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184 results for “variability analysis”
Data and Analysis for Kaplanis, Denny, and Raimondi 2024, "Vertical distribution of rocky intertidal organisms shifts with sea-level variability on the Northeast Pacific Coast".
<p>This repository contains all the data and R scripts used to produce all analyses and figures for Kaplanis, Denny, and Raimondi 2024, as well as all intermediate outputs and final figures. To access this content, download and unzip the intertidalvertdist folder (for intertidal vertical distribution). The R Project is titled "intertidalvertdist". All pertinent information needed to access data, replicate the analyses, and produce figures is contained within the README file, but a brief desciption is below.</p> <p><br>Directory Architecture:</p> <p>Data:<br>Contains all data. Within this folder are two subdirectories - Raw Data, and Processed Data. Raw Data are unmanipulated, straight from the data source. Processed Data are outputs from scripted data wrangling and transformations. </p> <p>Within each of these folders are two more subdirectories: Tide Gauge Data, and MARINe Data. These are the two data sources used in this manuscript - monthly sea-level data from The National Oceanic and Atmospheric Administration Center for Operational Oceanographic Products and Services (NOAA CO-OPS) tide gauge stations, and long-term rocky intertidal biological monitoring data from Multi-Agency Rocky Intertidal Network (MARINe) survey sites.</p> <p>Scripts:<br>All R scripts are contained within the Scripts folder. The scripts have the prefix IVD (for intertidal vertical distribution), then a name that indicates the major function of the code. The scripts either downloads data, manipulates data, conducts analyses, and/or produces a figure.</p> <p>Outputs:<br>Any figures and tables from preliminary analyses, but that are not used in the final manuscript, are saved in Outputs.</p> <p>Figures:<br>All final figures and tables are contained in the Figures folder. All figures are produced by scripts, except Figs. 1 and 2, which are schematics produced manually in a graphics editor. This folder contains two other folders: Supplemenatary Figures, and Partial Regression Plots. Partial Regression plots are the same as the final Figures 8-12, except they are grouped by taxa rather than by explanatory variable.</p> <p>Data Processing Workflow - Overview: <br>Tide Gauge Data (Data/Raw Data/Tide Gauge Data/individual stations) were downloaded using the NOAA Co-Ops API URL Builder (https://tidesandcurrents.noaa.gov/api-helper/url-generator.html), merged, then analyzed. Three MARINe data sets from the Coastal Biodiversity Survey (CBS) were accessed via data requests (https://marine.ucsc.edu/explore-the-data/contact/data-request-form.html). The first MARINe dataset (Data/Raw Data/MARINe Data/CBS_Percent Cover Data, both First Sample and Full Sample) was used to determine the top ten most abundant taxa (hereafter termed “dominant taxa”) across CBS survey sites during the monitoring period of 2001-01-01 to 2021-09-30. The second MARINe dataset (Data/Raw Data/MARINe Data/CBS_Elevation Data) was used to describe the upper limits of vertical distribution of dominant taxa through time. The third MARINe dataset (Data/Raw Data/MARINe Data/CBS_Presence Data) was used to visualize latitudinal distribution of taxa.</p> <p>Location information for Tide Gauge Stations and CBS Survey Sites were assembled into a table (Data/Raw Data/CBS_Tide Gauge_Data.csv)</p> <p>Tide Gauge Data were processed first, then MARINe Data. To replicate this workflow follow the steps described in the README file, in order.</p>
Potential Metabolic Activity, Catalase Activity, Performance traits and Morphological variables of 94 individuals belonging to Podarcis muralis species used in the analysis
<p>Potential Metabolic Activity (ETS26_P, ETS31_P, ETS36_P), Catalase Activity (CAT_P), Performance traits (BITE, SPRINT,CLIMB, MANO) and Morphological variables (snout-vent length (SVL), trunk length (TRL), pileus length (PL), head length (HL), head width (HW), head height (HH), fore limb length (FLL) and hind limb length (HLL) of 94 individuals belonging to <em>Podarcis muralis</em> species. The data was used in the analysis of the paper entitled: Is It Function or Fashion? An Integrative Analysis of Morphology, Performance, and Metabolism in a Colour Polymorphic Lizard, by authors Verónica Gomes, Anamarija Žagar, Guillem Pérez i de Lanuza, Tatjana Simčič and Miguel A. Carretero, published in the journal Diversity 2022, 14, 116. <a href="https://doi.org/10.3390/d14020116">https://doi.org/10.3390/d14020116</a></p>
