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Figure 5 from: Sochorová Z, Döbbeler P, Sochor M, van Rooy J (2019) Octospora conidiophora (Pyronemataceae) – a new species from South Africa and the first report of anamorph in bryophilous Pezizales. MycoKeys 54: 49-76. https://doi.org/10.3897/mycokeys.54.34571
Figure 5 Microscopic characters of Octosporaconidiophora. A Cells of the outermost layer of excipulum from an outside view stained with CRBBMycelial hypha stained with CRBC Appressoria and hyphae on a leaf of Sematophyllumbrachycarpum in tap water D Appressorium stained with LACBE Germinating conidium stained with LPCBF Germinated conidium produced a bifurcate warted hypha (right arrow), appressorium (left arrow) probably not connected to the conidium, in LACBA, B, F ZE77/18 C, E ZE11/18 D holotype ZE48/18.
Figure 13 from: Sochorová Z, Döbbeler P, Sochor M, van Rooy J (2019) Octospora conidiophora (Pyronemataceae) – a new species from South Africa and the first report of anamorph in bryophilous Pezizales. MycoKeys 54: 49-76. https://doi.org/10.3897/mycokeys.54.34571
Figure 13 Geographical distribution of Trichosteleumperchlorosum in southern Africa based on records in BM, L, MO and PRE.
Figure 2 from: Sochorová Z, Döbbeler P, Sochor M, van Rooy J (2019) Octospora conidiophora (Pyronemataceae) – a new species from South Africa and the first report of anamorph in bryophilous Pezizales. MycoKeys 54: 49-76. https://doi.org/10.3897/mycokeys.54.34571
Figure 2 Maximum clade credibility tree with estimated divergence times, based on SSU and LSU data; calibration points are marked by red circles, posterior probabilities shown above branches, bars indicate the 95% highest posterior density (HPD) intervals.
Figure 3 from: Sochorová Z, Döbbeler P, Sochor M, van Rooy J (2019) Octospora conidiophora (Pyronemataceae) – a new species from South Africa and the first report of anamorph in bryophilous Pezizales. MycoKeys 54: 49-76. https://doi.org/10.3897/mycokeys.54.34571
Figure 3 Octosporaconidiophora. A–E Apothecia in situ F Habitat A, E ZE63/18 B ZE57/18 C, F ZE11/18 D holotype ZE48/18.
Figure 4 from: Sochorová Z, Döbbeler P, Sochor M, van Rooy J (2019) Octospora conidiophora (Pyronemataceae) – a new species from South Africa and the first report of anamorph in bryophilous Pezizales. MycoKeys 54: 49-76. https://doi.org/10.3897/mycokeys.54.34571
Figure 4 Microscopic characters of Octosporaconidiophora. A Ascospores inside ascus stained with CRBB Free ascospores in tap water C Paraphyse in tap water D Young ascus in tap water E Margin of apothecium in tap water F Hair and excipular cells in tap water A–F ZE77/18.
Figure 11 from: Sochorová Z, Döbbeler P, Sochor M, van Rooy J (2019) Octospora conidiophora (Pyronemataceae) – a new species from South Africa and the first report of anamorph in bryophilous Pezizales. MycoKeys 54: 49-76. https://doi.org/10.3897/mycokeys.54.34571
Figure 11 Geographical distribution of Sematophyllumbrachycarpum in southern Africa based on records in BM, L, MO and PRE.
Figure 1 from: Sochorová Z, Döbbeler P, Sochor M, van Rooy J (2019) Octospora conidiophora (Pyronemataceae) – a new species from South Africa and the first report of anamorph in bryophilous Pezizales. MycoKeys 54: 49-76. https://doi.org/10.3897/mycokeys.54.34571
Figure 1 Bayesian phylogeny inference, based on concatenated alignment of EF1α, LSU and SSU sequences. Bayesian posterior probabilities are shown above branches; Otidealeporina serves as outgroup; trees based on analysis of each locus are shown in Suppl. material 2: Fig. S1.
Figure 10 from: Sochorová Z, Döbbeler P, Sochor M, van Rooy J (2019) Octospora conidiophora (Pyronemataceae) – a new species from South Africa and the first report of anamorph in bryophilous Pezizales. MycoKeys 54: 49-76. https://doi.org/10.3897/mycokeys.54.34571
Figure 10 Sematophyllumbrachycarpum.A Plants B Typical shoot with leaves C Leaf D Leaf base with alar cells.
Figure 8 from: Sochorová Z, Döbbeler P, Sochor M, van Rooy J (2019) Octospora conidiophora (Pyronemataceae) – a new species from South Africa and the first report of anamorph in bryophilous Pezizales. MycoKeys 54: 49-76. https://doi.org/10.3897/mycokeys.54.34571
Figure 8 Geographical distribution of the four lineages of Octosporaconidiophora agg. in South Africa. Red circle: lineage A (O. conidiophora s.str.); green triangle: lineage B; light blue square: lineage C, dark blue star: lineage D.
