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112 results for “NeXus”

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zenodo32/100

Figure 1 in Integrative taxonomy at the nexus of population divergence and speciation in insular speckled rattlesnakes

Figure 1. Distribution of species of the speckled rattlesnake (Crotalus mitchellii) species complex in western North America. Symbols represent sampling locations and indicate species and populations recognised in this publication.

opennotspecifiedFeb 2018View details →
zenodo32/100

Figure 4 in Integrative taxonomy at the nexus of population divergence and speciation in insular speckled rattlesnakes

Figure 4. Scatterplots of first three axes from linear discriminant analyses of phenotypic data (a), principal component axes of microsatellite alleles (b), principal component axes of concatenated mitochondrial sequences (c), and principal component axes of concatenated nSNP data (d). Colour scheme: Crotalus angelensis = pink, C. mitchellii = red, C. polisi = gold, C. pyrrhus mainland = blue, C. pyrrhus El Muerto Island = black, C. pyrrhus Smith Island = brown, C. thalassoporus = purple.

opennotspecifiedFeb 2018View details →
zenodo32/100

Figure 3. Maximum likelihood phylogram constructed from 6755 in Integrative taxonomy at the nexus of population divergence and speciation in insular speckled rattlesnakes

Figure 3. Maximum likelihood phylogram constructed from 6755 nSNPs (left), with nodal support derived from summarising results of 100 bootstrap pseudoreplicates, and posterior assignment of each individual to clusters using STRUCTURE (right), based on 29,624 biallelic nSNPs. Colours correspond to populations or species discussed in the text.

opennotspecifiedFeb 2018View details →
zenodo32/100

Data repository for "Navigating trade-offs and sustainable development pathways in the Andean water-energy-food-ecosystem nexus"

<p>Data and code supporting the research article: Navigating trade-offs and sustainable development pathways in the Andean water-energy-food-ecosystem nexus.</p>

opencc-by-4.0Jul 2023View details →
zenodo32/100

Digitalisation for Sustainability: Understanding the Nexus

<p>This dynamic and informative video explores the cross-cutting nature of digitalisation in the context of the Sustainable Development Goals.&nbsp; It understands the &quot;nexus&quot; of digitalisation and sustainability by exploring the discourse of meanings surrounding the concepts and analyzing the implications of digitalisation in the 21st century.</p>

opencc-by-4.0Apr 2023View details →
ClinicalTrials.gov32/100

EVA Nexus Vitrectomy Device Field Observation Study

ClinicalTrials.gov study NCT05229094. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

Project Nexus: Providing Online Counseling for Home-Based HIV Testing

ClinicalTrials.gov study NCT02335138. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

Nexus Evaluation Primary Trident II Uncemented Shell

ClinicalTrials.gov study NCT05366712. IPD Sharing: NO. Countries: 1. Publications: 9.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

Project Daire: Derry/Londonderry as the Nexus City for Food, Education, Trust and Health

ClinicalTrials.gov study NCT04277312. IPD Sharing: NO. Countries: 1. Publications: 2.

closedIPD-NOFeb 2026View details →
dryad32/100

Data from: Selection on the morphology-physiology-performance nexus: lessons from freshwater stickleback morphs

Open the record for dataset details and reuse information.

publicOct 2018View details →
dryad32/100

Nexus-formatted text file of ISSR band presence/absence for 75 loci in Corallorhiza bentleyi

Open the record for dataset details and reuse information.

publicDec 2020View details →
dryad32/100

Nexus file for: Molecular diversification of hummingbirds

Open the record for dataset details and reuse information.

publicNov 2022View details →
zenodo28/100

Wife of Bath's Prologue Nexus File

<p>This nexus file was generated within Textual Communities (textualcommunities.org) from the regularized apparatus of the Wife of Bath&#39;s Prologue.&nbsp;</p>

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

Climate policy and the SDGs agenda: How does near-term action on nexus SDGs influence the achievement of long-term climate goals?

<p><span>The Sustainable Development Goals (SDGs) represent the global ambition to accelerate sustainable development. Several SDGs are directly related to climate change and policies aiming to mitigate it. This includes, among others, the set of SDGs that directly influence the climate, land, energy, and water (CLEW) nexus (SDGs 2, 6, 7, 13, 15). This study aims at understanding the synergies and trade-offs between climate policy and the SDGs agenda: how does near-term action on SDGs influence long-term climate goals?&nbsp;</span></p>

openFeb 2024View details →
zenodo28/100

German NFDI, FAIRmat-NFDI, NOMAD, NOMAD Oasis, pynxtools, pynxtools-em, NeXus, example datasets for electron microscopy

Open the record for dataset details and reuse information.

opencc-by-4.0May 2024View 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 →
ClinicalTrials.gov28/100

NEXUS Study for the Treatment of de Novo Native Coronary Artery Lesions

ClinicalTrials.gov study NCT00231283. IPD Sharing: Not stated. Countries: 1. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →
dryad28/100

Supporting data for Loik et al. 2017 Wavelength-Selective Solar Photovoltaic Systems: Powering greenhouses for plant growth at the food-energy-water nexus. Earth's Future

Open the record for dataset details and reuse information.

publicAug 2017View details →
geo24/100

The AXL-PYK2-PKCα axis as a nexus of stemness circuits in TNBC

GEO Series GSE166609. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenMar 2021View details →
geo24/100

Mitotic gene regulation by the N-MYC-WDR5-PDPK1 nexus

GEO Series GSE244899. Homo sapiens. 14 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenMar 2024View details →

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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