Dataset of Spatial Room Impulse Responses in a Variable Acoustics Room for Six Degrees-of-Freedom Rendering and Analysis
<p>Room acoustics measurements are used in many areas of audio research, from physical acoustics modelling and speech enhancement to virtual reality applications. This paper documents the technical specifications and choices made in the measurement of a dataset of spatial room impulse responses (SRIRs) in a variable acoustics room. Two spherical microphone arrays are used: the mh Acoustics Eigenmike em32 and the Zylia ZM-1, capable of up to fourth- and third-order Ambisonic capture, respectively. The dataset consists of three source and seven receiver positions, repeated with five configurations of the room's acoustics with varying levels of reverberation. Possible applications of the dataset include six degrees-of-freedom (6DoF) analysis and rendering, SRIR interpolation methods, and spatial dereverberation techniques. </p> <p>Accompanying paper on details of the dataset measurement: https://arxiv.org/abs/2111.11882</p> <p>Changelog:</p> <p>V 1.0 - Initial version.<br> V 1.1 - SOFA files updated to latest Matlab API (1.1.3), 'SingleRoomDRIR' convention, with SourcePosition and ListenerPosition z data corrected. Changed ListenerPosition and SourcePosition x data so that it follows the convention of origin in bottom left corner (rather than the previous bottom right). Fixed the swapped x and y labels in 6dof_source_and_receiver_positions.pdf.</p>
Model output and analysis scripts for "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity"
<p>Here, we provide annually averaged model output from a 3000-year control simulation of PlaSim–LSG, a climate model of intermediate complexity. Processed variables and a Jupyter notebook to reproduce all figures of the manuscript (Mehling et al.: "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity") can also be found in this repository.</p> <p>In addition, a Python implementation of the three-box model proposed in the manuscript can be found in the notebook <em>boxmodel.ipynb</em>.</p>
Exploring variability in the diet of depredating sperm whales in the Gulf of Alaska through stable isotope analysis
Sperm whales interact with commercially important groundfish fisheries offshore in the Gulf of Alaska (GOA). This study aims to use stable isotope analysis to better understand the trophic variability of sperm whales and their potential prey, and to use dietary mixing models to estimate the importance of prey species to sperm whale diets. We analyzed tissue samples from sperm whales and seven potential prey (five groundfish and two squid species). Samples were analyzed for stable carbon and nitrogen isotope ratios, and diet composition was estimated using Bayesian isotopic mixing models. Mixing model results suggest that an isotopically combined sablefish/dogfish group, skates, and rockfish make up the largest proportion of sperm whale diets (35%, 28% and 12%) in the GOA. The top prey items of whales that interact more frequently with fishing vessels consisted of skates (49%) and the sablefish/dogfish group (24%). This is the first known study to provide an isotopic baseline of adult male sperm whales and these adult groundfish and offshore squid species, and to assign contributions of prey to whale diets in the GOA. This study provides information to commercial fishermen and fisheries managers to better understand trophic connections of important commercial species.
Dataset: Analysis of timing variability in human movements by aligning parameter curves in time
<p>Supplementary Data for <em><strong>Analysis of timing variability in human movements by aligning parameter curves in time</strong></em> article</p> <p>Dataset associated with the following publication:<br> Maurer, L. K., Maurer, H., & Müller, H. (2017). Analysis of timing variability in human movements by aligning parameter curves in time.</p> <p>-------------------------------------------------------------------------------</p> <p>The data files are structured in the following way:<br> (1) Basic subject information (age, sex) can be found in the file subject_data.txt (tabulator separated text file).</p> <p>(2) The folder parameter_curves contains the angle trajectories of all trials structured in blocks of 50 trials (sometimes less than 50 because of data cleaning procedures deleating corrupted data and trials in which participants released accidentally [with zero velocity]). Each participant performed five practice days with four blocks of 50 trials, i.e. 20 blocks. File names contain subject (1,...,14), day (1,...,5), and block (1,...,4) information. Within the tabulator separated text files each column contains the angle trajectory of one trial consisting of 1000 values (sampled with 1000 Hz). Index 600 is the moment when participants released the virtual ball.</p>
