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9,113 results for “Sampling”
Container Registry Benchmark experiments measurements and trace workload samples
<p>Measurements for experiments using Container Registry Benchmark, CReB. 4 experiments: Long running, small experiment stress mode, small experiment delay mode, and large workload experiment.</p> <p> </p> <p>Structure:</p> <ol> <li><strong>full-measurements-long-running-pull.csv : </strong>measurements for long running pull experiment</li> <li><strong>full-measurements-long-running-push.csv: </strong>measurements for long running push experiment</li> <li><strong>result-bug-analysis.zip: </strong>results from bug analysis of trace replayer</li> <li><strong>results-1hr-experiment.zip: </strong>measurements for the large experiment (4 registries)</li> <li><strong>results-small-delay.zip: </strong>measurements for the delay mode, small experiment with real workload</li> <li><strong>results-small-stress.zip: </strong>measurements for the stress mode, small experiment with real workload</li> <li><strong>traces.zip: </strong>traces used for pen-and-paper experiment, 1 hour sample, and the trace used for small experiment (selected are first 405 requests)</li> </ol>
AWARE characterization factor samples
<p>Files contain 5000 samples of AWARE characterization factors, as well as sampled independent data used in their calculations and selected intermediate results.</p> <p>AWARE is a consensus-based method development to assess water use in LCA. It was developed by the <a href="http://www.wulca-waterlca.org/index.html">WULCA UNEP/SETAC working group</a>. Its characterization factors represent the relative Available WAter REmaining per area in a watershed, after the demand of humans and aquatic ecosystems has been met. It assesses the potential of water deprivation, to either humans or ecosystems, building on the assumption that the less water remaining available per area, the more likely another user will be deprived.</p> <p>The code used to generate the samples can be found here: <a href="https://github.com/PascalLesage/aware_cf_calculator/">https://github.com/PascalLesage/aware_cf_calculator/</a></p> <p>Samples were updated from v1.0 in 2020 to include model uncertainty associated with the choice of WaterGap as the global hydrological model (GHM).</p> <p>The following datasets are supplied:</p> <p><strong>1) AWARE_characterization_factor_samples.zip</strong></p> <p>Actual characterization factors resulting from the Monte Carlo Simulation. Contains 4 zip files:</p> <p> * monthly_cf.zip: contains 116,484 arrays of 5000 monthly characterization factor samples for each of 9707 watershed and for each month, in csv format. Names are cf_<BAS34S_ID>_<MONTH>.csv, where <BAS34S_ID> is the watershed id and <MONTH> is the first three letters of the month ('jan', 'feb', etc.).</p> <p> * average_agri_cf.zip: contains 9707 arrays of 5000 annual average, agricultural use, characterization factor samples for each watershed, in csv format. Names are cf_average_agri_<BAS34S_ID>.csv.</p> <p> * average_non_agri_cf.zip: contains 9707 arrays of 5000 annual average, non-agricultural use, characterization factor samples for each watershed, in csv format. Names are cf_average_non_agri_<BAS34S_ID>.csv.</p> <p> * average_unknown_cf.zip: contains 9707 arrays of 5000 annual average, unspecified use, characterization factor samples for each watershed, in csv format. Names are cf_average_unknown_<BAS34S_ID>.csv..</p> <p><strong>2) AWARE_base_data.xlsx</strong> </p> <p>Excel file with the deterministic data, per watershed and per month, for each of the independent variables used in the calculation of AWARE characterization factors. Specifically, it includes:</p> <p> Monthly irrigation<br> Description: irrigation water, per month, per basin<br> Unit: m3/month<br> Location in Excel doc: Irrigation<br> File name once imported: irrigation.pickle<br> table shape: (11050, 12)</p> <p> Non-irrigation hwc: electricity, domestic, livestock, manufacturing<br> Description: non-irrigation uses of water<br> Unit: m3/year<br> Location in Excel doc: hwc_non_irrigation<br> File name once imported: electricity.pickle, domestic.pickle,<br> livestock.pickle, manufacturing.pickle<br> table shape: 3 x (11050,)</p> <p> avail_delta<br> Description: Difference between "pristine" natural availability<br> reported in PastorXNatAvail and natural availability calculated<br> from "Actual availability as received from WaterGap - after<br> human consumption" (Avail!W:AH) plus HWC.