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Dataset results
482 results for “Digital data”
The differences of the transcriptional profile between wide-type maize and transgenic ZmPIS maize (drought tolerance) by the assay of digital gene expression profile data
GEO Series GSE31944. Zea mays. 14 samples. Type: Expression profiling by high throughput sequencing.
Digital transformation of herbal medicine: Conversion to biological entity data using tonifying herbal medicine-induced transcriptome sequencing_SW1783_batchD
GEO Series GSE245907. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Digital transformation of herbal medicine: Conversion to biological entity data using tonifying herbal medicine-induced transcriptome sequencing_HepG2_batchC
GEO Series GSE244704. Homo sapiens. 60 samples. Type: Expression profiling by high throughput sequencing.
Qialitative data WP2 and WP3: Examining acceptance, readability and comprehension of Digital communication as part of three population-based cancer screening programs
<p>Data for the the ZonMw project: <em>Examining acceptance, readability and comprehension of Digital communication as part of three population-based cancer screening programs</em></p> <p>Qualitative results of the co-creation sessions (WP2) in an excel file</p> <p>Qualitative results of the three posters for the workshop (WP3) in images</p> <p><em>Note. Part 1 (quantitative datasetwas for WP1) was stored in Zenodo, link (of persistent identifier) 10.5281/zenodo.6977196</em></p> <p><em> </em></p>
Digital transformation of herbal medicine: Conversion to biological entity data using tonifying herbal medicine-induced transcriptome sequencing_HT29_batchB
GEO Series GSE244690. Homo sapiens. 60 samples. Type: Expression profiling by high throughput sequencing.
Digital Gene Expression data of two Trypanosoma brucei hybrids (T.b.brucei crossed with T.b.gambiense (group 2))
GEO Series GSE24435. Trypanosoma brucei brucei x Trypanosoma gambiense 181; Trypanosoma brucei brucei x Trypanosoma gambiense 414. 2 samples. Type: Expression profiling by high throughput sequencing.
Digital transformation of herbal medicine: Conversion to biological entity data using tonifying herbal medicine-induced transcriptome sequencing_A549_batchC
GEO Series GSE244684. Homo sapiens. 60 samples. Type: Expression profiling by high throughput sequencing.
Digital transformation of herbal medicine: Conversion to biological entity data using tonifying herbal medicine-induced transcriptome sequencing_A549_batchD
GEO Series GSE244685. Homo sapiens. 60 samples. Type: Expression profiling by high throughput sequencing.
Digital transformation of herbal medicine: Conversion to biological entity data using tonifying herbal medicine-induced transcriptome sequencing_HT29_batchC
GEO Series GSE244691. Homo sapiens. 60 samples. Type: Expression profiling by high throughput sequencing.
Sloan Digital Sky Survey Broad Absorption Line Quasars Catalog: 5th Data Release
No Description Available
Digital Gene Expression data of human serum sensitive and resistant group 2 Trypanosoma brucei gambiense
GEO Series GSE23650. Trypanosoma brucei brucei; Trypanosoma brucei gambiense. 11 samples. Type: Expression profiling by high throughput sequencing.
Complementary Material for the paper "Applying Directed Qualitative Content Analysis for Data-driven Persona Creation: A Case Study on User-Centred Digital Marketplace Development"
Open the record for dataset details and reuse information.
Research on the impact of the digital economy on narrowing the gap between urban and rural areas—An empirical analysis based on provincial panel data
Open the record for dataset details and reuse information.
Digital Gene Expression data of a group 1 Trypanosoma brucei gambiense vs a Trypanosoma brucei brucei
GEO Series GSE23885. Trypanosoma brucei gambiense; Trypanosoma brucei brucei. 3 samples. Type: Expression 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/.
IHW COMET LSPN DERIVED DIGITIZED IMG DATA REC CROMMELIN V1.0
In preparation for the concerted international study of Comet Halley, the IHW conducted a trial run with observations of Comet Crommelin, largely during February and March of 1984.
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.
MMS 3 Digital Signal Processor (DSP) Search Coil Magnetometer (SCM), Magnetic Field Power Spectral Density, Level 2 (L2), Slow Mode, 16 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 (scm1 = - x sensor; scm2 = -z sensor; scm3 = -y sensor). 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 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.
MMS 4 Digital Signal Processor (DSP) Search Coil Magnetometer (SCM), Magnetic Field Power Spectral Density, Level 2 (L2), Slow Mode, 16 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 (scm1 = - x sensor; scm2 = -z sensor; scm3 = -y sensor). 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 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.
IHW COMET SPEC EDITED DIGITALIZED IMAGE DATA RECORD GZ V1.0
In preparation for the concerted international study of Comet Halley, the IHW conducted a trial run with observations of Comet Crommelin, largely during February and March of 1984.
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.