Dataset for "Study of 72 pulsars discovered in the PALFA survey: Timing analysis, glitch activity, emission variability, and a pulsar in an eccentric binary"
<p>This repository includes TEMPO-readable ephemerides associated with the final timing solutions presented in Table 3 (text-readable format; .par files) of the manuscript, "<em>Study of 72 pulsars discovered in the PALFA survey: Timing analysis, glitch activity, emission variability, and a pulsar in an eccentric binary</em>" accepted to the Astrophysical Journal (October 2021).</p>
Meta-analysis suggests variable, but pCO2-specific, effects of ocean acidification on crustacean biomaterials
Crustaceans comprise an ecologically and morphologically diverse taxonomic group. They are typically considered resilient to many environmental perturbations found in marine and coastal environments, due to effective physiological regulation of ions and hemolymph pH, and a robust exoskeleton. Ocean acidification can affect the ability of marine calcifying organisms to build and maintain mineralized tissue and poses a threat for all marine calcifying taxa. Currently, there is no consensus on how ocean acidification will alter the ecologically-relevant exoskeletal properties of crustaceans. Here, we present a systematic review and meta-analysis on the effects of ocean acidification on the crustacean exoskeleton, assessing both exoskeletal ion content (calcium and magnesium) and functional properties (biomechanical resistance and cuticle thickness). Our results suggest that the effect of ocean acidification on crustacean exoskeletal properties varies based upon seawater <i>p</i>CO<sub>2</sub> and species identity, with significant levels of heterogeneity for all analyses. Calcium and magnesium content were significantly lower in animals held at <i>p</i>CO<sub>2</sub> levels of 1500-1999 μatm as compared to those under ambient <i>p</i>CO<sub>2</sub>. At lower <i>p</i>CO<sub>2</sub> levels, however, statistically significant relationships between changes in calcium and magnesium content within the same experiment were observed: a negative relationship between calcium and magnesium content at <i>p</i>CO<sub>2</sub> of 500-999 μatm and a positive relationship at 1000-1499 μatm. Exoskeleton biomechanics, such as resistance to deformation (microhardness) and shell strength, also significantly decreased under <i>p</i>CO<sub>2</sub> regimes of 500-999 μatm and 1500-1999 μatm, indicating functional exoskeletal change coincident with decreases in calcification. Overall, these results suggest that the crustacean exoskeleton can be susceptible to ocean acidification at the biomechanical level, potentially predicated on changes in ion content, when exposed to high influxes of CO<sub>2</sub>. Future studies will need to accommodate the high variability of crustacean responses to ocean acidification, as well as ecologically-relevant ranges of <i>p</i>CO<sub>2</sub> conditions, when designing experiments with conservation-level endpoints. --
Subpolar ocean variability analysis using DePreSys4 hindcast output
<p>The dataset contains python scripts and pre-processed data, which was generated using DePreSys4 Met Office ensemble hindcast output. These scripts were used to create a subset of ensemble members based on seasonal NAO indices. The approach could be used to perform an ensemble-composite analysis to examine the impacts of NAO forcing on the ocean circulation in the subpolar North Atlantic ocean. </p>
FIGURE 7. Age-specific discriminant analysis using all morphometric parameters for all sites. 7A in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 7. Age-specific discriminant analysis using all morphometric parameters for all sites. 7A: Eocene. 7B: Oligocene. Triangles: Platanus neptuni. Squares: Eotrigonobalanus furcinervis. Circles: Daphnogene cinnamomifolia.