<br> This should be added to calculated water availability to<br> get the water availability used for the calculation of EWR<br> Unit: m3/month<br> Location in Excel doc: avail_delta<br> File name once imported: avail_delta.pickle<br> table shape: (11050, 12)</p> <p> avail_net<br> Description: Actual availability as received from WaterGap - after human consumption<br> Unit: m3/month<br> Location in Excel doc: avail_net<br> File name once imported: avail_net.pickle<br> table shape: (11050, 12)</p> <p> pastor<br> Description: fraction of PRISTINE water availability that should be reserved for environment<br> Unit: unitless<br> Location in Excel doc: pastor<br> File name once imported: pastor.pickle<br> table shape: (11050, 12)</p> <p> area<br> Description: area<br> Unit: m2<br> Location in Excel doc: area<br> File name once imported: area.pickle<br> table shape: (11050,)<br> It also includes:</p> <p>* information (k values) on the distributions used for each variable (uncertainty tab)</p> <p>* information (k values) on the model uncertainty (model uncertainty tab)</p> <p>* two filters used to exclude watersheds that are either in Greenland (polar filter) or without data from the Pastor et al. (2014) method (122 cells), representing small coastal cells with no direct overlap (pastor filter). (filters tab)</p> <p><strong>3) independent_variable_samples.zip</strong></p> <p>Samples for each of the independent variables used in the calculation of characterization factors. Only random variables are contained. For all watershed or watershed-months without samples, the Monte Carlo simulation used the deterministic values found in the AWARE_base_data.xlsx file. </p> <p>The files are in csv format. The first column contains the watershed id (BAS34S_ID) if the data is annual or the (BAS34S_ID, month) for data with a monthly resolution. the other 5000 columns contain the sampled data.</p> <p>The names of the files are <variable_name.csv>.</p> <p><strong>4) intermediate_variables.zip</strong></p> <p>Contains results of intermediate calculations, used in the calculation of characterization factors. The zip file contains 3 zip files:</p> <p> * AMD_world_over_AMD_i.zip: contains 116,484 arrays (for each watershed-month) of 5000 calculated values of the ratio between the AMD (Availability Minus Demand) for the watershed-month and AMD_glo, the world weighted AMD average. Format is csv.<br> * AMD_world.zip: contains one array of 5000 calculated values of the world average AMD. Format is csv.</p> <p> * HWC.zip: contains 116,484 arrays (for each watershed-month) of 5000 calculated values of the total Human Water Consumption. Format is csv.</p> <p><strong>5) watershedBAS34S_ID.zip</strong></p> <p>Contains the GIS files to link the watershed ids (BAS34S_ID) to actual spatial data. </p>
Sample BBBC OME-TIFF file
<p>This file was derived from the image set BBBC017v1 available from the Broad Bioimage Benchmark Collection [Ljosa et al., Nature Methods, 2012]. The NIRHTa+0001 plate was converted into a single OME-TIFF using Bio-Formats 5.3.2.</p>
Data: Flower visiting insects of kiwifruit within New Zealand commercial orchard blocks sampled over two years in the Bay of Plenty, New Zealand
<p>These data are total counts of individual bee and non–bee insects observed visiting the flowers of kiwifruit (<em>Actinidia chinensis</em> var.deliciosa) (‘Hayward’) vines in three commercial orchards located in the Bay of Plenty Region of New Zealand (37° 46' 56" S; 176° 19' 10" E). Each block was located on a different farm and each separated by a distance of at least two kilometres and surveyed twice in two consecutive years. A total of 1181 insects were observed, 741 in the 2014 season and 460 in the 2015 season. Insects from four orders were recorded. The most abundant species were honey bees <em>Apis mellifera</em> (n= 1068; 90.4%), flower longhorn beetles <em>Zorion guttigerum</em> (n= 52; 4.4%), the native bee <em>Lasioglossum</em> <em>sordidum</em>/c<em>ognatum</em> (n= 12; 1.0%) and the hover fly <em>Melanostoma fasciatum</em> (n= 11; 0.9%) Others insects represented 3.2% of individuals observed (n=38). We present a table of counts of the insects observed.</p>
IODP Expedition 368X Sample report
<p>Report includes detailed information about samples taken for testing: location/depth, type/form factor, test, request number, and database identifier.</p>
