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dTwin4SkullShapes: a Digital Twin Dataset of Human Skull Shapes for Skull Missing Vs. Existing Part Learning and Prediction
<p>This dataset include the total of 757 human skulls reconstructed from computed tomography (CT) image sets. The CT image sets were constructed from the three main databases: (1) The Cancer Imaging Archive (DOI: 10.7937/TCIA.HMQ8-J677), (2) The New Mexico Decedent CT Images (DOI: 10.1055/s-0041-1730999), and (3) The MGU500+ (DOI: 10.1016/j.dib.2021.107524). The selected skull image sets have normal and full skull structures. We employed the voxelization technique to reconstruct skull meshes from CT image slices of each subject. The skull meshes were stored in *.off files. For each skull mesh, we estimated the skull shape by estimating the alpha shape of the skull meshes and stored in *.off files. The feature points on the skulls were also manually picked and stored in *.csv files with the form of 16x3 matrices, in which 16 is the number of feature points. A template skull shape was also deformed to the skull shapes of all subjects and stored in *.off files. The deformed skull shapes have unified skull feature points throughout all subjects. Moreover, we also added the source codes of the project. The details of the dataset were presented in the ReadMe.txt file. More details of how to use the source code project and the dataset were presented in the dTwin4SkullShapeTutorials.pdf file.</p>
Constructing Urban Tourism Space Digitally — Supplementary Material
<p>Over the past decade, Airbnb has emerged as the most popular platform for renting out single rooms or whole apartments. The impact of Airbnb listings on local neighborhoods has been controversially discussed in many cities around the world. The platform's widespread adoption led to changes in urban life, and in particular urban tourism. In this paper, we argue that urban tourism space can no longer be understood as a fixed, spatial entity. Instead, we follow a constructivist approach and argue that urban tourism space is (re-)produced digitally and collaboratively on online platforms such as Airbnb. We relate our work to a research direction in the CSCW community that is concerned with the role of digital technologies in the production and appropriation of urban space and use the concept of representations as a theoretical lens for our empirical study. In that study, we qualitatively analyzed how the two Berlin neighborhoods <em>Kreuzkölln</em> and <em>City West</em> are digitally constructed by Airbnb hosts in their listing descriptions. Moreover, we quantitatively investigated to what extend mentioned places differ between Airbnb hosts and <em>visitBerlin</em>, the city's destination management organization (DMO). In our qualitative analysis, we found that hosts primarily focus on facilities and places in close proximity to their apartment. In the traditional urban tourism hotspot <em>City West</em>, hosts referred to many places also mentioned by the DMO. In the neighborhood of <em>Kreuzkölln</em>, in contrast, hosts reframed everyday places such as parks or an immigrant food market as the must sees in the area. We discuss how Airbnb hosts contribute to the discursive production of urban neighborhoods and thus co-produce them as tourist destinations. With the emergence of online platforms such as Airbnb, power relations in the construction of tourism space might shift from the DMOs towards local residents who are now producing tourism space collaboratively.</p>
The Impact of the Digital Economy on the Integration of Rural Tertiary Industries: A Study Based on Dual Mediation and Dual Threshold Models
Open the record for dataset details and reuse information.
