, while change in lignin content was used to quantify the fate of refractory organic matter.
Effects of Nitrogen Fertilization on Litter and Soil Decomposition: Gravimetric Soil Moisture
The influence of inorganic nitrogen (N) inputs on decomposition is poorly understood. Some prior studies suggest that N may reduce the decomposition of substrates with high concentrations of lignin via inhibitory effects on the activity of lignin-degrading enzymes, although such inhibition has not always been demonstrated. The purpose of E145 was to study the effects of nitrogen (N) addition on decomposition of seven substrates ranging in initial lignin concentrations (from 7.4 - 25.6%) over five years in eight different grassland and forest sites in central Minnesota.
Effects of ectomycorrhizal fungi on pine litter decomposition in temperate pine forests in California, Florida, and Minnesota
This experiment is designed to assess the generality of the effect of ECM fungi on leaf litter decomposition in temperate pine forests. To assess ECM fungal effects on decomposition, we established and ECM fungal knockdown experiment (via trenching) in nine temperate pine forests in California, Florida, and Minnesota. In litter bags incubated (July 2021-July 2022) in paired trenched and untrenched plots at each site we compared leaf litter decomposition (of native pine litter and a common Pinus strobus litter), fungal community composition (via high throughput sequencing), fungal abundance (via qPCR), decomposition enzyme expression, and soil nutrient availability. Contrary to widely cited theory and other results from a subset of our field sites, we found that ECM fungi either increased or did not impact pine litter decomposition in temperate pine forests.
Patterns of root biomass, productivity, turnover, and decomposition in riverine and scrub mangroves in the Everglades, Florida, USA, immediate-post-Irma, 2018-2019, and post-Irma, 2023-2024
Mangrove root biomass, productivity, and decomposition in the shallow (0-45 cm depth) root zone were estimated at Florida Coastal Everglades Long Term Ecological Research (FCE-LTER) Program Shark River (SRS4, SRS5, SRS6, SRS7) and Taylor River (TS/Ph6b, TS/Ph7b) mangrove sites during 2018-2019 and 2023-2024 following Hurricane Irma’s impacts in September 2017. Root biomass was estimated at all sites during both immediate-post-Irma (March 2018) and post-Irma (February-May 2023) periods with a PVC coring device (10.2 cm diameter x 45 cm length) using the same sampling protocol previously published for the study area (Castañeda-Moya et al. 2011). After collection, root cores were processed individually and initially rinsed with water through a 1-mm screen mesh to remove soil particles. Roots were separated manually based on their buoyancy, turgor, and color into biomass (live) and necromass (dead) components (Castañeda-Moya et al. 2011; Cormier et al. 2015; Medina-Calderon et al. 2021). Live roots were further sorted into three size diameter classes including fine (<2 mm), small (2-5 mm), and coarse (5-20 mm). Roots greater than 20 mm in diameter were not included in this study due to sampling limitations (i.e., core area). All root samples were oven-dried at 60°C to a constant mass and weighed to estimate root biomass and necromass (g m-2). Root productivity was estimated with the ingrowth core technique (Vogt et al., 1998) during both the immediate-post-Irma and post-Irma periods using the same sampling protocol previously published for the study area (Castañeda-Moya et al. 2011). Ingrowth cores (10.2 cm diameter x 45 cm length) were made of synthetic material (3 mm mesh) and filled with root-free commercial sphagnum peat moss. This material has similar soil properties (i.e., bulk density, organic matter content, total C and N) as mangrove peat in our study sites. Ingrowth cores were installed in each of the cored holes formed during sampling of root biomass. At each
Decomposition and lignin content of wooden dowels deployed in a salt- or brackish marsh for 3, 5, or 7 years at the Plum Island Ecosystem LTER site, Rowley MA, 2017-2024.
Wooden dowels were used to examine organic matter decomposition in salt- and brackish marsh soils. Dowels were inserted to a depth of 45cm in control and fertilized marsh plots. They were harvested after 3, 5 or 7 years and sectioned into 5-cm segments. Weight loss of the dowel segments was used to quantify the fate of labile organic matter while change in lignin content was used to quantify the fate of refractory organic matter.
