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51,102 results for “analysis”
NEON distributed initial soil characterization dataset (DP1.10047.001) modified for statistical analysis of organic carbon and extractable metals in Hall and Thompson (2021)
We compiled National Ecological Observatory Network (NEON) datasets related to the initial distributed soil sampling effort and subsetted them (removed samples with missing values for certain variables, and several samples with extreme values) for use in statistical analyses to describe relationships between soil organic carbon (SOC) and metals measured in several soil chemical extractions. The NEON provisional data products we used were DP1.10047.001 and DP1.10008.001, which were subsequently combined by NEON as a single data product DP1.10047.001, “Soil physical and chemical properties, distributed initial characterization”. These datasets were used for the analyses reported in a manuscript by Hall and Thompson (2021) in the Soil Science Society of America Journal.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Undisturbed tussock tundra
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 undisturbed tussock tundra. Data is presented for day 250 of each year.
North Temperate Lakes LTER: Patterns of Soil Phosphorus - Y Plot Analysis 2001
In natural soils, patterns of variance are generated by driving forces such as parent materials, climate, hydrology, relief, disturbance and biological activity. These drivers, operating at particular scales and interacting with other drivers across scales, create a complex pattern of soil variability. Human activity may change the natural patterns of variance by changing the scale at which the governing processes are operating or the governing processes that are dominant at a given scale. In the case of soils and phosphorus (P) concentrations, this may involve changing dominant forces from plant-soil interactions and parent material to fertilizer inputs. Here, we examine the hypothesis that human activity changes natural patterns of variance in soil P concentrations across several spatial scales. We measured soil P concentrations and variability at 3 distinct levels of analysis - among sites, within a field, and within a 10-m diameter plot - and across 4 management regimes - remnant prairie, lawns, cash grain farms, and dairies. Variance changed across scale in any one management regime and across management regimes at the same scale. Rescaling the pattern of P accumulation and variability has implications for managing P runoff from uplands. For sample sites on private property, specific site location information, such as GPS coordinates, is not included in these datasets. If you have a need for this information, please get in touch with the contact person listed above Number of sites: 30
Sevilleta LTER Vegetation Sample Catalog- Ground Samples for Chemical Analysis
Several long-term studies at the Sevilleta LTER measure net primary production (NPP) across ecosystems and treatments. Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. The NPP weight data (SEV 157) is obtained by harvesting a series of covers for species observed during plot sampling. These species are always harvested from habitat comparable to the plots in which they were recorded. This data is then used to make volumetric measurements of species and build regressions correlating biomass and volume. From these calculations, seasonal biomass and seasonal and annual NPP are determined. These sampled are then vouchered for use to do analyses of inorganic and organic components such as carbon, nitrogen, and phosphorous as well as and other macro and micro nutrients and organic components such as cellulose and lignin.
Meta-analysis reveals controls on oyster predation 1977-2021
This dataset was created for investigating the patterns and drivers of variation in predation strength on oysters. Predation on oysters has been the subject of intense study because oysters are vital to creating coastal habitat, sustaining biodiversity, and enhancing or stabilizing important ecosystem functions. Understanding how predators affect oyster populations is important given the global decline in oysters and the substantial efforts focused on re-establishment and conservation. Despite widespread losses of oyster reefs, there have been no standardized and integrated quantitative assessments of the influence of predators on oysters. Therefore, this dataset was created by combining the results of 49 peer-reviewed articles reporting 384 experiments on oyster predation. These field and laboratory experiments tested whether the presence of predators affects oyster mortality or recruitment. The combined data was used in a meta-analysis, the results of which are published in the paper titled Meta-analysis reveals controls on oyster predation (Tedford and Castorani 2022), which represents the first quantitative synthesis to show that across a range of environments, predators have strong impacts on oysters.
Meta-analysis of ecosystem services associated with oyster restoration on the Eastern and Gulf coasts of the US
We conducted a meta-analysis to systematically quantify the success and uncertainty of oyster reef restoration for a suite of biological, biogeochemical, and physical ecosystem services relative to both degraded and natural reference habitats. We focused on the eastern oyster, Crassostrea virginica. To evaluate whether restored eastern oyster reefs enhance ecosystem services relative to unaltered, degraded habitats and whether restored reefs provide ecosystem services equivalent to reference reefs, we synthesized data and calculated log response ratios for 245 restored-degraded reef pairs and 136 restored-reference reef pairs from 106 publications collected along 3500 km of U.S. Gulf of Mexico and Atlantic coastlines.