Troubadour Melodies: A Complete Digital Edition
<p>A complete digital edition of the music of the Troubadours, in modern and ancient notation. The data is gathered in a mySQL database compatible with the web application MedMel (10.5281/zenodo.7254259).</p> <p>The digital edition is publicly available at https://medmel.seai.uniroma1.it/troubadours/</p> <p> </p>
Georeferenced and Optimized Digital Asset Files of the Milner Hall in UNESCO World Heritage Sterkfontein Caves
<p>These are the asset files attached to the article: Georeferenced and Optimized Digital Asset of the Milner Hall. The article presents a fully visualized and georeferenced digital asset of a chamber in the Sterkfontein Caves, namely, the Milner Hall, in South Africa’s Cradle of Humankind (a UNESCO World Heritage Site). The creation of which is valuable in virtually connecting the global community to this important paleontological and archeological site. A considerable number of hominin fossils and earlier Stone Age archeological artefacts have been excavated from ancient cave infills at Sterkfontein, including the important Australopithecus specimens, “Little Foot” and “Mrs Ples”. This is also the only publicly accessible, paleoanthropological fossil-bearing cave in the Cradle of Humankind. However, the creation of accurate, visually detailed documentation and emplacement within the larger geomorphological context is challenging here. Real-world constraints, such as availability of equipment, resources and skills are often problematic at heritage sites like this. Furthermore, optimizing data for many uses that is so complex in its topology, scale and textures increases the difficulty in creating a digital asset of this nature. Our asset enables a wide range of use cases, especially within heritage, and scientific planning and outreach. It is a single digital model that contains a spectrum of believable visual effects and surveying accurate layers, and ultimately addresses the aforementioned challenges.</p> <p>This video shows the methods used in the production of this asset: https://youtu.be/e3bmxOzzKSY</p>
Video Showcasing the Digital Twin Developed for PRECINCT Living Lab 1 (Ljubljana)
<p>This video showcases the digital twin that was developed as part of LL1 (Ljubljana) in the PRECINCT Project.</p>
Continuous activation of the IL-17F driven inflammatory pathway in acute and chronic digital dermatitis lesions in dairy cattle
GEO Series GSE196166. Bos taurus. 29 samples. Type: Expression profiling by high throughput sequencing.
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.
Digital gene expression tag profiling of M. grisea G11 and T146
GEO Series GSE35840. Pyricularia grisea. 2 samples. Type: Expression profiling by SAGE.
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/.
THEMIS-E: Probe Electric Field Instrument and Search Coil Magnetometer Instrument, Digital Fields Board - digitally computed Filter Bank spectra and E12 peak and average in HF band (FBK).
The Filter Bank is part of the Digital fields board and provides band-pass filtering for EFI and SCM spectra as well as E12HF peak and average value calculations. The Filter Bank provides band-pass filtering for less computationally and power intensive spectra than the FFT would provide. The process is as follows: Signals are fed to the Filter Bank via a low-pass FIR filter with a cut-off frequency half that of the original signal maximum. The output is passed to the band-pass filters, is differenced from the original signal, then absolute value of the data is taken and averaged. The output from the low-pass filter is also sent to a second FIR filter with 2:1 decimation. This output is then fed back through the system. The process runs through 12 cascades for input at 8,192 samples/s and 13 for input at 16,384 samples/sec (EAC input only), reducing the signal and computing power by a factor 2 at each cascade. At each cascade a set of data is produced at a sampling frequency of 2^n from 2 Hz to the initial sampling frequency (frequency characteristics for each step are shown below in Table 1). The average from the Filter Bank is compressed to 8 bits with a pseudo-logarithmic encoder. The data is stored in sets of six frequency bins at 2.689 kHz, 572 Hz, 144.2 Hz, 36.2 Hz, 9.05 Hz, and 2.26 Hz. The average of the coupled E12HF signal and it's peak value are recorded over 62.5 ms windows (i.e. a 16 Hz sampling rate). Accumulation of values from signal 31.25 ms windows is performed externally. The analog signals fed into the FBK are E12DC and SCM1. Sensor and electronics design provided by UCB (J. W. Bonnell, F. S. Mozer), Digital Fields Board provided by LASP (R. Ergun), Search coil data provided by CETP (A. Roux). Table 1: Frequency Properties. Cascade Frequency content of Input Signal Low-pass Filter Cutoff Frequency Freuency Content of Low-pass Output Signal Filter Bank Frequency Band 0* 0 - 8 kHz 4 kHz 0 - 4 kHz 4 - 8 kHz 1 0 - 4 kHz 2 kHz 0 - 2 kHz 2 - 4 kHz 2 0 - 2 kHz 1 kHz 0 - 1 kHz 1 - 2 kHz 3 0 - 1 kHz 512 Hz 0 - 512 Hz 512 Hz - 1 kHz 4 0 - 512 Hz 256 Hz 0 - 256 Hz 256 - 512 Hz 5 0 - 256 Hz 128 Hz 0 - 128 Hz 128 - 256 Hz 6 0 - 128 Hz 64 Hz 0 - 64 Hz 64 - 128 Hz 7 0 - 64 Hz 32 Hz 0 - 32 Hz 32 - 64 Hz 8 0 - 32 Hz 16 Hz 0 - 16 Hz 16 - 32 Hz 9 0 - 16 Hz 8 Hz 0 - 8 Hz 8 - 16 Hz 10 0 - 8 Hz 4 Hz 0 - 4 Hz 4 - 8 Hz 11 0 - 4 Hz 2 Hz 0 - 2 Hz 2 - 4 Hz 12 0 - 2 Hz 1 Hz 0 - 1 Hz 1 - 2 Hz *Only available for 16,384 Hz sampling.