Lagrangian Decomposition of the Meridional Heat Transport at 26.5N - Water Parcel Crossings of the RAPID 26.5N Array
<p>This dataset contains the initial and final positions and properties of Lagrangian trajectories evaluated using 5-day mean velocity and tracer fields output from the ORCA0083-N06 ocean sea-ice model hindcast (1958-2015). Numerical water parcels are initialised to sample the full-depth southward transport across the RAPID 26.5N array every month during 2004-2015. Water parcels are advected backwards-in-time using a bespoke version of TRACMASS v7.1 Lagrangian particle tracking tool which enables users to specify a custom domain using a mask netCDF file.</p><p>Particles are initialised on the first-available day of each month (based on the centre of the model 5-day mean field windows) between 2004 and 2015 (inclusive) before being advected backwards-in-time within the North Atlantic Ocean until any one of four termination conditions are met: (1) water parcels reach the RAPID 26.5N array, (2) water parcels reach the OSNAP (West or East) arrays in the subpolar North Atlantic, (3) water parcels reach either the English Channel or Gibraltar Strait, or (4) particles reach the maximum advection time of 25-years. The 25-year maximum advection time ensures that we adequately resolve the subtropical gyre circulation north to the RAPID 26.5N array. The pathway transporting dense North Atlantic Deep Water from the OSNAP arrays to RAPID at 26.5N is not fully resolved in this Lagrangian experiment since these water parcels transit on multi-decadal timescales.</p><p>The number of water parcels initialised in each model-grid cell scales with the total northward transport through that cell, such that the maximum possible transport conveyed by any single particle is 5.0 mSv (mSv == 10-3 Sv), enabling the calculation of robust Lagrangian statistics. In reality, the average. water parcel has an associated volume transport of 3.3 mSv which is conserved throughout its circulation.</p><p>Water parcel locations (converted to geographical coordinates) and properties (conservative temperature, absolute salinity, potential density [TEOS-10]) are output on every model-grid cell crossing. TRACMASS determines particle properties on grid-cell crossings by taking the average of the properties stored at the nearest two T-grid points. Here, we provide the initial and final locations and properties of all water parcels initialised from RAPID 26.5N.</p><p>All Lagrangian experiments were completed using the JASMIN High-Performance Computing facility (<a href="https://jasmin.ac.uk">https://jasmin.ac.uk</a>).</p><p><strong>For a complete description of the ORCA0083-N06 hindcast configuration see:</strong> Moat et al. (2016).</p><p><strong>For a complete description of TRACMASS v7.1 see</strong>: <a href="https://www.tracmass.org">https://www.tracmass.org</a></p>
Investigation of the properties of conductivity signals in BK channels by Empirical Mode Decomposition
<p>The idea of the project is the comprehensive time-frequency analysis of ion current data registered from BK channels of the different cell lines and measured under the different experimental conditions. Decomposition of signals into individual frequency modes and application of non-linear measures in the form of Information Entropy or Hurst exponent to individual signal components will allow for a more detailed analysis of the information hidden behind the complex ionic conduction sequences. The sample data contains patch-clamp sequences. </p>
Clonal decomposition and DNA replication states defined by scaled single cell genome sequencing