Cost Analysis TBI
Open the record for dataset details and reuse information.
Supplementary Material for A Global Analysis of Dark Matter Signals from 27 Dwarf Spheroidal Galaxies using 11 Years of Fermi-LAT Observations
<p><strong>Description of the Supplementary Data</strong></p> <p>This record contains tabulated Bayesian and frequentist exclusion limits, profile likelihood maps and posterior probability maps for the publication S. Hoof, A. Geringer-Sameth, and R. Trotta, “<i>A Global Analysis of Dark Matter Signals from 27 Dwarf Spheroidal Galaxies using 11 Years of Fermi-LAT Observations</i>,” <a href="https://doi.org/10.1088/1475-7516/2020/02/012">JCAP 02 (2020) 012</a> (also available on the <a href="https://arxiv.org/abs/1812.06986">arXiv</a>). The dwarf spheroidal galaxies considered in this work are (in alphabetical order): Aquarius II, Boötes I, Canes Venatici I, Canes Venatici II, Carina, Carina II, Coma Berenices, Draco, Draco II, Fornax, Grus I, Hercules, Horologium I, Leo I, Leo II, Leo IV, Leo V, Pegasus III, Pisces II, Reticulum II, Sculptor, Segue 1, Sextans, Tucana II, Ursa Major I, Ursa Major II, and Ursa Minor.</p> <p>This record consists of the following files, which correspond to the limits presented Figures 9 and 10 of the paper. The files can be downloaded individually or obtained by downloading and unpacking the <code>record_2612268.zip</code>. In what follows,<code><strong>[CHANNEL]</strong></code> refers to the annihilation channel used, i.e. <i>e<sup>+</sup> e<sup>-</sup></i>, <i>μ<sup>+</sup> μ<sup>-</sup></i>, <i>τ<sup>+</sup> τ<sup>-</sup></i>, <i>b b̄</i>, <i>c c̄</i>, <i>t t̄</i>, <i>g g</i>, <i>W<sup>+</sup> W<sup>-</sup></i>, and <i>Z Z</i>. We also provide a simple plotting script for <code>Python</code>, named <code>plotting_script.py</code>, which provides basic plotting routines for all files.</p> <ul> <li>One-dimensional limits on <i><σ v></i>. The files <code>oneD_frequentist_limits_<strong>[CHANNEL]</strong>_channel.txt</code> contain the frequentist limits (at 95% confidence level, 1 degree of freedom) given the value of the WIMP mass <i>m<sub>χ</sub></i> tabulated there. The files <code>oneD_Bayesian_limits_<strong>[CHANNEL]</strong>_channel.txt</code> contain the Bayesian limit (95% credibility conditioned on the mass <i>m<sub>χ</sub></i> tabulated there).</li> <li>Two-dimensional grid of profile likelihood values. The files <code>twoD_profile_likelihood_map_<strong>[CHANNEL]</strong>_channel.txt</code> contain the natural logarithm of the profile likelihood w.r.t. the global best-fit likelihood value for that channel together with the corresponding values of <i>m<sub>χ</sub></i> and <i><σ v></i>. Note that for obtaining the limits in Fig. 10, which are conditioned on the WIMP mass, one needs to rescale the profile likelihood values with the maximum profile likelihood for a given WIMP mass.</li> <li>Two-dimensional grid of posterior probabilities for each combination of <i>m<sub>χ</sub></i> and <i><σ v></i>. The files <code>twoD_posterior_probability_map_<strong>[CHANNEL]</strong>_channel.txt</code> contain probabilities (obtained using a log-uniform prior on <i><σ v></i>) together with the corresponding values of <i>m<sub>χ</sub></i> and <i><σ v></i>. The tabulated values of <i>m<sub>χ</sub></i> and <i><σ v></i> correspond to the centres of the respective bins in <i>m<sub>χ</sub></i> and <i><σ v></i> and the posterior probability contained in them (the total posterior probability sums to 1).</li> </ul> <p>Please contact the authors if you require different data or have any questions regarding this data set.</p>
RIBuild: Analysis of models for failure
<p>The dataset consists of data used for analysing a number of models, each related to a specific failure mode or failure mechanism that affects the material properties of building materials. Three RIBuild partners (KUL, UNIVPM, RISE) were responsible of performing laboratory tests to evaluate the models chosen to characterize a specific failure mode (frost, algae, mould). One RIBuild partner (DTU/AAU) used measurement data from a WP3 test setup to validate simulations of wood rot in wooden beam ends.</p> <p>Further details to be found in RIBuild deliverable D2.2.</p> <p>Overview of data files to be found in 'RIBuild data WP2 Model analysis' as part of this dataset.</p>
Catalysis of Tos-Gly-Pro-Arg-p-nitroanilide by α-thrombin in the presence of an anticoagulant produced by D. andersoni (dataset formatted for analysis by interferENZY)