Рис. 1. Ctenoceratoda tancrei, бабочки из разΛичных ΛокаΛитетов: а, e — Киргизский хр., нац. парк «АΛа-Арча»; b, f — хр. ΔжумгаΛтоо, массив Сары-Кайкы; c, d, g, h — хр. МоΛΑо-Тоо, пер. Коро-Гоо. a–d — самцы, e–h — самки Fig. 1. Ctenoceratoda tancrei, the wing pattern variability: a, e — Kirghiz Mts., «Ala-Archa» national park; b, f — Dzhumgaltoo Mts., Sary-Kaiky gorge; c, d, g, h — Moldo-Too Mts., Koro-Goo Pass. a–d — males, e–h — females in Morphometric analysis of genitalia of Ctenoceratoda tancrei (Graeser, 1892) (Lepidoptera, Noctuidae)
Рис. 1. Ctenoceratoda tancrei, бабочки из разΛичных ΛокаΛитетов: а, e — Киргизский хр., нац. парк «АΛа-Арча»; b, f — хр. ΔжумгаΛтоо, массив Сары-Кайкы; c, d, g, h — хр. МоΛΑо-Тоо, пер. Коро-Гоо. a–d — самцы, e–h — самки Fig. 1. Ctenoceratoda tancrei, the wing pattern variability: a, e — Kirghiz Mts., «Ala-Archa» national park; b, f — Dzhumgaltoo Mts., Sary-Kaiky gorge; c, d, g, h — Moldo-Too Mts., Koro-Goo Pass. a–d — males, e–h — females
Рис. 1. Bryoxena centralasiae, изменчивость крыΛового рисунка: а, λ — Киргизский хр., нац. парк «АΛа-Арча»; в, г, з — хр. ΔжумгаΛтоо, массив Сары-Кайкы; д, к, м — хр. МоΛΑо-Тоо, пер. Коро-Гоо; б, е — Ферганский хр., пер. Урумбаш; ж, и — АΛайский хр., пер. ТаΛΑык Fig. 1. Bryoxena centralasiae, the wing pattern variability: а, λ — Kirghiz Mts., «Ala-Archa» national park; в, г, з — Dzhumgaltoo Mts., Sary-Kaiky gorge; д, к, м — Moldo-Too Mts., Koro-Goo Pass; б, е — Fergansky Mts., Urumbash Pass; ж, и — Alai Mts., Taldyk Pass in Morphometric Analysis Of The Genitalia Of (Staudinger, 1882) (Lepidoptera, Noctuidae)
Рис. 1. Bryoxena centralasiae, изменчивость крыΛового рисунка: а, λ — Киргизский хр., нац. парк «АΛа-Арча»; в, г, з — хр. ΔжумгаΛтоо, массив Сары-Кайкы; д, к, м — хр. МоΛΑо-Тоо, пер. Коро-Гоо; б, е — Ферганский хр., пер. Урумбаш; ж, и — АΛайский хр., пер. ТаΛΑык Fig. 1. Bryoxena centralasiae, the wing pattern variability: а, λ — Kirghiz Mts., «Ala-Archa» national park; в, г, з — Dzhumgaltoo Mts., Sary-Kaiky gorge; д, к, м — Moldo-Too Mts., Koro-Goo Pass; б, е — Fergansky Mts., Urumbash Pass; ж, и — Alai Mts., Taldyk Pass
Figure 6 in A geometric morphometric approach to the analysis of the shape variability of the haptoral attachment structures of Ligophorus species (Platyhelminthes: Monogenea)
Figure 6. Combination of the outlines of all dorsal anchors of each analyzed Ligophorus species (other haptoral structures outlines see http://marineparasites.org/morphometry/
Figure 5 in A geometric morphometric approach to the analysis of the shape variability of the haptoral attachment structures of Ligophorus species (Platyhelminthes: Monogenea)
Figure 5. Cluster (A, C) and PC analysis (B, D) of the combinations of four harmonics for each dorsal and ventral anchors, and ventral bar obtained for each Ligophorus specimens. Upper graphs (A, B) are based on the size-invariant EFDs; lower graphs (C, D) – on the size-considered EFDs.
Figure 4 in A geometric morphometric approach to the analysis of the shape variability of the haptoral attachment structures of Ligophorus species (Platyhelminthes: Monogenea)
Figure 4. PCA of the size-invariant (A, C, E) and size-considered (B, D, F) harmonics of the dorsal (А, B) and ventral (C, D) anchors, and the ventral bars (E, F) of Ligophorus species. All graphs are based on fifty harmonics. Keys: dots – dorsal anchors; triangles – ventral anchors; rhombus – ventral bars.
Figure 2. A in A geometric morphometric approach to the analysis of the shape variability of the haptoral attachment structures of Ligophorus species (Platyhelminthes: Monogenea)
Figure 2. A After the automatic normalization, the outlines of anchors still have different orientation of the blades, different positions of the digitization starting point and directions of digitization; this affects the signs of the first harmonic components, which are shown in pink rectangle, and the signs of identical components differ. B, C After manual correction of the orientation of anchors (B) and bars (C), the outlines and signs of first harmonic components are identical.
Figure 3 in A geometric morphometric approach to the analysis of the shape variability of the haptoral attachment structures of Ligophorus species (Platyhelminthes: Monogenea)
Figure 3. PCA of the size-invariant (A, B) and the size-considered (C, D) harmonics of all dorsal and ventral anchors of analyzed Ligophorus species. Left graphs (A, C) are based on fifty harmonics; right graphs (B, D) – on four ones. Keys: dots – dorsal anchors; triangles – ventral anchors.
