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9,882 results for “Scale”
Dataset relate to article "TOWARDS A NEW ASSESSMENT TOOL FOR CAREGIVERS OF PATIENTS WITH DISORDERS OF CONSCIOUSNESS: THE SOCIAL AND FAMILY EVALUATION SCALE (SAFE)"
<p>FOR EVERY PARTICIPANT, THE RESPONSE AT EACH QUESTION OF THE SURVEY IS PROVIDED.</p>
National-Scale Spatial Flood Modeling with an Optimized Deep Learning Approach (case study: Sweden)
<p>National-Scale Spatial Flood Modeling with an Optimized Deep Learning Approach (case study: Sweden)</p>
Validation of the Spanish version of the Nurses' Global Assessment of Suicide Risk Scale (NGASR)
Open the record for dataset details and reuse information.
Large-scale bidirectional arrayed CRISPR screens identify OXR1 and EMC4 as modifiers of α-synuclein aggregation
GEO Series GSE295558. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Large-scale Chemical Screen Identifies a Geroprotective Role of Gallic Acid in Premature Aging [ATAC-seq]
GEO Series GSE181444. Homo sapiens. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
DGP1 family members mediate large-scale metabolic remodeling by integrating two environmental signals in rice
GEO Series GSE282620. Oryza sativa. 6 samples. Type: Expression profiling by high throughput sequencing.
Clock-controlled diurnal gene expression changes on a genome-wide scale
GEO Series GSE93340. Arabidopsis thaliana. 6 samples. Type: Expression profiling by array.
Persistent large-scale changes in alternative splicing in prefrontal cortical neuron types following psychedelic exposure
GEO Series GSE286904. Mus musculus. 118 samples. Type: Expression profiling by high throughput sequencing.
Transcriptional and chromatin-based partitioning mechanisms uncouple protein scaling from cell size
GEO Series GSE167842. Schizosaccharomyces pombe; Saccharomyces cerevisiae. 72 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Large scale transcriptome analysis was used to identify the genes most related to the transcription of cellulase genes and/or xylanase genes expression
GEO Series GSE155790. Penicillium oxalicum. 114 samples. Type: Expression profiling by high throughput sequencing.
Black Soldier Fly larvae (Hermetia illucens) meal is a viable protein source for Atlantic salmon (Salmo salar) during a large-scale controlled field trial under commercial-like conditions
GEO Series GSE241320. Salmo salar. 37 samples. Type: Expression profiling by high throughput sequencing.
Large-scale gene expression profiling of hepatocellular adenomas
GEO Series GSE88839. Homo sapiens. 38 samples. Type: Expression profiling by array.
Making Predictions using Large Scale Gaussian Processes
One of the key problems that arises in many areas is to estimate a potentially nonlinear function [tex] G(x, \theta)[/tex] given input and output samples [tex] ( X,y ) [/tex] so that [tex]y approx G(x, \theta)[/tex]. There are many approaches to addressing this regression problem. Neural networks, regression trees, and many other methods have been developed to estimate [tex]$G$[/tex] given the input output pair [tex] ( X,y ) [/tex]. One method that I have worked with is called Gaussian process regression. There many good texts and papers on the subject. For more technical information on the method and its applications see: http://www.gaussianprocess.org/ A key problem that arises in developing these models on very large data sets is that it ends up requiring an [tex]O(N^3)[/tex] computation where N is the number of data points and the training sample. Obviously this becomes very problematic when N is large. I discussed this problem with Leslie Foster, a mathematics professor at San Jose State University. He, along with some of his students, developed a method to address this problem based on Cholesky decomposition and pivoting. He also shows that this leads to a numerically stable result. If ou're interested in some light reading, I’d suggest you take a look at his [recent paper]( ) (which was accepted in the Journal of Machine Learning Research) posted on dashlink. We've also posted code for you to try it out. Let us know how it goes. If you are interested in applications of this method in the area of prognostics, check out our [new paper](/dashlink/resources/51/) on the subject which was published in IEEE Transactions on Systems, Man, and Cybernetics.