<p><strong>Main description</strong></p> <p>This dataset depicts the catalysis of the chromogenic substrate Tos-Gly-Pro-Arg-p-nitroanilide by α-thrombin in the presence of an anticoagulant produced by <em>Dermacentor andersoni</em>, for fixed concentration of enzyme (and modulator) and variation of concentration of initial substrate. It was originally documented in <em>Biophysical Chemistry 252 (2019) 106193</em> (<a href="https://doi.org/10.1016/j.bpc.2019.106193">https://doi.org/10.1016/j.bpc.2019.106193</a>), and <em>PNAS 116 (28) 13873-13878</em> (<a href="https://doi.org/10.1073/pnas.1905177116">https://doi.org/10.1073/pnas.1905177116</a>), and then used as a study case for the webserver interferENZY (a web-based tool for enzymatic assay validation and standardized kinetic analysis; visit <a href="https://interferenzy.i3s.up.pt">https://interferenzy.i3s.up.pt</a> for more information). To this end, it was converted to the format here presented: tab-separated *.txt input required for interferENZY analysis.</p> <p> </p> <p><strong>Dataset organization</strong></p> <p>Line 1: Tab-separated initial concentrations of substrate Tos-Gly-Pro-Arg-p-nitroanilide in micromolar (µM) concentration</p> <p>Line 2: Concentration of enzyme (0.15 nM)</p> <p>Line 3: Units of time</p> <p>Line 4: Units of concentration for substrate values and measurements</p> <p>Line 5: Dataset name</p> <p>Line 6 and downwards: Tab-separated column-pairs of the progress curves (time,Product) corresponding to the indicated values of initial concentrations of substrate in line 1</p> <p> </p> <p><strong>Contact information:</strong></p> <p>Maria Filipa Pinto (mfpinto@i3s.up.pt)<br> Pedro M. Martins (pmartins@ibmc.up.pt)</p> <p>i3S – Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, 4200-135 Porto, Portugal. Telephone number: +351 226 074 900</p>
Catalysis of Ac-DEVD-AMC by procaspase-3 (dataset formatted for analysis by interferENZY)
<p><strong>Main description</strong></p> <p>This dataset depicts the catalysis of the fluorogenic substrate Ac-DEVD-AMC to the fluorescent substrate AMC by recombinant procaspase-3 obtained in yeast cell extracts, for fixed concentration of enzyme and variation of concentration of initial substrate. It was originally documented in <em>Biophysical Chemistry 252 (2019) 106193</em> (<a href="https://doi.org/10.1016/j.bpc.2019.106193">https://doi.org/10.1016/j.bpc.2019.106193</a>), and then used as a study case for the webserver interferENZY (a web-based tool for enzymatic assay validation and standardized kinetic analysis; visit <a href="https://interferenzy.i3s.up.pt">https://interferenzy.i3s.up.pt</a> for more information). To this end, it was converted to the format here presented: tab-separated *.txt input required for interferENZY analysis.</p> <p> </p> <p><strong>Dataset organization</strong></p> <p>Line 1: Tab-separated initial concentrations of substrate Ac-DEVD-AMC in micromolar (µM) concentration</p> <p>Line 2: Concentration of protein in yeast extract (0.123 mg/mL)</p> <p>Line 3: Units of time</p> <p>Line 4: Units of concentration for substrate values and measurements</p> <p>Line 5: Dataset name</p> <p>Line 6 and downwards: Tab-separated column-pairs of the progress curves (time,Product) corresponding to the indicated values of initial concentrations of substrate in line 1</p> <p> </p> <p><strong>Contact information:</strong></p> <p>Maria Filipa Pinto (mfpinto@i3s.up.pt)<br> Pedro M. Martins (pmartins@ibmc.up.pt)</p> <p>i3S – Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, 4200-135 Porto, Portugal. Telephone number: +351 226 074 900</p>
Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text
<p>Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.</p>
A Sentiment Analysis Dataset for Code-Mixed Malayalam-English
<p>There is an increasing demand for sentiment analysis of text from social media which are mostly code-mixed. Systems trained on monolingual data fail for code-mixed data due to the complexity of mixing at different levels of the text. However, very few resources are available for code-mixed data to create models specific for this data. Although much research in multilingual and cross-lingual sentiment analysis has used semi-supervised or unsupervised methods, supervised methods still performs better. Only a few datasets for popular languages such as English-Spanish, English-Hindi, and English-Chinese are available. There are no resources available for Malayalam-English code-mixed data. This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators. This gold standard corpus obtained a Krippendorff’s alpha above 0.8 for the dataset. We use this new corpus to provide the benchmark for sentiment analysis in Malayalam-English code-mixed texts.</p>
