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49 results for “File formats”

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dryad32/100

Supplementary tables S5, S7, S9, S10, original protein models fasta files used for alignments, aligned and manually curated protein modes files used for phylogenies (PHYLIP format), and phylogenetic trees of plant cell wall decomposition gene families from 44 basidiomycete genomes (.tre files)

Open the record for dataset details and reuse information.

publicJun 2020View details →
zenodo28/100

UPLC-IMS-QTOF data from road runoff samples - mz5 files with scans in centroid spectrum format

<p>This dataset has been acquired from road runoff samples collected as part of the Roulepur project (Office Fran&ccedil;ais de la Biodiversit&eacute;, Agence de l&rsquo;eau de Seine-Normandie).</p> <p>All samples were extracted by SPE with HLB cartridges at pH 2. Data was acquired with a UPLC-IMS-QTOF (Waters Vion) running Unifi, in HDMSe mode. Samples were injected in a random sequence (see order in samplemetadata.csv file). A list of markers was obtained from Unifi after alignment across all samples, and exported as a markertable.csv file. Each detected marker has m/z, retention time, drift time (ion mobility) values, and intensity values in all samples. Raw data was converted using Proteowizard MSConvert version 3.0.19014-f9d5b8a3b, using zlib compression, combining ion mobility scans, with the CWT peakpicking algorithm (snr=0, peakSpace=0) to transform profile to centroid data, and the zeroSamples option (removeExtra 1-). The chosen output format was .mz5.</p> <p>For file naming explanation, see the readme.txt file.</p>

opencc-by-4.0Dec 2020View details →
zenodo28/100

68 image lysozyme dataset recorded on the Jungfrau 16M detector at SwissFEL and formatted as a NeXus file, revised for clean cnxvalidate error report

