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7,195 results for “SET”
ROS-Specific Huntingtin Interactions: Chromatin Retention Assay Set-up
<p>Optimization of an assay to measure huntingtin chromatin retention in response to oxidative stress, using the YFP-tagged huntingtin-specific intrabody nucHCB2.</p>
A PL/SQL Code Smell Data Set
<p>A PL/SQL Code Smells data set extracted from 20 open source projects on GitHub.</p>
Inferecladr Saccharomyces Cerevisiae Data Set
<p>This data is associated with the Inferecladr package. It consists of expression data clustered into separate files, associated metadata, genes clustered into separate lists of files, and a gold standard derived from YEASTRACT. It was initially used in Tchourine, K., Vogel, C., and Bonneau, R. (2018). Condition-Specific Modeling of Biophysical Parameters Advances Inference of Regulatory Networks. Cell Reports 23, 376–388.</p>
See & Grasp 2 Data Set
<p>This is the second See & Grasp data set introduced from Yildirim & Jacobs (2013). It is a data set containing both the visual and haptic features for "Fribbles". The set of "Fribbles" is larger than the original See & Grasp dataset, which is available <a href="http://www2.bcs.rochester.edu/sites/jacobslab/dataset.html">here</a>.</p> <p>Fribbles are complex, 3-D objects with multiple parts and spatial relations among the parts. Moreover, Fribbles have a categorical structure—that is, each Fribble is an exemplar from a category formed by perturbing a category prototype. There are 4 Fribble families, each with 4 species. Each Fribble has 4 slots, and one of 3 possible parts are attached to each slot, leading to (4 × (3<sup>4</sup>)) = 324 total Fribbles in each family. The unmodified 3-D object files for the whole set of Fribbles can be found on Mike Tarr's (Department of Psychology, Carnegie Mellon University) web pages (<a href="http://tarrlabwiki.cnbc.cmu.edu/index.php/Main_Page">TarrLab webpage</a>).</p> <p>The See & Grasp data set contains 891 items corresponding to 891 Fribbles (324 for the A and C families, and 243 for family B because the 3D model files for one of 4 species in family B seem to be corrupt). There are 3 entries associated with each item. One entry is the 3-D object model for a Fribble. The second entry is an image of a Fribble rendered from a canonical viewpoint so that the Fribble's parts and spatial relations among the parts are clearly visible. (Using the 3-D object model, users can easily generate new images of a Fribble from any desired viewpoint.) The third entry is a way of representing a Fribble's haptic features. It is a set of joint angles obtained from a grasp simulator known as "GraspIt!" (Miller & Allen, 2004). GraspIt! contains a simulator of a human hand. When forming the representation of a Fribble's haptic features, the input to GraspIt! was the 3-D object model for the Fribble. Its output was a set of 16 joint angles of the fingers of a simulated human hand obtained when the simulated hand "grasped" the Fribble. Grasps—or closings of the fingers around a Fribble—were performed using GraspIt!'s AutoGrasp function. Each Fribble was grasped 24 times, with the fribble rotated 8 times (by 45°) on each axis. To be sure that Fribbles fit inside GraspIt!'s hand, their sizes were reduced by 29%. Please see the <a href="http://www2.bcs.rochester.edu/sites/jacobslab/images/fb.png">figure</a> for sample images from the dataset and an illustration of the grasping procedure.</p> <p>The file contains three folders and a CSV file. The folders “Fa”, “Fb”, and “Fc” contain the files for each family of Fribble we examined—A, B, and C. These files each contain the following folders:</p> <ul> <li>“renders” contains the images of each Fribble in the species.</li> <li>“obj” contains the MTL and OBJ files for each Fribble in the species.</li> <li>“parts” contains the original Fribble parts from Mike Tarr's web pages which are composited into the individual Fribbles.</li> <li>“vrml” contains the VRML file for each Fribble in the species.</li> </ul> <p>The files are named based on the parts which were combined to make that Fribble. E.g., 4_a1b3c2d3 was made by combining 4_body.obj, 4_a1.obj, 4_b3.obj, 4_c2.obj, and 4_d3.obj.</p> <p>Finally, “haptics.csv” contains the haptic features for each of the Fribbles. It is a CSV file with 892 rows (including one header) and 255 columns. The first column is the Fribble's species and name, the next 254 columns are the joint angles from Graspit!: we performed a grasp for each of 24 different angles (rotating 45° around each axis), and each grasp has 16 DOF values associated with it, representing the position of the hand's joints.</p> <p>The rest of the columns contain the data for the contact points between the Fribble and the hand in Graspit. The first number for each grasp (there are 24 grasps for each Fribble) is the number of contact points for that grasp. For each contact point, the first six numbers relate to the contact's wrench, the next 3 numbers specify the contact's location in body coordinates, and the last number specifies the scalar constraint error for that contact.</p> <p>Sample code for rendering objects visually with VTK and haptically with GraspIt can be seen in the folder SampleCode. Please read GraspItUpdate.pdf file to see how to update GraspIt’s TCP server code for haptic rendering. Sample code folder also contains aomr.xml, the world file (i.e., setup of the human hand) used for haptic rendering in GraspIt. <br> <br> Please cite the following paper in relation to the See & Grasp 2 data set. <br> <br> Yildirim, I. & Jacobs, R. A. (2013). Transfer of object category knowledge across visual and haptic modalities: Experimental and computational studies. Cognition, 126, 135-148.</p> <p>Citation for GraspIt!<br> Miller, A., & Allen, P. K. (2004). Graspit!: A versatile simulator for robotic grasping. IEEE Robotics and Automation Magazine, 11, 110-122.</p>
Agfa IT8.7/2 Set - Marszalec (n.d.)