User-centered Usability Analysis of 41 Open Government Data Portals
<p>The data were collected during the user-centered analysis of usability of 41 open government data portals including EU27, applying a common methodology to them, considering aspects such as specification of open data set, feedback and requests, further broken down into 14 sub-criteria. Each aspect was assessed using a three-level Likert scale (fulfilled - 3, partially fulfilled - 2, and unfulfilled – 1), that belongs to the acceptability tasks. This dataset summarises a total of 1640 protocols obtained during the analysis of the selected portals carried out by 40 participants, who were selected on a voluntary basis. This is complemented with 4 summaries of these protocols, which include calculated average scores by category, aspect and country. These data allow comparative analysis of the national open data portals, help to find the key challenges that can negatively impact users’ experience, and identifies portals that can be considered as an example for the less successful open data portals.</p>
Data release for paper "Towards the routine use of subdominant harmonics in gravitational-wave inference: re-analysis of GW190412 with generation X waveform models"
<p>This data release for the paper "Towards the routine use of subdominant harmonics in gravitational-wave inference: re-analysis of GW190412 with generation X waveform models" [<a href="https://arxiv.org/abs/2010.05830">arXiv:2010.2010.05830</a>] contains posterior samples for the GW190412 binary black hole merger event obtained from public GWOSC data with the parallel bilby Bayesian inference package, dynesty nested sampler and a set of waveforms from the "generation X" of phenomenological waveform models: IMRPhenomXAS, IMRPhenomXHM, IMRPhenomXP, IMRPhenomXPHM, IMRPhenomT and IMRPhenomTHM. The provided file is a "meta file" that can be read with the <a href="https://lscsoft.docs.ligo.org/pesummary/">PESummary</a> python package. The posterior samples included correspond to runs [2,6,10,12,14,26] in Table III of the paper (standard settings for each waveform, standar priors and sampler settings of Nlive=2048 and Nact=10 or 50). If you make use of these samples, please cite both this data release and the paper.</p>
IPBES Data Management Tutorials - Session 5.4: Processing and analysis
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The<em> Tools for data management </em>chapter provides IPBES authors with an overview of open source tools used frequently by the scientific community to help it implement data management for the entire data life cycle.</p> <p>This session on <em>processing and analysis </em>reviews common scripting languages for data analysis and processing, such as python and R. </p>
Pan-cancer analysis of mRNA stability for decoding tumour post-transcriptional programs
<p>Supplemental data and analysis files for Perron et al.: "Pan-cancer analysis of mRNA stability for decoding tumour post-transcriptional programs" (<a href="https://www.nature.com/articles/s42003-022-03796-w">https://www.nature.com/articles/s42003-022-03796-w</a>). The .tar.gz files contain read counts associated with various RNA-seq analyses. The .rds files are single R object files that contain various analysis results tables. The .csv files also contain analysis results or sample metadata tables. See <a href="http://csg.lab.mcgill.ca/sup/pancancer_stability/">http://csg.lab.mcgill.ca/sup/pancancer_stability/</a> for a full description of the files.</p>
Supplementary Data for MOCCASIN: A method for correcting known and unknown confounders in RNA-Seq-based splicing analysis