Model Counting and Uniform Sampling Instances
<p>These instances mainly consist of the formulas that have been used in the evaluation of recent model counting techniques. A significant set of benchmarks involving sampling set, i.e., they are meant for projected model counting.<br> <br> The specification for reading such files can be found at <a href="https://github.com/meelgroup/approxmc">https://github.com/meelgroup/approxmc</a><br> <br> Here is list of some of the papers that have reported results on these instances:</p> <p>1. BIRD: Engineering an Efficient CNF-XOR SAT Solver and its Applications to Approximate Model Counting<br> Mate Soos and Kuldeep S. Meel<br> Proceedings of AAAI Conference on Artificial Intelligence (AAAI), 2019.</p> <p>2. Accelerating Approximate Techniques for Counting and Sampling Models Through Refined CNF-XOR Solving<br> Mate Soos, Stephan Gocht, and Kuldeep S. Meel<br> Proceedings of International Conference on Computer-Aided Verification (CAV), 2020.<br> </p>
IODP Expedition 366 Sample report
<p>Report includes detailed information about samples taken for testing: location/depth, type/form factor, test, request number, and database identifier.</p>
Replication Data for: Continuously moving table MRI with golden angle radial sampling
<p>Continuously moving table (CMT) MRI is a high throughput technique that has multiple applications in whole-body imaging. In this work, CMT MRI based on golden angle (GA, 111.246° azimuthal step) radial sampling is developed at 3 Tesla (T), with the goal of increased flexibility in image reconstruction using arbitrary profile groupings.</p>
Posterior PDF samples for flicker model parameters
<p>h5py files containing posterior PDF samples of the flicker-logg-rho_star relations described in the associated paper (submitted).</p>
Updated posterior PDF samples for flicker model parameters
<p>Updated posterior PDF samples for for the model parameters of the flicker-logg-rho_star relations. </p>
USPTO patent data: 250k random sample and NPEs' patents
<p>This package includes:</p> <ol> <li>Two Stata .dta files consisting of information on patents assigned by the United States Patent and Trademark Office between 1976 and 2014: a random sample of 250 000 US patents, and data on patent owned by Intellectual Ventures, RPX, and several other companies. The variables for example include: grant date, application date, forward and backward citations, renewals, claims and others.</li> <li>Source codes and methods used in generating and analyzing the two data files.</li> </ol> <p>A bachelor thesis with further information will be linked here.</p>
molyso Mother Machine Sample Datasets
<p>Example datasets for molyso, the MOther machine anaLYsis SOftware [ https://github.com/modsim/molyso ].</p> <p>The datasets show <em>Corynebacterium glutamicum</em> cells growing in a Mother Machine device. For additional information, please see the publication.</p>
Sample datasets used in [Gil et al 2017] for AAAI 2017
<p>This file includes the pointer to the 42 patient ids and zip file names of the 84 genomic and proteomic datasets used for the paper "Gil, Y, Garijo, D, Ratnakar, V, Mayani, R, Adusumilli, A, Srivastava, R, Boyce, H, Mallick,P. Towards Continuous Scientific Data Analysis and Hypothesis Evolution", accepted in AAAI 2017.</p> <p>The datasets itself are not published due to their size and access conditions. They can be retrieved with the provided ids from TCGA (https://gdc-portal.nci.nih.gov/legacy-archive/search/f) and CPTAC (https://cptac-data-portal.georgetown.edu/cptac/s/S022) archives.</p> <p>These patient ids are a subset of the nearly 90 samples used in "Zhang, B., Wang, J., Wang, X., Zhu, J., Liu, Q., et al. Proteogenomic characterization of human colon and rectal cancer. <em>Nature</em> 513,382–387", in order to test the system described in the AAAI 2017 paper. More samples were not included in the analysis due to time constraints.</p>
Figs 83-90 in Fragilaria rinoi sp. nov. (Fragilariales, Fragilariophyceae) from periphytic river samples in Central Portugal
Figs 83-90. SEM micrographs of the type material of Fragilaria vaucheriae (Kütz.) J.B.Petersen. 83. Complete valve in external view. 84. Complete frustule in external view. 85–86. Frustule in girdle view. 87, 90. Detail of the apex in girdle view, with the girdle bands and fimbriae clearly visible (indicated by the arrows). 88–89. Detail of the apex in external view (arrow indicates the rimoportula). Scale bars = 2 µm.