<p><strong>Source URL</strong>: <a href="http://www.uef.fi/web/spectral/agfa-it8.7/2-set">http://www.uef.fi/web/spectral/agfa-it8.7/2-set</a></p> <p>The Agfa ColorReference is designed according to the IT8.7 standard.The standard defines the layout and colorimetric values of an input test target that allows any color scanner to be calibrated with any photographic medium dye set. The IT8 target was designed by the ANSI IT8 subcommittee to be used for both calibration by visual comparison and as a numerical data target.</p> <p>Each ColorReference consist of 288 patches, and has been designed to represent the color space from full saturation to near neutrals at highlight, midtone, and shadows.Hues falling at regular intervals through the color space represent a full spectrum gamut. A linear perception neutral density scale, and CMY and RGB color dye scales provide basic reference points to check for gray balance, tone reproduction, and color correction. Finally, a vendor-optional area containing flesh colors based on the colorimetric measurements of various skin tones plus the colors of nature completes the ColorReference.</p> <p>The spectral reflectance of samples were measured with Minolta spectro- photometer with option SCE (Specular component excluded). The CM-2002 utilizes d/8 (diffuse illumination/8o viewing) geometry which conforms to ISO and DIN standards (and also to CIE d/0 recommendations).</p> <p>Measuring equipment: Minolta spectrofotometer CM-2002.<br> Wavelength interval: 400 nm - 700 nm.<br> Wavelength resolution: 10 nm.</p>
Crunchbase in RDF: A Large Data Set About Jobs, Websites, Organizations, News, People, Products, and Acquisitions
<p><strong>CrunchBase</strong> in an online platform providing information about startups and technology companies, including related entities such as the products they sell, key people they employ, and investments they made and received.</p> <p>We provide here an <strong>RDF data set of Crunchbase</strong> as of October 2015. The data set contains information about</p> <ul> <li>1,946,435 jobs</li> <li>1,348,449 websites</li> <li>567,937 organizations</li> <li>519,763 news</li> <li>430,093 people</li> <li>60,076 products, and</li> <li>33,127 acquisitions.</li> </ul> <p>The data set has been used, among other things, for data integration with financial data sources to evaluate the performance of particular companies and for monitoring news to find statements that are not in Crunchbase as an RDF knowledge graph yet.</p> <p>Note that the provided data set was created in October 2015 when all Crunchbase data was <strong>licensed under Creative Commons Attribution-NonCommercial License 4.0 (CC-BY-NC) and partly under Creative Commons Attribution License 4.0 (CC-BY)</strong>. Also the provied<strong> data set is licensed under these licenses.</strong> Concerning licensing of current Crunchbase data, we can refer to <a href="https://about.crunchbase.com/terms-of-service/">https://about.crunchbase.com/terms-of-service/</a>.</p> <p>For <strong>more information</strong> about the data set, see our paper <a href="http://dbis.informatik.uni-freiburg.de/content/team/faerber/papers/CrunchBaseWrapper_SWJ2017.pdf">A Linked Data Wrapper for CrunchBase.</a></p> <p>When you use the data set, please <strong>cite</strong> us as follows:</p> <blockquote> <p>Michael Färber, Carsten Menne, Andreas Harth. “A Linked Data Wrapper for CrunchBase”. In: Semantic Web Journal 9(4). IOS Press, 2018, pp. 505–5015. (<a href="https://dblp.org/rec/bibtex/journals/semweb/FarberMH18">BibTeX entry at DBLP</a>)</p> </blockquote>
Data set Absorption coefficients of Lead Iodine perovskites using 14 different organic cations
<p>Dataset provided as suplementary material of the article published in Solar Energy Material and Solar Cell: https://doi.org/10.1016/j.solmat.2019.110022.</p> <p>File description:The way to obtain the files described below is detailed in the methodology section in the article</p> <p>SALIDA.A-PbI3.Total (A=Ac, Az, Di, Et, Fo, Gu, Hy1, Hy2, Im, Is Me, Pr, Te, Tr)</p> <p>Date files corresponding to the total absorption coefficients for the 14 organic cations labelled as A.</p> <p>First | second column: energy (eV) | 2*absorption coefficient (cm-1)</p>
Data set supporting Journal article: Siebicke, L., and Emad, A., "True eddy accumulation trace gas flux measurements: proof of concept", Atmos. Meas. Tech., 12, 1–28, 2019.