THEMIS-D: Probe Electric Field Instrument and Search Coil Magnetometer Instrument, Digital Fields Board - digitally computed Filter Bank spectra and E12 peak and average in HF band (FBK).
The Filter Bank is part of the Digital fields board and provides band-pass filtering for EFI and SCM spectra as well as E12HF peak and average value calculations. The Filter Bank provides band-pass filtering for less computationally and power intensive spectra than the FFT would provide. The process is as follows: Signals are fed to the Filter Bank via a low-pass FIR filter with a cut-off frequency half that of the original signal maximum. The output is passed to the band-pass filters, is differenced from the original signal, then absolute value of the data is taken and averaged. The output from the low-pass filter is also sent to a second FIR filter with 2:1 decimation. This output is then fed back through the system. The process runs through 12 cascades for input at 8,192 samples/s and 13 for input at 16,384 samples/sec (EAC input only), reducing the signal and computing power by a factor 2 at each cascade. At each cascade a set of data is produced at a sampling frequency of 2^n from 2 Hz to the initial sampling frequency (frequency characteristics for each step are shown below in Table 1). The average from the Filter Bank is compressed to 8 bits with a pseudo-logarithmic encoder. The data is stored in sets of six frequency bins at 2.689 kHz, 572 Hz, 144.2 Hz, 36.2 Hz, 9.05 Hz, and 2.26 Hz. The average of the coupled E12HF signal and it's peak value are recorded over 62.5 ms windows (i.e. a 16 Hz sampling rate). Accumulation of values from signal 31.25 ms windows is performed externally. The analog signals fed into the FBK are E12DC and SCM1. Sensor and electronics design provided by UCB (J. W. Bonnell, F. S. Mozer), Digital Fields Board provided by LASP (R. Ergun), Search coil data provided by CETP (A. Roux). Table 1: Frequency Properties. Cascade Frequency content of Input Signal Low-pass Filter Cutoff Frequency Freuency Content of Low-pass Output Signal Filter Bank Frequency Band 0* 0 - 8 kHz 4 kHz 0 - 4 kHz 4 - 8 kHz 1 0 - 4 kHz 2 kHz 0 - 2 kHz 2 - 4 kHz 2 0 - 2 kHz 1 kHz 0 - 1 kHz 1 - 2 kHz 3 0 - 1 kHz 512 Hz 0 - 512 Hz 512 Hz - 1 kHz 4 0 - 512 Hz 256 Hz 0 - 256 Hz 256 - 512 Hz 5 0 - 256 Hz 128 Hz 0 - 128 Hz 128 - 256 Hz 6 0 - 128 Hz 64 Hz 0 - 64 Hz 64 - 128 Hz 7 0 - 64 Hz 32 Hz 0 - 32 Hz 32 - 64 Hz 8 0 - 32 Hz 16 Hz 0 - 16 Hz 16 - 32 Hz 9 0 - 16 Hz 8 Hz 0 - 8 Hz 8 - 16 Hz 10 0 - 8 Hz 4 Hz 0 - 4 Hz 4 - 8 Hz 11 0 - 4 Hz 2 Hz 0 - 2 Hz 2 - 4 Hz 12 0 - 2 Hz 1 Hz 0 - 1 Hz 1 - 2 Hz *Only available for 16,384 Hz sampling.