Fig. 1 in Fragilaria rinoi sp. nov. (Fragilariales, Fragilariophyceae) from periphytic river samples in Central Portugal
Fig. 1. Geographic locations of the sampling sites where Fragilaria rinoi Almeida & C.Delgado sp. nov. was found. The type locality is represented by a black circle in the Vouga river basin followed south by river Mondego and Lis basins.
Figs 78-82 in Fragilaria rinoi sp. nov. (Fragilariales, Fragilariophyceae) from periphytic river samples in Central Portugal
Figs 78-82. Scanning electron microscope images of Fragilaria rinoi Almeida & C.Delgado sp. nov. from Mogofores (river Cértima, Vouga river basin), 21 Mar. 2012 (Holotype BM 101 794). 78. Complete valve in external view. 79–80. Complete valves in internal view. 81. Detail of an apex in external view, with the apical pore field and rimoportula. 82. Detail of an apex in internal view, with the rimoportula clearly visible. Scale bars: 78–80, 82 = 2 µm; 81 = 1 µm.
Fig. 91 in Fragilaria rinoi sp. nov. (Fragilariales, Fragilariophyceae) from periphytic river samples in Central Portugal
Fig. 91. Comparison of the type population of Fragilaria rinoi Almeida & C.Delgado sp. nov. with the type material of related taxa. Data fitted to 95% confidence ellipses. NMDS plot of Booksteintransformed valve outline pseudolandmark coordinates. Black: F. rinoi sp. nov. Red: F. candidagilae Almeida, C.Delgado, Novais & S.Blanco. Blue: F. capucina Desm. Pink: F. intermedia Grunow in Van Heurck. Olive green: F. microvaucheriae C.E.Wetzel & Ector. Purple: F. neointermedia Tuji & D.M.Williams. Green: F. perminuta (Grunow) Lange-Bert. Dark blue: F. recapitellata Lange-Bert. & Metzeltin. Sky blue: F. vaucheriae (Kütz.) J.B.Petersen.
Figs 3–77 in Fragilaria rinoi sp. nov. (Fragilariales, Fragilariophyceae) from periphytic river samples in Central Portugal
Figs 3–77. Light microscope images of Fragilaria rinoi Almeida & C.Delgado sp. nov. 3–62. Mogofores (Cértima river, Vouga river basin, 21 Mar. 2012) type material (BM 101 794; Fig. 9 is from the holotype). 63–72. Estarreja (Antuã river, Vouga river basin, 22 Mar. 2012). 73–77. Foz do Ceira (river Ceira, Mondego river basin, 20 Mar. 2012). Scale bar = 10 µm.
Fig. 92 in Fragilaria rinoi sp. nov. (Fragilariales, Fragilariophyceae) from periphytic river samples in Central Portugal
Fig. 92. Boxplot of measurements of different Fragilaria species. Variation in valve length (a), width (b) and striae in 10 μm (c) measured in the type populations. The boxes represent the 25–75% quartiles, the median is shown with a horizontal line inside the box, and minimum and maximum values are shown with short horizontal lines (whiskers). Outliers are plotted as individual points.
Fig. 2 in Fragilaria rinoi sp. nov. (Fragilariales, Fragilariophyceae) from periphytic river samples in Central Portugal
Fig. 2. Type material of Fragilaria vaucheriae deposited in the Natural History Museum of London (BM 78023, BM 78024).
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.