<p>This data set contains carbon dioxide trace gas flux measurements obtained by the true eddy accumulation technique as described in the research article:</p> <p>Siebicke, L., and Emad, A., "True eddy accumulation trace gas flux measurements: proof of concept", Atmos. Meas. Tech., 12, 1–28, 2019.</p> <p>It contains meteorological measurements (file "meteo.csv"), turbulent energy fluxes (file "energy_fluxes.csv"), three-diomensional wind vector measurements ("file wind.csv"), and carbon dioxide trace gas fluxes obtained by true eddy accumulation (file "co2_flux_TEA.csv") and eddy covariance using data from two separate anomemoters (files "co2_flux_EC_TEA_sonic.csv" and "co2_flux_EC_EC_sonic.csv").</p>
Images of apples for the use of the Viola-Jones method. Data set no. 1 - multicolor.
<p>The database contains pictures of apples made at different angles, from different sides and containing different varieties. In this way, two bases of apple images were created (each database contains over 1,100 images). This set is data set no. 1 - multicolor: processed images in multicolor. The photos were prepared for the best possible detection process in the Viola-Jones method. These photo bases with apples can be used to teach machines to recognize specific varieties and count apples.</p> <p>Please note that these are not raw data, but images processed for use in the algorithm in the Viola-Jones method.</p> <p> </p>
Small example Eiger 2X 16M data set from Diamond Light Source I04
<p>Useful small (488 frame) Eiger data set recorded during routine testing, useful for software testing as it is small. Data recorded from a thaumatin crystal (unfortunate naming on my part) </p> <p> </p> <p>Revision includes screening images, and addition of name dataset in /entry/instrument</p> <p> </p> <pre>> h5dump -d /entry/instrument/name Therm_6_2.nxs HDF5 "Therm_6_2.nxs" { DATASET "/entry/instrument/name" { DATATYPE H5T_STRING { STRSIZE H5T_VARIABLE; STRPAD H5T_STR_NULLTERM; CSET H5T_CSET_ASCII; CTYPE H5T_C_S1; } DATASPACE SCALAR DATA { (0): "Macromolecular Crystallography I04" } } } </pre>
Testing Data set - DiSCount - Masteling et al.
<p>Data set used to test the accuracy of the software DiSCount: computer vision for automated quantification of <em>Striga</em> seed germination.</p>
data set related to article Magnetic resonance fingerprinting with dictionary-based fat and water separation (DBFW MRF): A multi-component approach
<p>This record contains raw data related to article Magnetic resonance fingerprinting with dictionary-based fat and water separation (DBFW MRF): A multi-component approach</p>
data set related to article A Nervous System-Specific Model of Creatine Transporter Deficiency Recapitulates the Cognitive Endophenotype of the Disease: a Longitudinal Study
<p>This record contains raw data related to article A Nervous System-Specific Model of Creatine Transporter Deficiency Recapitulates the Cognitive Endophenotype of the Disease: a Longitudinal Study</p>
data set related to article Broad phenotypic spectrum and genotype-phenotype correlations in GMPPB-related dystroglycanopathies: an Italian cross-sectional study
<p>This record contains raw data related to article Broad phenotypic spectrum and genotype-phenotype correlations in GMPPB-related dystroglycanopathies: an Italian cross-sectional study</p>
data set related to article Simultaneous and sequential subitizing are separate systems, and neither predicts math abilities
<p>This record contains raw data related to article Simultaneous and sequential subitizing are separate systems, and neither predicts math abilities</p>
data set related to article Typical numerosity adaptation despite selectively impaired number acuity in dyscalculia
<p>This record contains raw data related to article Typical numerosity adaptation despite selectively impaired number acuity in dyscalculia</p>
data set related to article Independent adaptation mechanisms for numerosity and size perception provide evidence against a common sense of magnitude
<p>This record contains raw data related to article Independent adaptation mechanisms for numerosity and size perception provide evidence against a common sense of magnitude</p>
The ecology of suburban juvenile European hedgehogs (Erinaceus europaeus). Supplementary data set for home range calculations.