<p><strong>OV2295 Tables</strong></p> <p>ov2295_breakpoint_counts.csv.gz: Table of breakpoint counts per cell</p> <ul> <li>prediction_id: identifier for the breakpoint</li> <li>cell_id: identifier for the cell</li> <li>read_count: number of reads</li> <li>library_id: identifier for the DNA library</li> <li>sample_id: identifier for the sequenced sample</li> <li>chromosome_1: chromosome of breakend 1</li> <li>strand_1: orientation of break end 1</li> <li>position_1: position of break end 1</li> <li>chromosome_2: chromosome of breakend 2</li> <li>strand_2: orientation of break end 2</li> <li>position_2: position of break end 2</li> </ul> <p>ov2295_cell_cn.csv.gz: Table of cell specific copy number</p> <ul> <li>cell_id: identifier for the cell</li> <li>sample_id: identifier for the sequenced sample</li> <li>library_id: identifier for the DNA library</li> <li>chr: chromosome of bin</li> <li>start: start of bin</li> <li>end: end of bin</li> <li>reads: number of reads</li> <li>copy: raw normalized copy number</li> <li>state: copy number state</li> <li>gc: percent gc of the bin</li> <li>map: average mappability of the bin</li> </ul> <p>ov2295_cell_metrics.csv.gz: Table of cell metrics</p> <ul> <li>cell_id: identifier of the cell</li> <li>unpaired_mapped_reads: number of unpaired mapped reads</li> <li>paired_mapped_reads: number of mapped reads that were properly paired</li> <li>unpaired_duplicate_reads: number of unpaired duplicated reads</li> <li>paired_duplicate_reads: number of paired reads that were also marked as duplicate</li> <li>unmapped_reads: number of unmapped reads</li> <li>percent_duplicate_reads: percentage of duplicate reads</li> <li>estimated_library_size: scaled total number of mapped reads</li> <li>total_reads: total number of reads, regardless of mapping status</li> <li>total_mapped_reads: total number of mapped reads</li> <li>total_duplicate_reads: number of duplicate reads</li> <li>total_properly_paired: number of properly paired reads</li> <li>coverage_breadth: percentage of genome covered by some read</li> <li>coverage_depth: average reads per nucleotide position in the genome</li> <li>median_insert_size: median insert size between paired reads</li> <li>mean_insert_size: mean insert size between paired reads</li> <li>standard_deviation_insert_size: standard deviation of the insert size between paired reads</li> <li>index_sequence: index sequence of the adaptor sequence</li> <li>column: column of the cell on the nanowell chip</li> <li>img_col: column of the cell from the perspective of the microscope</li> <li>index_i5: id of the i5 index adapter sequence</li> <li>sample_type: type of the sample</li> <li>primer_i7: id of the i5 index primer sequence</li> <li>experimental_condition: experimental treatment of the cell, includes controls</li> <li>index_i7: id of the i7 index adapter sequence</li> <li>cell_call: living/dead classification of the cell based on staining usually, C1 == living, C2 == dead</li> <li>sample_id: name of the sample</li> <li>primer_i5: id of the i5 index primer sequence</li> <li>row: row of the cell on the nanowell chip</li> <li>library_id: identifier for the DNA library</li> <li>index: ignored</li> <li>multiplier: during parameter searching, the set [1..6] that was chosen</li> <li>MSRSI_non_integerness: median of segment residuals from segment integer copy number states</li> <li>MBRSI_dispersion_non_integerness: median of bin residuals from segment integer copy number states</li> <li>MBRSM_dispersion: median of bin residuals from segment median copy number values</li> <li>autocorrelation_hmmcopy: hmmcopy copy autocorrelation</li> <li>cv_hmmcopy: ignored</li> <li>empty_bins_hmmcopy: number of empty bins in hmmcopy</li> <li>mad_hmmcopy: median absolute deviation of hmmcopy copy</li> <li>mean_hmmcopy_reads_per_bin: mean reads per hmmcopy bin</li> <li>median_hmmcopy_reads_per_bin: median reads per hmmcopy bin</li> <li>std_hmmcopy_reads_per_bin: standard deviation value of reads in hmmcopy bins</li> <li>total_halfiness: summed halfiness penality score of the cell</li> <li>total_mapped_reads_hmmcopy: total mapped reads in all hmmcopy bins</li> <li>scaled_halfiness: summed scaled halfiness penalty score of the cell</li> <li>mean_state_mads: mean value for all median absolute deviation scores for each state</li> <li>mean_state_vars: variance value for all median absolute deviation scores for each state</li> <li>mad_neutral_state: median absolute deviation score of the neutral 2 copy state</li> <li>breakpoints: number of breakpoints, as indicated by state changes not at the ends of chromosomes</li> <li>mean_copy: mean hmmcopy copy value</li> <li>state_mode: the most commonly occuring state</li> <li>log_likelihood: hmmcopy log likelihood for the cell</li> <li>true_multiplier: the exact decimal