<p>This kit includes an additional revised master file, lyso009a_0087.JF07T32V01_master_rev.h5 that provides compliance with the October 2019 NXmx specification as proposed in https://github.com/HDRMX/definitions.git</p> <p>To create a new NeXus master file, assuming DIALS is installed in the folder $DIALS, use this command:</p> <p>libtbx.python $DIALS/modules/cctbx_project/xfel/swissfel/jf16m_cxigeom2nexus.py unassembled_file=lyso009a_0087.JF07T32V01.h5 geom_file=16M_bernina_backview_optimized_adu_quads.geom wavelength=1.368479 detector_distance=97.830 mask_file=lyso009a_0087.JF07T32V01.mask.h5</p> <p>Geometry file is in CrystFEL format but has been realigned to group the modules hierarchically into quadrants.</p> <p>View the data using DIALS: dials.image_viewer lyso009a_0087.JF07T32V01_master.h5</p> <p>Process the data using DIALS, treating the images as stills, assuming 64 cores available on the system:<br> dials.stills_process mp.nproc=64 lyso009a_0087.JF07T32V01_master.h5 dispersion.gain=10 known_symmetry.space_group=P43212 known_symmetry.unit_cell=77,77,37,90,90,90 refinement_protocol.d_min_start=2.5</p> <p>Download DIALS at&nbsp;dials.github.io.</p> <p>After the DIALS run, for full NXmx compliance you will need the jungfrau portions of the script that was used to generate lyso009a_0087.JF07T32V01_master_rev.h5</p> <p>&nbsp;</p> <pre>#!/bin/bash cp Therm_6_2.nxs Therm_6_2_rev.nxs cp Therm_6_2_master.h5 Therm_6_2_master_rev.h5 cp jungfrau/lyso009a_0087.JF07T32V01_master.h5 jungfrau/lyso009a_0087.JF07T32V01_master_rev.h5 export curdat=`date +%FT%T.%3` export LD_LIBRARY_PATH=$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 h5copy -i jungfrau/lyso009a_0087.JF07T32V01_master.h5 -o jungfrau/lyso009a_0087.JF07T32V01_master_rev.h5 -s /entry/sample/beam -d /entry/instrument/beam -f ref export end_time=`h5dump -d &quot;/entry/end_time&quot; Therm_6_2_master.h5 | grep &quot;:&quot; | sed &#39;s/^.........//&#39;|sed &#39;s/.\$//&#39;` echo &quot;end_time: $end_time&quot; python &lt;&lt; &#39;EOL&#39; import h5py as h5 import numpy as np import os end_time=os.environ[&#39;end_time&#39;] curdat=os.environ[&#39;curdat&#39;] fvds = h5.File(&#39;Therm_6_2_rev.nxs&#39;,&#39;r+&#39;) fmaster = h5.File(&#39;Therm_6_2_master_rev.h5&#39;,&#39;r+&#39;) jungfrau= h5.File(&#39;jungfrau/lyso009a_0087.JF07T32V01_master_rev.h5&#39;,&#39;r+&#39;) fvds_keys=fvds.keys() fmaster_keys=fmaster.keys() jungfrau_keys=jungfrau.keys() fvds_entry=fvds[&#39;entry&#39;] fmaster_entry=fmaster[&#39;entry&#39;] jungfrau_entry=jungfrau[&#39;entry&#39;] fvds_entry_keys=fvds_entry.keys() fmaster_entry_keys=fmaster_entry.keys() jungfrau_entry_keys=jungfrau_entry.keys() fvds_entry_instrument=fvds[&#39;entry&#39;][&#39;instrument&#39;] fmaster_entry_instrument=fmaster[&#39;entry&#39;][&#39;instrument&#39;] jungfrau_entry_instrument=jungfrau[&#39;entry&#39;][&#39;instrument&#39;] fvds_entry_instrument_keys=fvds_entry_instrument.keys() fmaster_entry_instrument_keys=fmaster_entry_instrument.keys() jungfrau_entry_instrument_keys=jungfrau_entry_instrument.keys() fvds_entry_instrument_name=(fvds[&#39;entry&#39;][&#39;instrument&#39;][&#39;name&#39;]) fmaster_entry_instrument_name=(fmaster[&#39;entry&#39;][&#39;instrument&#39;][&#39;name&#39;]) jungfrau[&#39;entry&#39;][&#39;instrument&#39;].create_dataset(&quot;name&quot;, data=np.string_(&quot;Paul Scherrer Institute SwissFEL Aramis 1 (Alvra)&quot;)) jungfrau_entry_instrument_name=(jungfrau[&#39;entry&#39;][&#39;instrument&#39;][&#39;name&#39;]) fvds_entry_instrument_short_name=fvds_entry_instrument.attrs[&#39;short_name&#39;] fmaster_entry_instrument_short_name=fmaster_entry_instrument.attrs[&#39;short_name&#39;] jungfrau_entry_instrument_name.attrs.modify(&#39;short_name&#39;,np.string_(&quot;Alvra&quot;)) jungfrau_entry_instrument_short_name=jungfrau_entry_instrument_name.attrs[&#39;short_name&#39;] zero_offset=fmaster_entry_instrument[&#39;detector&#39;][&#39;module&#39;][&#39;fast_pixel_direction&#39;].attrs[&#39;offset&#39;] fmaster_det_z=fmaster_entry_instrument[&#39;transformations&#39;][&#39;det_z&#39;] fvds_det_z=fvds_entry_instrument[&#39;transformations&#39;][&#39;det_z&#39;] print(&#39;fvds_keys: &#39;,fvds_keys) print(&#39;fmaster_keys: &#39;,fmaster_keys) print(&#39;jungfrau_keys: &#39;,jungfrau_keys) print(&#39;fvds_entry_keys: &#39;,fvds_entry_keys) print(&#39;fmaster_entry_keys: &#39;,fmaster_entry_keys) print(&#39;jungfrau_entry_keys: &#39;,jungfrau_entry_keys) print(&#39;fvds_entry_instrument_keys: &#39;,fvds_entry_instrument_keys) print(&#39;fmaster_entry_instrument_keys: &#39;,fmaster_entry_instrument_keys) print(&#39;jungfrau_entry_instrument_keys: &#39;,jungfrau_entry_instrument_keys) print(&#39;fvds_entry_instrument_name: &#39;,fvds_entry_instrument_name) print(&#39;fmaster_entry_instrument_name: &#39;,fmaster_entry_instrument_name) print(&#39;jungfrau_entry_instrument_name: &#39;,jungfrau_entry_instrument_name) print(&#39;fvds_entry_instrument_short_name: &#39;,fvds_entry_instrument_short_name) print(&#39;fmaster_entry_instrument_short_name: &#39;,fmaster_entry_instrument_short_name) print(&#39;jungfrau_entry_instrument_short_name: &#39;,jungfrau_entry_instrument_short_name) print(&#39;fmaster_entry_instrument_detector_module_fast_pixel_direction_offset: &#39;,zero_offset) print(&#39;fmaster_entry_instrument_detector_detector_z_det_z: &#39;,fmaster_det_z) print(&#39;fmaster_entry_end_time: &#39;,end_time) fmaster.attrs.modify(&#39;file_time&#39;,np.string_(end_time)) fmaster.attrs.modify(&#39;file_name&#39;,np.string_(&#39;Therm_6_2_master_rev.h5&#39;)) fmaster.attrs.modify(&#39;HDF5_Version&#39;,np.string_(&#39;hdf5-1.8.18&#39;)) fvds.attrs.modify(&#39;file_time&#39;,np.string_(end_time)) fvds.attrs.modify(&#39;file_name&#39;,np.string_(&#39;Therm_6_2_master_rev.h5&#39;)) fvds.attrs.modify(&#39;HDF5_Version&#39;,np.string_(&#39;hdf5-1.10.5&#39;)) jungfrau.attrs.modify(&#39;file_time&#39;,np.string_(curdat)) jungfrau.attrs.modify(&#39;file_name&#39;,np.string_(&#39;lyso009a_0087.JF07T32V01_master.h5&#39;)) jungfrau.attrs.modify(&#39;HDF5_Version&#39;,np.string_(&#39;hdf5-1.10.5&#39;)) fvds_entry_instrument_name.attrs.modify(&#39;short_name&#39;,np.string_(fvds_entry_instrument.attrs[&#39;short_name&#39;])) fmaster_entry_instrument_name.attrs.modify(&#39;short_name&#39;,np.string_(fmaster_entry_instrument.attrs[&#39;short_name&#39;])) fmaster_entry_instrument[&#39;attenuator&#39;][&#39;attenuator_transmission&#39;].attrs.modify(&#39;units&#39;,np.string_(&quot;&quot;)) fmaster_entry_instrument[&#39;detector&#39;][&#39;count_time&#39;].attrs.modify(&#39;units&#39;,np.string_(&quot;s&quot;)) fvds_entry_instrument_name.attrs.modify(&#39;short_name&#39;,np.string_(fvds_entry_instrument.attrs[&#39;short_name&#39;])) fvds_entry_instrument[&#39;attenuator&#39;][&#39;attenuator_transmission&#39;].attrs.modify(&#39;units&#39;,np.string_(&quot;&quot;)) fvds_entry_instrument[&#39;detector&#39;][&#39;count_time&#39;].attrs.modify(&#39;units&#39;,np.string_(&quot;s&quot;)) fmaster_det_z.attrs.modify(&#39;offset&#39