Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
94
datasets available to search
ShareScore release 0.9.0
Dataset results
94 results for “DSP”
A Longitudinal, Multi-omic Atlas Reveals the Emergence of a Spatially Organized Immunosuppressive Ecosystem in Resistant Melanoma [DSP protein]
GEO Series GSE318248. Homo sapiens. 510 samples. Type: Protein profiling by protein array.
Spatial transcriptomic profiling of bronchioles using GeoMx DSP
GEO Series GSE316672. Homo sapiens. 92 samples. Type: Other.
Transcriptomic profiling of activated-HSCs/myofibroblasts of CCl4-injured mouse liver by NanoString GeoMX DSP.
GEO Series GSE279640. Mus musculus. 9 samples. Type: Other.
Extracellular niche and tumor microenvironment enhance the impact of KRAS inhibitors in pancreatic cancer models [DSP]
GEO Series GSE249538. Mus musculus. 23 samples. Type: Expression profiling by high throughput sequencing; Other.
Transcriptomic profiling of MC38 tumor cells on murine MC38 liver metastasis sections by NanoString GeoMX DSP
GEO Series GSE253937. Mus musculus. 27 samples. Type: Expression profiling by high throughput sequencing.
Genetic Activation and Suppression of the Canonical WNT Pathway in Cardiac Myocyte-Specific Dsp Deficient Mouse Model of Arrhythmogenic Cardiomyopathy
GEO Series GSE180972. Mus musculus. 35 samples. Type: Expression profiling by high throughput sequencing.
nCounter PanCancer mouse immune profiling in CT26-bearing mouse tumor treated with DSP-0509 combined with anti PD-1 antibody
GEO Series GSE216890. Mus musculus. 12 samples. Type: Expression profiling by array.
Transcriptomic profiling of MC38 tumor cells and cancer-associated fibroblasts on murine MC38 liver metastasis sections by NanoString GeoMX DSP
GEO Series GSE225458. Mus musculus. 20 samples. Type: Other.
Transcriptomic profiling of Adjacents CAFs of MC38 tumor cells on murine MC38 liver metastasis sections by NanoString GeoMX DSP
GEO Series GSE253938. Mus musculus. 27 samples. Type: Expression profiling by high throughput sequencing.
The Neurotoxin DSP-4 Dysregulates the Locus Coeruleus-Norepinephrine System and Recapitulates Molecular and Behavioral Aspects of Prodromal Neurodegenerative Disease
GEO Series GSE221404. Mus musculus. 13 samples. Type: Expression profiling by high throughput sequencing.
GeoMx DSP- profiling of mouse spleen
GEO Series GSE275724. Mus musculus; synthetic construct. 67 samples. Type: Expression profiling by high throughput sequencing.
Periostin+ stromal cells guide lymphovascular invasion by cancer cells [DSP]
GEO Series GSE231806. Mus musculus. 11 samples. Type: Expression profiling by high throughput sequencing; Other.
A novel oncomicropeptide encoded by DSP-AS1 exhibits proneoplastic properties in lung adenocarcinoma by facilitating YTHDF1-induced translation initiation through mediation of mRNA circularization
GEO Series GSE249133. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing; Other.
Enhancing Immunotherapy Outcomes: Spatial Multi-Omics Predictive Models for Non-Small Cell Lung Cancer [GeoMx DSP]
GEO Series GSE292098. Homo sapiens. 315 samples. Type: Other.
CBFB-DSP-ChIPseq and YAP-DSP-ChIPseq
GEO Series GSE234731. Homo sapiens. 5 samples. Type: Expression profiling by high throughput sequencing.
DSP_count
Open the record for dataset details and reuse information.