value used to scale the copy number for segmentation</li> <li>order: order of the cell in the hierarchical clustering tree</li> <li>quality: random forest classifier proability score that cell is good</li> </ul> <p>ov2295_clone_alleles.csv.gz: Table of clone specific allele data</p> <ul> <li>chr: chromosome of bin</li> <li>start: start of bin</li> <li>end: end of bin</li> <li>hap_label: haplotype block identifier</li> <li>clone_id: clone identifier</li> <li>allele_1_sum: number of reads for allele 1 of the haplotype block</li> <li>allele_2_sum: number of reads for allele 2 of the haplotype block</li> <li>total_counts_sum: total reads for the haplotype block</li> </ul> <p>ov2295_clone_breakpoints.csv.gz: Table of breakpoints per clone for OV2295 samples. Columns:</p> <ul> <li>prediction_id: identifier for the breakpoint</li> <li>chromosome_1: chromosome of breakend 1</li> <li>strand_1: orientation of break end 1</li> <li>position_1: position of break end 1</li> <li>chromosome_2: chromosome of breakend 2</li> <li>strand_2: orientation of break end 2</li> <li>position_2: position of break end 2</li> <li>clone_id: clone identifier</li> <li>read_count: number of reads</li> <li>is_present: presence=1, absent=0</li> </ul> <p>ov2295_clone_clusters.csv.gz: Table of cell clusters as putative clones</p> <ul> <li>cell_id: identifier for the cell</li> <li>clone_id: clone identifier</li> </ul> <p>ov2295_clone_cn.csv.gz: Table of allele specific copy number per clone for OV2295 samples. Columns:</p> <ul> <li>chr: chromosome of bin</li> <li>start: start of bin</li> <li>end: end of bin</li> <li>total_cn: HMMCopy predicted total copy number </li> <li>minor_cn: HMM predicted minor copy number </li> <li>major_cn: HMM predicted major copy number </li> <li>clone_id: clone identifier</li> </ul> <p>ov2295_clone_snvs.csv.gz: Table of SNVs per clone for OV2295 samples. Columns:</p> <ul> <li>chrom: chromosome</li> <li>coord: genome position</li> <li>ref: reference nucleotide</li> <li>alt: alternate nucleotide</li> <li>clone_id: clone identifier</li> <li>ref_counts: number of reads at this position matching the reference nucleotide</li> <li>alt_counts: number of reads at this position matching the alternate nucleotide</li> <li>total_counts: total number of reads at this position</li> <li>is_present: presence=0, absent=1</li> <li>is_het: is heterozygous</li> <li>is_hom: is homozygous for the alternate</li> </ul> <p>ov2295_nodes.csv.gz: Table of phylogenetic information for SNV evolution</p> <ul> <li>variant_id: identifier for the SNV as chrom:coord:ref:alt</li> <li>node: node in the phylogenetic tree</li> <li>loss: probability the SNV was lost at this node</li> <li>origin: probability the SNV originated at this node</li> <li>presence: probability the SNV is present at this node</li> <li>ml_origin: binary indicator the SNV originated at this node</li> <li>ml_presence: binary indicator the SNV is present at this node</li> <li>ml_loss: binary indicator the SNV was lost at this node</li> </ul> <p>ov2295_snv_counts.csv.gz: Table of SNV counts</p> <ul> <li>chrom: chromosome</li> <li>coord: genome position</li> <li>ref: reference nucleotide</li> <li>alt: alternate nucleotide</li> <li>ref_counts: number of reads at this position matching the reference nucleotide</li> <li>alt_counts: number of reads at this position matching the alternate nucleotide</li> <li>cell_id: identifier for the cell</li> <li>total_counts: total number of reads at this position</li> <li>sample_id: identifier for the sequenced sample</li> </ul> <p>ov2295_tree.pickle: Phylogenetic tree in python pickle format. Requires installation of the stochastic dollo code at: https://bitbucket.org/dranew/dollo, version 0.4.2.</p> <p>Note the following sample mapping: ‘SA922’: ‘OV2295(R2)’, ‘SA921’: ‘TOV2295(R)’, ‘SA1090’: ‘OV2295’,</p> <p><strong>Plots</strong></p> <p>ov_supp_clone_allele_cn.png: Clone allele ratios for each OV2295 sample.</p> <p>ov_supp_clone_total_cn.png: Clone copy number for each OV2295 sample.</p> <p>ov_supp_sample_total_cn.png: Bulk copy number for each OV2295 sample.</p> <p>ov_supp_sample_allele_cn.png: Bulk allele ratios for each OV2295 sample.</p>
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.