;,zero_offset) fvds_det_z.attrs.modify(&#39;offset&#39;,zero_offset) fmaster_entry[&#39;sample&#39;][&#39;transformations&#39;][&#39;phi&#39;].attrs.modify(&#39;offset&#39;,zero_offset) fmaster_entry[&#39;sample&#39;][&#39;transformations&#39;][&#39;chi&#39;].attrs.modify(&#39;offset&#39;,zero_offset) fmaster_entry[&#39;sample&#39;][&#39;transformations&#39;][&#39;sam_x&#39;].attrs.modify(&#39;offset&#39;,zero_offset) fmaster_entry[&#39;sample&#39;][&#39;transformations&#39;][&#39;sam_y&#39;].attrs.modify(&#39;offset&#39;,zero_offset) fmaster_entry[&#39;sample&#39;][&#39;transformations&#39;][&#39;sam_z&#39;].attrs.modify(&#39;offset&#39;,zero_offset) fmaster_entry[&#39;sample&#39;][&#39;transformations&#39;][&#39;omega&#39;].attrs.modify(&#39;offset&#39;,zero_offset) fvds_entry[&#39;sample&#39;][&#39;transformations&#39;][&#39;phi&#39;].attrs.modify(&#39;offset&#39;,zero_offset) fvds_entry[&#39;sample&#39;][&#39;transformations&#39;][&#39;chi&#39;].attrs.modify(&#39;offset&#39;,zero_offset) fvds_entry[&#39;sample&#39;][&#39;transformations&#39;][&#39;sam_x&#39;].attrs.modify(&#39;offset&#39;,zero_offset) fvds_entry[&#39;sample&#39;][&#39;transformations&#39;][&#39;sam_y&#39;].attrs.modify(&#39;offset&#39;,zero_offset) fvds_entry[&#39;sample&#39;][&#39;transformations&#39;][&#39;sam_z&#39;].attrs.modify(&#39;offset&#39;,zero_offset) fvds_entry[&#39;sample&#39;][&#39;transformations&#39;][&#39;omega&#39;].attrs.modify(&#39;offset&#39;,zero_offset) print(fmaster[&#39;entry&#39;][&#39;instrument&#39;][&#39;name&#39;].attrs[&#39;short_name&#39;]) print(fmaster[&#39;entry&#39;][&#39;instrument&#39;][&#39;name&#39;].attrs[&#39;short_name&#39;].shape) print(fmaster[&#39;entry&#39;][&#39;instrument&#39;][&#39;name&#39;].attrs[&#39;short_name&#39;].dtype) print(&quot;/entry/instrument/ELE_D0/pixel_mask_applied :&quot;,jungfrau_entry_instrument[&#39;ELE_D0&#39;][&#39;pixel_mask_applied&#39;]) del jungfrau_entry_instrument[&#39;ELE_D0&#39;][&#39;pixel_mask_applied&#39;] jungfrau_entry_instrument[&#39;ELE_D0&#39;].create_dataset(&quot;pixel_mask_applied&quot;,dtype=&#39;int8&#39;, data=1) print(&quot;/entry/instrument/ELE_D0/pixel_mask_applied :&quot;,jungfrau_entry_instrument[&#39;ELE_D0&#39;][&#39;pixel_mask_applied&#39;]) jungfrau_entry_source=jungfrau_entry.create_group(&#39;source&#39;) jungfrau_entry_source=jungfrau_entry[&#39;source&#39;] jungfrau_entry_source.attrs.modify(&#39;NX_class&#39;,np.string_(&quot;NXsource&quot;)) jungfrau_entry_source.create_dataset(&quot;name&quot;,data=np.string_(&quot;Paul Scherrer Institute SwissFEL&quot;)) jungfrau_entry_source[&#39;name&#39;].attrs.modify(&#39;short_name&#39;,np.string_(&quot;SwissFEL&quot;)) #jungfrau_entry_instrument.create_group[&#39;beam&#39;] #jungfrau_entry_instrument[&#39;beam&#39;]=jungfrau_entry[&#39;sample&#39;][&#39;beam&#39;] jungfrau_entry_instrument[&#39;beam&#39;].create_dataset(&#39;total_flux&#39;,dtype=&#39;float64&#39;,data=1000000000000.) jungfrau_entry_instrument[&#39;beam&#39;][&#39;total_flux&#39;].attrs.modify(&#39;units&#39;,np.string_(&#39;/pulse&#39;)) del jungfrau_entry[&#39;sample&#39;][&#39;beam&#39;] del fvds_entry_instrument.attrs[&#39;short_name&#39;] del fmaster_entry_instrument.attrs[&#39;short_name&#39;] del fmaster_entry_instrument[&#39;source&#39;] fvds.close() fmaster.close() jungfrau.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 $HOME/bin/nxvalidate -a NXmx -l /home/yaya/hdrmx_rev_29Sep19/hdrmx/definitions jungfrau/lyso009a_0087.JF07T32V01_master_rev.h5 </pre> <p>&nbsp;</p>