Characterization of open chromatin sensitive to actin polymerization using DSP-MNase-DNA-seq
GEO Series GSE257539. Homo sapiens. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
DSP-MNase-DNA-seq
GEO Series GSE221380. Homo sapiens. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
MMS 1 Digital Signal Processor (DSP) Search Coil Magnetometer (SCM), Magnetic Field Power Spectral Density, Level 2 (L2), Fast Mode, 2 s Data
The MMS magnetic field power spectral density (BPSD) is computed onboard by the Digital Signal Processor (DSP). The fast Fourier transform (FFT) calculation is performed on a digitized version of analog signals from the Search Coil Magnetometer (SCM) in the SCM123 coordinate system, see SCM data product guide for details, https://lasp.colorado.edu/mms/sdc/public/datasets/fields/. This data product is computed in space from individual components that are not synchronized to the 1 second pulse. Therefore, the timing between channels can be inaccurate by a fraction of a second. The samples times are interval start times taken from the x component. The spectra are calculated via a 1024-point FFT algorithm on piecewise continuous sets of waveform data. Nine signals can be processed simultaneously. Six of the twelve DC-coupled E, DC-coupled V, or SCM signals (16384 samples/s) are selected for spectral processing at 100% duty cycle. In addition, the three AC-coupled signals (262,144 kS/s) each can be processed at 6.25% duty cycle. Each of the nine signals has 16, 1024-point FFT operations every second; the field-programmable gate array (FPGA) performs 144 FFTs per second. The FFT is performed by an arithmetic logic unit (ALU), which is controlled by a state machine. Both are hard-coded into the FPGA. The operation starts by applying a 1024-point Hanning window onto a waveform. Next, an FFT is implemented. The FFT is broken into a series of "butterfly" operations performed by the ALU. The result has real and imaginary data. Power spectra are calculated by taking the sum of squares of real and imaginary values (the ALU includes a multiplier), which produces a power spectrum with 512 frequency bins. The frequency bins are then combined to give pseudo-logarithmic frequency spacing (del f)/f. The spectra are reduced to 88 frequency bins with (del f)/f between 6% and 12% when possible. Narrow-band emissions can be fit to an accuracy of (del f)/f ~3%, allowing for an accurate determination of plasma density. The spectra can be averaged in time. The fastest reporting rate of any signal is 16 spectra per second. Reporting rates can be as slow a one spectra every 16 s (averaging 256 spectra). The DSP and SCM instrument papers can be found at https://link.springer.com/article/10.1007/s11214-014-0115-x and https://link.springer.com/article/10.1007/s11214-014-0096-9, respectively. The DSP and SCM data product guides can be found at https://lasp.colorado.edu/mms/sdc/public/datasets/fields/.
MMS 4 Digital Signal Processor (DSP) Double Probe (ADP, SDP), Electric Field Power Spectral Density, Level 2 (L2), Slow Mode, 16 s Data
The MMS electric field power spectral density (EPSD) is computed onboard by the Digital Signal Processor (DSP). The fast Fourier transform (FFT) calculation is performed on a digitized version of analog signals from the Axial Double Probe (ADP) and Spin-Plane Double Probe (SDP). This data product is computed in space from individual components that are not synchronized to the 1 second pulse. Therefore, the timing between channels can be inaccurate by a fraction of a second. The samples times are interval start times taken from the x component. The spectra are calculated via a 1024-point FFT algorithm on piecewise continuous sets of waveform data. Nine signals can be processed simultaneously. Six of the twelve DC-coupled E, DC-coupled V, or SCM signals (16384 samples/s) are selected for spectral processing at 100% duty cycle. In addition, the three AC-coupled signals (262,144 kS/s) each can be processed at 6.25% duty cycle. Each of the nine signals has 16, 1024-point FFT operations every second; the field-programmable gate array (FPGA) performs 144 FFTs per second. The FFT is performed by an arithmetic logic unit (ALU), which is controlled by a state machine. Both are hard-coded into the FPGA. The operation starts by applying a 1024-point Hanning window onto a waveform. Next, an FFT is implemented. The FFT is broken into a series of "butterfly" operations performed by the ALU. The result has real and imaginary data. Power spectra are calculated by taking the sum of squares of real and imaginary values (the ALU includes a multiplier), which produces a power spectrum with 512 frequency bins. The frequency bins are then combined to give pseudo-logarithmic frequency spacing (del f)/f. The spectra are reduced to 56 frequency bins with (del f)/f between 6% and 12% when possible. Narrow-band emissions can be fit to an accuracy of (del f)/f ~3%, allowing for an accurate determination of plasma density. The spectra can be averaged in time. The fastest reporting rate of any signal is 16 spectra per second. Reporting rates can be as slow a one spectra every 16 s (averaging 256 spectra). The averaging process has 48-bit accuracy to maximize the dynamic range. The amplitudes undergo a pseudo-logarithmic compression to an 8-bit number representing over 120 dB of dynamic range at ~5% precision.
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.