<p>The complete data set on which we based the home range calculations on in our paper: The ecology of suburban juvenile European hedgehogs (Erinaceus europaeus) in Denmark, Ecology and Evolution.</p>
Photographs of a confocal set-up build by Fred Brakenhoff
<p>Photographs of a confocal microscope that was buildt in the 1980s by Fred Brakenhoff and colleagues at the Department of Electron Microscopy and Molecular Cytology, University of Amsterdam, The Netherlands. The photos were taken in October 2019, of the set-up that is on display at the Section of Molecular Cytology (UvA): <a href="http://www.molcyto.nl">http://www.molcyto.nl</a></p> <p>One of the photographs was used in a contribution for www.biotechniques.com with the title "A hammer doesn’t need a nail"</p>
Small example Eiger 2X 16M data set from Diamond Light Source I04 revised for HDRMX Gold Standard Discussion
<p>Revised useful small (488 frame) Eiger data set recorded during routine testing, useful for software testing as it is small. Data recorded is from a thaumatin crystal by Graeme Winter, The original dataset is <a href="https://zenodo.org/record/3385862">https://zenodo.org/record/3385862</a> which contains two Eiger MX datasets, Therm_6_1 and Them_6_2, each with a data file and two versions each of the metadata -- a "..._master.h5" file and a "....nxs" file. The former are the usual Eiger metadata files using exposed external links to connect the metadata to the date, and the latter are HDF5-1.10 VDS files. This revision has the same data as the original Therm_6_2 data, but now includes with the "master.h5" file a "..._master_rev.h5" file and with the ".nxs" file a "..._rev.nxs" VDS file.</p> <p>The purpose to the changes in the "..._rev" files is to provide a supporting example for the HDRMX discusssion of a new proposed Eiger "gold standard" to improve the ability to process Eiger MX data collected at one facility at other facilties, by ensuring that sufficient metadata is stored with all datasets.</p> <p>The changes were made by the following script</p> <pre>cp Therm_6_2.nxs Therm_6_2_rev.nxs cp Therm_6_2_master.h5 Therm_6_2_master_rev.h5 export LD_LIBRARY_PATH=$HOME/lib export HDF5_PLUGIN_PATH=$HOME/lib export PATH=$HOME/bin:$PATH h5copy -i Therm_6_2_rev.nxs -o Therm_6_2_master_rev.h5 -s /entry/instrument/name -d /entry/instrument/name -f ref h5copy -i Therm_6_2_rev.nxs -o Therm_6_2_master_rev.h5 -s /entry/instrument/source -d /entry/source -f ref h5copy -i Therm_6_2_rev.nxs -o Therm_6_2_rev.nxs -s /entry/instrument/source -d /entry/source -f ref export end_time=`h5dump -d "/entry/end_time" Therm_6_2_master.h5 | grep ":" | sed 's/^.........//'|sed 's/.\$//'` echo "end_time: $end_time" python << 'EOL' import h5py import numpy as np import os end_time=os.environ['end_time'] fvds = h5py.File('Therm_6_2_rev.nxs','r+') fmaster = h5py.File('Therm_6_2_master_rev.h5','r+') fvds_keys=fvds.keys() fmaster_keys=fmaster.keys() fvds_entry=fvds['entry'] fmaster_entry=fmaster['entry'] fvds_entry_keys=fvds_entry.keys() fmaster_entry_keys=fmaster_entry.keys() fvds_entry_instrument=fvds['entry']['instrument'] fmaster_entry_instrument=fmaster['entry']['instrument'] fvds_entry_instrument_keys=fvds_entry_instrument.keys() fmaster_entry_instrument_keys=fmaster_entry_instrument.keys() fvds_entry_instrument_name=(fvds['entry']['instrument']['name']) fmaster_entry_instrument_name=(fmaster['entry']['instrument']['name']) fvds_entry_instrument_short_name=fvds_entry_instrument.attrs['short_name'] fmaster_entry_instrument_short_name=fmaster_entry_instrument.attrs['short_name'] zero_offset=fmaster_entry_instrument['detector']['module']['fast_pixel_direction'].attrs['offset'] fmaster_det_z=fmaster_entry_instrument['transformations']['det_z'] fvds_det_z=fvds_entry_instrument['transformations']['det_z'] print('fvds_keys: ',fvds_keys) print('fmaster_keys: ',fmaster_keys) print('fvds_entry_keys: ',fvds_entry_keys) print('fmaster_entry_keys: ',fmaster_entry_keys) print('fvds_entry_instrument_keys: ',fvds_entry_instrument_keys) print('fmaster_entry_instrument_keys: ',fmaster_entry_instrument_keys) print('fvds_entry_instrument_name: ',fvds_entry_instrument_name) print('fmaster_entry_instrument_name: ',fmaster_entry_instrument_name) print('fvds_entry_instrument_short_name: ',fvds_entry_instrument_short_name) print('fmaster_entry_instrument_short_name: ',fmaster_entry_instrument_short_name) print('fmaster_entry_instrument_detector_module_fast_pixel_direction_offset: ',zero_offset) print('fmaster_entry_instrument_detector_detector_z_det_z: ',fmaster_det_z) print('fmaster_entry_end_time: ',end_time) fmaster.attrs.modify('file_time',np.string_(end_time)) fmaster.attrs.modify('file_name',np.string_('Therm_6_2_master_rev.h5')) fmaster.attrs.modify('HDF5_Version',np.string_('hdf5-1.8.18')) fvds.attrs.modify('file_time',np.string_(end_time)) fvds.attrs.modify('file_name',np.string_('Therm_6_2_master_rev.h5')) fvds.attrs.modify('HDF5_Version',np.string_('hdf5-1.10.5')) fvds_entry_instrument_name.attrs.modify('short_name',np.string_(fvds_entry_instrument.attrs['short_name'])) fmaster_entry_instrument_name.attrs.modify('short_name',np.string_(fmaster_entry_instrument.attrs['short_name'])) fmaster_entry_instrument['attenuator']['attenuator_transmission'].attrs.modify('units',np.string_("")) fmaster_entry_instrument['detector']['count_time'].attrs.modify('units',np.string_("s")) fvds_entry_instrument_name.attrs.modify('short_name',np.string_(fvds_entry_instrument.attrs['short_name'])) fvds_entry_instrument['attenuator']['attenuator_transmission'].attrs.modify('units',np.string_("")) fvds_entry_instrument['detector']['count_time'].attrs.modify('units',np.string_("s")) fmaster_det_z.attrs.modify('offset',zero_offset) fvds_det_z.attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['phi'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['chi'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_x'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_y'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_z'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['omega'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['phi'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['chi'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_x'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_y'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_z'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['omega'].attrs.modify('offset',zero_offset) print(fmaster['entry']['instrument']['name'].attrs['short_name']) print(fmaster['entry']['instrument']['name'].attrs['short_name'].shape) print(fmaster['entry']['instrument']['name'].attrs['short_name'].dtype) del fvds_entry_instrument.attrs['short_name'] del fmaster_entry_instrument.attrs['short_name'] del fmaster_entry_instrument['source'] fvds.close() fmaster.close() quit() EOL $HOME/bin/nxvalidate -a NXmx -l /home/yaya/hdrmx_rev_29Sep19/hdrmx/definitions Therm_6_2_master_rev.h5 $HOME/bin/nxvalidate -a NXmx -l /home/yaya/hdrmx_rev_29Sep19/hdrmx/definitions Therm_6_2_rev.nxs </pre> <p>The revised cnxvalidate and definitions are available on github</p> <p><a href="https://github.com/HDRMX/cnxvalidate.git">https://github.com/HDRMX/cnxvalidate.git</a></p> <p><a href="https://github.com/HDRMX/definitions.git">https://github.com/HDRMX/definitions.git</a></p> <p> </p> <p> </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.