GSSR V RTLS 5 12.6-12.9CM RADAR SCALED ECHO POWER/ALT V1.0
The Goldstone Solar System Radar observations of Venus from 1972 until 1988 are included in this data set. The system operated at 12.6 cm up until 1977 operated at 12.9 cm. The data consist of radar scaled backscatter cross sections in image format and, in most cases, associated altimetry observations. The data were acquired over 52 separate observing runs. In total the observations cover the region between approximately 260 degrees (leftmost) east longitude to 30 degrees (rightmost) east longitude, within approximately 20 degrees of the equator. Backscatter data were acquired with opposite sense circular polarization with incidence angles typically less than 10 degrees. Associated altimetric data were acquired using a two or three station interferometric system. Data are presented in Mercator projection with degrees/pixel in longitude dependent on the individual data file.
Centers of endemism of freshwater protists deviate from pattern of taxon richness on a continental scale
<p>Supplemental Material of the Manuskript: Centers of endemism of freshwater protists deviate from pattern of taxon richness on a continental scale.</p> <p>Figures include distribution maps of V9-groups (with corresponding ITS-variants) which occurred in at least 5 lakes of the sampling area.</p>
Thesis: Locate and understand the mortality risk of forest tree under climate change. A multi-scale approach combining statistical and mechanistic modeling
<p>Data and tables in chapters 2 and 3 of the thesis</p>
ECC: Tetramethyl-ammonium - Acetate. CGENFF, scaled charges (0.75)
<p>CGENFF forcefield with scaled charges for TMA and ACE in TIP3P water, as downloaded from <a href="http://virtualchemistry.org">virtualchemistry.org</a></p> <p>* 100 ns NPT for equilibrium box size</p> <p>* 100 ns NVT equilibration</p> <p>* 20 ns NVT production</p>
ECC: Tetramethyl-ammonium - Acetate. GAFF-RESP, scaled charges (0.75)
<p> GAFF forcefield with recalculated, scaled RESP charges for TMA and ACE in SPCE water, as downloaded from virtualchemistry.org</p> <p> * 100 ns NPT for equilibrium box size</p> <p> * 100 ns NVT equilibration</p> <p> * 20 ns NVT production</p>
ECC: Tetramethyl-ammonium - Acetate. GAFF-BCC, scaled charges (0.75)
<p> GAFF forcefield with scaled BCC charges for TMA and ACE in SPCE water, as downloaded</p> <p> from virtualchemistry.org</p> <p> * 100 ns NPT for equilibrium box size</p> <p> * 100 ns NVT equilibration</p> <p> * 20 ns NVT production</p>
MalDevGenome: A Longitudinal and Large-Scale AndroidMalware Developer Dataset
<p>Dataset for the paper "MalDevGenome: A Longitudinal and Large-Scale AndroidMalware Developer Dataset".</p> <p>We have collected 1,411,830 apps from 10,708 different malware developer. These apps belongs to 1,035 different malware families. We provided the basic information, anti-virus detection result and sensitive behaviors of the apps, including app name, package name, apk file hashes, developer certificate hashes, permission, service, content provider, activity, broadcast receiver, library, hardcode string and the malware family name. And we will upload some representative malware developers’ application APK files later.</p> <p>As far, we uploaded 3 CSV file, including app basic informations, Virustotal's detection results and sensitive behaviors .</p> <p>(1)MalDevGenome_basic_info.csv</p> <p>App basic informations, including app name, package name, developer signature, apk file hashes, AV-Rank, malware family, etc.</p> <p>(2)MalDevGenome_virustotal_detection.csv</p> <p>Detailed VirusTotal's detection, including AV-Rank and details detection information of each anti-virus engine. These data are indexed according to Apk file MD5.</p> <p>(3)MalDevGenome_sensitive_behaviors.csv</p> <p>The sensitive behaviors of the samples, including permission, service, content provider, activity, broadcast receiver, library and hardcode string, etc. These data are indexed according to Apk file MD5.</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.