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.
VO1/VO2 MARS VISUAL IMAGING SUBSYSTEM DIGITAL TERRAIN MODEL
This digital image map of Mars is a cartographic extension of a previously released set of CDROM volumes containing individual Viking Orbiter Images (PDS volumes VO_1001, VO_1002, etc.). The data in the latter are pristine, in the sense that they were processed only to the extent required to view them as images. They contain the artifacts and the radiometric, geometric, and photometric characteristics of the raw data transmitted by the spacecraft. This new volume set, on the other hand, contains cartographic compilations made by processing the raw images to reduce radiometric and geometric distortions and to form geodetically controlled Mosaicked Digital Image Models (MDIMs). (Because the photometric processing used in this MDIM was oversimplified, quantitative radiometric analysis on this data is not possible.) It also contains digitized versions of an airbrushed map of Mars as well as a listing of all IAU-approved feature names. In addition, special geodetic and photogrammetric processing has been performed to derive rasters of topographic data, or Digital Terrain Models (DTMs). The latter has a format similar to that of the MDIM, except that elevation values are used in the array instead of image brightness values. The Mars DTM exists in its entirety on Volume 7. Finally, Volume 7 has a listing of SPICE files derived for the Viking images used in the MDIM and the DTM. These files contain updated camera orientation matrices for each of the frames. The matrices are by-products of the photogrammetric processing for the control network required to compile the MDIM and the DTM.
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.
IHW COMET HALLEY LSPN NON-DIGITIZED IMAGES V1.0
NASA's International Halley Watch (IHW) has created a Comet Halley Archive. The collection of data spans the full wavelength range as submitted by scientists to the IHW. The observations belong to one of the following Disciplines: Amateur, Astrometry, Infrared Studies, Large-Scale Phenomena, Meteor Studies, Near-Nucleus Studies, Photometry and Polarimetry, Radio Studies, and Spectroscopy and Spectrophotometry. The data collected by these nine disciplines were augmented by Spacecraft measurements. The data were submitted to IHW, but the evaluation and selection for the Archive has been the primary responsibility of the Discipline Specialist Teams for each network in cooperation with the Lead Center. The data from the Large-Scale Phenomena Network has been organized into a set of digitized images and those left in the original analog state for reasons of quality, time coverage, or submission problems. These latter 1771 pieces of data are held in an archive both at Goddard Space Flight Center and with the Small Bodies Node of the Planetary Data System. (In the latter case, contact specifically the Comet Sub-Node of the PDS at the Laboratory for Atmospheric and Space Physics associated with the University of Colorado Boulder, for more assistance). Some of these analog images are presented in 'The International Halley Watch Atlas of Large-Scale Phenomena' (Brandt, John C., Niedner, Malcolm B. Jr., Rahe, Jurgen, 1992). The data cover a time range from 1984 December 22 through 1987 April 26 through 23 of the IHW Chronological Data Archive Collection
THEMIS-C: Probe Electric Field Instrument and Search Coil Magnetometer Instrument, Digital Fields Board - digitally computed Filter Bank spectra and E12 peak and average in HF band (FBK).