<p>Contents</p> <ol> <li><strong>moccasin_paper_env.yaml</strong>: conda environment file with R and Python packages and modules needed to reproduce analyses.</li> <li><strong>FigureReproduction.zip</strong>: data and code to reproduce main and supplemental figures.</li> <li><strong>MOCCASIN_ExampleDataset.zip</strong>: A small subset of the simulated data with example code to run MOCCASIN.</li> <li><strong>encode_corrected.zip</strong>: Folder with batch-corrected ENCODE differential splicing quantifications (dPSI).</li> </ol> <p> </p> <p> </p> <p>(1) <strong>moccasin_paper_env.yaml</strong></p> <p>Use the moccasin_paper_env.yaml file to create a conda environment from which all analyses for the paper can be reproduced.</p> <pre><code class="language-bash"># need to first install conda. See here: # https://docs.conda.io/en/latest/miniconda.html # Next, create a conda environment: conda env create --name moccasin_paper_env --file moccasin_paper_env.yaml --force # Activate the environment: conda activate moccasin_paper_env</code></pre> <p><br> The only Python packages not included in this environment are MAJIQ & VOILA. Please see majiq.biocipers.org for installation instructions.</p> <p> </p> <p> </p> <p>(2) <strong>FigureReproduction.zip</strong></p> <p>Within FigureReproduction are folders with code and data to reproduce the main and supplemental figures of the publication. Each folder contains data, script(s) and a README.txt with instructions on how to reproduce figures.</p> <p> </p> <p> </p> <p>(3) <strong>MOCCASIN_ExampleDataset.zip</strong></p> <p>Within this folder is an example dataset to test MOCCASIN. The README.txt file contains detailed line-by-line instructions for how to run MOCCASIN and do post-MOCCASIN analyses. In this example, we show how to run MOCCASIN on a group of .majiq samples with one known confounding effect. Also demonstrated is how to run an "explore unknown residuals" analysis as described in the detailed methods in the supplemental of the paper. </p> <p> </p> <p> </p> <p>(4) <strong>encode_corrected.zip</strong></p> <p>Includes a file called ENCODE_BeforeAndAfterMOCCASIN.voila.tsv.zip which includes LSV quantifications before and after MOCCASIN. Each row in the file represents a junction from an LSV. Each column header starts with the prefix "BeforeMOCCASIN" or "AfterMOCCASIN" and headers ending in dPSI corresponds to the dPSI of an ENCODE knockdown vs control experiment. </p>
Comparative proteomics analysis of whole-cell catalyst of K. rhizophila strain SA117 catabolism of SMX
<p><span>Sulfamethoxazole (SMX), an oral sulfonamide antibiotic, presents significant environmental challenges due to its persistence and potential role in promoting antibiotic resistance. The bacterial strain <em><span>Kocuria rhizophila</span></em> SA117, isolated from polluted soils, has demonstrated a remarkable capability to metabolize SMX. Proteomic analysis revealed the presence of various enzymes and metabolic pathways that may contribute to SMX degradation, including those involved in para-aminobenzoate condensation and protocatechuate metabolism. Notably, the genome of SA117 harbors eight monooxygenase genes, including those related to antibiotic biosynthesis and flavin family monooxygenases. Additionally, several cytochrome c-encoding genes, known for their role in respiratory versatility and potential application in bioremediation, were identified. Genes associated with sulfur metabolism, including an iron-sulfur cluster gene cluster (SufB, C, D, R, E) linked to oxidative stress response, were also found. A comparative proteomic study under SMX exposure highlighted significant upregulation of stress-related proteins. These findings underscore the metabolic adaptability of <em><span>Kocuria rhizophila</span></em> SA117 and its potential application in the bioremediation of SMX-contaminated environments.</span></p>
Data From: Exploring Gelatin-A and Mouse Proline-Rich Protein 5 as Probes for Wine Polyphenols analysis by Quartz Crystal Microbalance with Dissipation Monitoring
<p>Polyphenols are essential in winemaking, affecting the wine's quality, color, astringency, bitterness, and chemical stability. Conventional methods for assessing polyphenolic content are both expensive and time-intensive, underscoring the need for new, efficient techniques.</p> <p>The Quartz Crystal Microbalance with Dissipation Monitoring (QCM-D) sensor is recognized for its speed and reliability as a label-free detection tool. This study applies QCM-D to evaluate Gelatin Type A (Gel-A) from porcine skin and Mouse Proline-Rich Protein 5 (MP5) for polyphenol analysis in red wines without pre-treatment. MP5 notably exhibited a linear dissipation signal response with both total polyphenol and hydroxybenzoic acid concentrations. These findings highlight the potential for creating a stand-alone sensor platform for real-time polyphenol monitoring in winemaking.</p>
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.