opencc-by-4.0Nov 2019View details →
zenodo24/100

Wig formatted file

<p>Wig formatted file output from TRANSIT with all transposon insertions for the publication &#39;A Mutant Fitness Assay Identifies Bacterial Interactions in a Model Ocean Hot-Spot&#39;</p>

opencc-by-4.0Dec 2022View details →
zenodo24/100

Trajectory files for "Where does the energy go during the interstellar NH3 formation on water ice? A computational study"

<p>CP2K trajectory files for the N+H, NH + H and NH2 + H reactions on the amorphous water ice surface</p>

opencc-by-4.0Dec 2022View details →
nasa24/100

MODIS/Aqua 24-hour Spacecraft ephemeris/orbit data files to be read via SDP Toolkit Binary Format - NRT

PM1EPHND is the Aqua Near Real Time (NRT) daily spacecraft definitive ephemeris data file in native format. This is MODIS Ancillary Data. The data collection consists of PM1 Platform Attitude Data that has been preprocessed by ECS to an internal standard supported by the ECS SDP Toolkit. This data is typically used in determining the geolocation of earth remote sensing observations.The file name format is the following:PM1EPHND_NRT.Ayyyyddd.hhmm.vvv where from left to right:PM1 = PM1 (Aqua);EPH = Spacecraft Ephemeris; N = Native format; D = Definitive; A = Acquisition; yyyy = data year, ddd = Julian data day, hh = data hour, mm = data minute; vvv = Version ID.

restrictednotspecifiedApr 2025View details →
nasa24/100

Extrapolated Orbital data files to be read via SDP toolkit, Binary Format

AM1EPHNE is the Terra Near Real Time (NRT) 2-hour spacecraft Extrapolated ephemeris data file in native format. The file name format is the following: AM1EPHNE.Ayyyyddd.hhmm.vvv.yyyydddhhmmss where from left to right: E = Extrapolated; N = Native format; A = AM1 (Terra); yyyy = data year, ddd = Julian data day, hh = data hour, mm = data minute; vvv = Version ID; yyyy = production year, ddd = Julian production day, hh = production hour, mm = production minute, and ss = production second. Data set information:http://modis.gsfc.nasa.gov/sci_team/

restrictednotspecifiedMay 2025View details →
nasa24/100

MODIS/Aqua Near Real Time (NRT) 24-hour Spacecraft ephemeris/orbit data files to be read via SDP Toolkit Binary Format

PM1EPHND is the Aqua Near Real Time (NRT) daily spacecraft definitive ephemeris data file in native format. This is MODIS Ancillary Data. The data collection consists of PM1 Platform Attitude Data that has been preprocessed by ECS to an internal standard supported by the ECS SDP Toolkit. This data is typically used in determining the geolocation of earth remote sensing observations.The file name format is the following:PM1EPHND_NRT.Ayyyyddd.hhmm.vvv where from left to right: PM1 = PM1 (Aqua); EPH = Spacecraft Ephemeris; N = Native format; D = Definitive; A = Acquisition; yyyy = data year, ddd = Julian data day, hh = data hour, mm = data minute; vvv = Version ID.

restrictednotspecifiedMay 2025View details →
nasa0/100

Files containing only extrapolated orbital metadata, to be read via SDP Toolkit, Binary Format

AM1EPHNE is the Terra Near Real Time (NRT) 2-hour spacecraft Extrapolated ephemeris data file in native format. The file name format is the following: AM1EPHNE.Ayyyyddd.hhmm.vvv.yyyydddhhmmsswhere from left to right: E = Extrapolated; N = Native format; A = AM1 (Terra); yyyy = data year, ddd = Julian data day, hh = data hour, mm = data minute; vvv = Version ID; yyyy = production year, ddd = Julian production day, hh = production hour, mm = production minute, and ss = production second. Data set information: http://modis.gsfc.nasa.gov/sci_team/

restrictednotspecifiedApr 2025View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record