The Filter Bank is part of the Digital fields board and provides band-pass filtering for EFI and SCM spectra as well as E12HF peak and average value calculations. The Filter Bank provides band-pass filtering for less computationally and power intensive spectra than the FFT would provide. The process is as follows: Signals are fed to the Filter Bank via a low-pass FIR filter with a cut-off frequency half that of the original signal maximum. The output is passed to the band-pass filters, is differenced from the original signal, then absolute value of the data is taken and averaged. The output from the low-pass filter is also sent to a second FIR filter with 2:1 decimation. This output is then fed back through the system. The process runs through 12 cascades for input at 8,192 samples/s and 13 for input at 16,384 samples/sec (EAC input only), reducing the signal and computing power by a factor 2 at each cascade. At each cascade a set of data is produced at a sampling frequency of 2^n from 2 Hz to the initial sampling frequency (frequency characteristics for each step are shown below in Table 1). The average from the Filter Bank is compressed to 8 bits with a pseudo-logarithmic encoder. The data is stored in sets of six frequency bins at 2.689 kHz, 572 Hz, 144.2 Hz, 36.2 Hz, 9.05 Hz, and 2.26 Hz. The average of the coupled E12HF signal and it's peak value are recorded over 62.5 ms windows (i.e. a 16 Hz sampling rate). Accumulation of values from signal 31.25 ms windows is performed externally. The analog signals fed into the FBK are E12DC and SCM1. Sensor and electronics design provided by UCB (J. W. Bonnell, F. S. Mozer), Digital Fields Board provided by LASP (R. Ergun), Search coil data provided by CETP (A. Roux). Table 1: Frequency Properties. Cascade Frequency content of Input Signal Low-pass Filter Cutoff Frequency Freuency Content of Low-pass Output Signal Filter Bank Frequency Band 0* 0 - 8 kHz 4 kHz 0 - 4 kHz 4 - 8 kHz 1 0 - 4 kHz 2 kHz 0 - 2 kHz 2 - 4 kHz 2 0 - 2 kHz 1 kHz 0 - 1 kHz 1 - 2 kHz 3 0 - 1 kHz 512 Hz 0 - 512 Hz 512 Hz - 1 kHz 4 0 - 512 Hz 256 Hz 0 - 256 Hz 256 - 512 Hz 5 0 - 256 Hz 128 Hz 0 - 128 Hz 128 - 256 Hz 6 0 - 128 Hz 64 Hz 0 - 64 Hz 64 - 128 Hz 7 0 - 64 Hz 32 Hz 0 - 32 Hz 32 - 64 Hz 8 0 - 32 Hz 16 Hz 0 - 16 Hz 16 - 32 Hz 9 0 - 16 Hz 8 Hz 0 - 8 Hz 8 - 16 Hz 10 0 - 8 Hz 4 Hz 0 - 4 Hz 4 - 8 Hz 11 0 - 4 Hz 2 Hz 0 - 2 Hz 2 - 4 Hz 12 0 - 2 Hz 1 Hz 0 - 1 Hz 1 - 2 Hz *Only available for 16,384 Hz sampling.
VO1/VO2 MARS VISUAL IMAGING SUBSYSTEM DIGITAL IMAGING MODEL
This digital image map of Mars is a cartographic extension of a previously released set of CD volumes containing individual Viking Orbiter Images (PDS volumes VO_1001, VO_1002, etc.). The data in the latter are pristine, in the sense that they were processed only to the extent required to view them as images. They contain the artifacts and the radiometric, geometric, and photometric characteristics of the raw data transmitted by the spacecraft. This new volume set, on the other hand, contains cartographic compilations made by processing the raw images to reduce radiometric and geometric distortions and to form geodetically controlled Mosaicked Digital Image Models (MDIMs). (Because the photometric processing used in this MDIM was oversimplified, quantitative radiometric analysis on this data is not possible.) It also contains digitized versions of an airbrushed map of Mars as well as a listing of all IAU-approved feature names.
DS1 DIGITAL ELEVATION MAPS OF COMET 19P/BORRELLY V1.0
This data set contains both the USGS and DLR versions of the digital elevation model of the surface areas of comet 19P/Borrelly 1 (1904 Y2) that were visible and illuminated during the Deep Space 1 encounter. The model was computed using the parallax, caused by the spacecraft's motion, between the highest resolution images. The USGS data set also includes the surface normal directions at each location.
ScienceDex guides
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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.