Figure 1. A in A geometric morphometric approach to the analysis of the shape variability of the haptoral attachment structures of Ligophorus species (Platyhelminthes: Monogenea)
Figure 1. A Ligophorus szidati dorsal (top) and ventral (bottom) anchors were outlined by cubic Bezier polylines and stored in SVG files. B ElFourier computer program converted digitized outlines into 50 EFDs. Only first four harmonics are visible at the screenshot's bottom; the negative components of harmonics are colored in light gray. The restored outline perfectly satisfies the shape of anchor (red line around gray anchor). At the left side used anchors are shown; they differ by orientation of blades and direction of digitization; outlines oriented counterclockwise are filled.
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019) in Variability of the gene cyt b in the Korean field mouse Apodemus peninsulae Thomas, 1906 - a reservoir host of AMRV in the Khasansky District of Primorsky Krai
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019)
Data and analysis scripts for the OSDI 2018 paper "Taming Performance Variability"
<p>This repository contains our raw datasets and notebooks for analyzing performance results of benchmarks executed on CloudLab machines.</p> <p>File organization:</p> <ul> <li><code>notebooks/</code> - Various Jupyter notebooks containing our analysis. Contains analysis on our main dataset as well as some analysis on one-off collections. <ul> <li><code>bench-lib/common.py</code> - Common python utilities that are shared by the notebooks.</li> <li><code>disk-process.ipynb</code> - Main notebook for analysis of disk performance (both HDDs and SSDs).</li> <li><code>e-vs-cov.ipynb</code> - Analysis of E vs CoV for selected configurations.</li> <li><code>env-test.ipynb</code> - Version information for Python and installed packages.</li> <li><code>kernel-testing-Nd.ipynb</code> - Analysis of data using kernel two-sample testing in one and multiple dimensions..</li> <li><code>mem-process.ipynb</code> - Main notebook for analysis of memory performance.</li> <li><code>network-process.ipynb</code> - Main notebook for analysis of network performance.</li> <li><code>normality.ipynb</code> - Notebook for the high-level analysis of normality (or lack of it) in the collected data.</li> <li><code>quantile-regression.ipynb</code> - Notebook for Quantile Regression analysis for both disk and memory data.</li> <li><code>temporal-analysis.ipynb</code> - Notebook to search for/analyze any temporal aspects to our dataset.</li> <li><code>testbed-coverage.ipynb</code> - Analysis of the extent to which we were able to cover all of the test hardware.</li> <li><code>variability.ipynb</code> - Notebook for the high-level analysis of variability.</li> <li><code>wisc-disktests.ipynb</code> - <strong>ONE-OFF RUN</strong>: Analyzing variation in fio results on Wisconsin SSDs over repeated runs.</li> <li><code>wisc-memtests.ipynb</code> - <strong>ONE-OFF RUN</strong>: Analysis of benchmark order for various Wisconsin hardware/memory configurations.</li> </ul> </li> <li><code>data/</code>. Contains a dump of the dataset (as of 04/04/2018). This dataset is generated by running the code contained in the <a href="https://gitlab.flux.utah.edu/emulab/cloudlab-orchestration">https://gitlab.flux.utah.edu/emulab/cloudlab-orchestration</a> repository. Some additional one-off collections are contained here as well. <ul> <li><code>CoV-Summary/</code> - Data derived from our raw dataset for use by <code>notebooks/e-vs-cov.ipynb</code>.</li> <li><code>nodes/</code> - Simple list of all nodeids for each hardware type for use in <code>notebooks/testbed-coverage.ipynb</code>.</li> <li><code>raw-data/</code> - Raw .sql (and .csv files made from it) file containing a dump of our primary dataset through April 4th, 2018, used for the majority of the notebooks. Filtering is done in our notebooks to exclude data past April 1st, 2018 (as well as remove runs that appear to contain impossible execution conditions).</li> <li><code>wisc-disktests/</code> - <strong>ONE-OFF RUN</strong>: Results from successive runs of fio on Wisconsin SSDs. Used in <code>notebooks/wisc-disktests.ipynb</code>.</li> <li><code>wisc-memtests/</code> - <strong>ONE-OFF RUN</strong>: Results from memory benchmarks run with specific orderings on various Wisconsin machines. Used in <code>notebooks/wisc-memtests.ipynb</code>.</li> <li><code>wisc-pagemaps/</code> - <strong>ONE-OFF RUN</strong>: Virtual-to-Physical memory address mappings for tests run in <code>wisc-memtests/</code>. Used in <code>notebooks/wisc-memtests.ipynb</code>.</li> </ul> </li> </ul>
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.