Figure 3 from: Miyazaki Y, Teramura A, Senou H (2019) Preliminary report on bycatch fish species collected from the Tokyo Submarine Canyon, Japan. ZooKeys 843: 129-147. https://doi.org/10.3897/zookeys.843.32410
Figure 3 Photos of the voucher specimens of the actinopterygian species (Polymixiiformes, Gadiformes, and Beryciformes) collected from the Uraga Suido Channel, the Sagami Sea, Japan. A KPN-NI 47868, Polymixia japonica, 171 mm SL BKPM-NI 47880, Coryphaenoides marginatus, 575 mm TL (tail broken) C KPN-NI 47876, Coelorinchus kishinouyei, 302 mm TL (tail broken) DMTUF-P 30680, Coelorinchus tokiensis, 772 mm TL (tail broken) E KPN-NI 47870, Beryx decadactylus, 210 mm SL F KPN-NI 47871, Gephyroberyx japonicus, 204 mm SL G KPN-NI 47872, Hoplostethus japonicus, 140 mm SL.
Figure 4 from: Miyazaki Y, Teramura A, Senou H (2019) Preliminary report on bycatch fish species collected from the Tokyo Submarine Canyon, Japan. ZooKeys 843: 129-147. https://doi.org/10.3897/zookeys.843.32410
Figure 4 Photos of the voucher specimens of the actinopterygian species (Perciformes, and Pleuronectiformes) collected from the Uraga Suido Channel, the Sagami Sea, Japan. A KPN-NI 47875, Helicolenus hilgendorfii, 146 mm SL BKPM-NI 46355, Sebastes iracundus, 531 mm SL C KPN-NI 47877, Scorpaena neglecta, 158 mm SL D KPN-NI 47885, Lepidotrigla guentheri, 122 mm SL E, F KPN-NI 47887, Scalicus amiscus, 179 mm SL G KPN-NI 47879, Malakichthys griseus, 150 mm SL HKPM-NI 46357, Atrobucca nibe, 348 mm SL IMTUF-P 30676, Pentaceros japonicus, 162 mm SL J KPN-NI 47888, Zoarcidae sp., 78 mm SL KMTUF-P 30674, Ruvettus pretiosus, 536 mm SL L, M KPN-NI 47878, Tanakius kitaharae, 228 mm SL.
Figure 1 from: Miyazaki Y, Teramura A, Senou H (2019) Preliminary report on bycatch fish species collected from the Tokyo Submarine Canyon, Japan. ZooKeys 843: 129-147. https://doi.org/10.3897/zookeys.843.32410
Figure 1 Map showing the study site in the eastern Uraga Suido Channel (extending from Cape Tsurugisaki [south-east of the Miura Peninsula] to Cape Sunosaki [south-west of the Boso Peninsula] and from Cape Kan-nonzaki [east of the Miura Peninsula] to Cape Futtsu [west of the Boso Peninsula]). The channel is included within the broad definition of Tokyo Bay (northward from the Uraga-suido Channel) and Sagami Bay (from Cape Irozaki on the southern Izu Peninsula to Cape Nojimazaki, on the southern Boso Peninsula, including Izu-oshima Island). The channel is not included in the narrow definition of Tokyo Bay.
Figure 5 from: Miyazaki Y, Teramura A, Senou H (2019) Preliminary report on bycatch fish species collected from the Tokyo Submarine Canyon, Japan. ZooKeys 843: 129-147. https://doi.org/10.3897/zookeys.843.32410
Figure 5 Voucher images deposited to the Image Database of Fishes at the Kanagawa Prefectural Museum of Natural History without specimens collected from the Uraga Suido Channel, the Sagami Sea, Japan. A–DKPM-NR 193004A–D, Mitsukurina owstoniE, FKPM-NR 193003A–B, Lophiomus setigerusGKPM-NR 193002, Doederleinia berycoidesHKPM-NR 193005, Eopsetta grigorjewi.
Figure 2 from: Miyazaki Y, Teramura A, Senou H (2019) Preliminary report on bycatch fish species collected from the Tokyo Submarine Canyon, Japan. ZooKeys 843: 129-147. https://doi.org/10.3897/zookeys.843.32410
Figure 2 Photos of the voucher specimens of the agnathan and chondrichthyans species collected from the Uraga Suido Channel, the Sagami Sea, Japan. AMTUF-P 30681: Eptatretus atami, 503 mm TLB KPN-NI 47883: Chimaera phantasma, 481 mm TL (tail broken) CKPM-NI 46348: Hydrolagus mitsukurii, 315 mm TL (tail broken) DMTUF-P 30716: Cephaloscyllium umbratile, 489 mm TLEKPM-NI 46359, Apristurus platyrhynchus, 634 mm TLFKPM-NI 46352: Chlamydoselachus anguineus, 1258 mm TLG KPN-NI 47884: Heptranchias perlo, 910 mm TLHKPM-NI 46346: Dalatias licha, 472 mm TLI KPN-NI 47882: Deania calcea, 656 mm TLJKPM-NI 46349: Deania hystricosa, 844 mm TLKMTUF-P 30717: Centrophorus atromarginatus, 453 mm TLLKPM-NI 46356: Cirrhigaleus barbifer, 846 mm TLMKPM-NI 46360: Squalus mitsukurii, 581 mm TL.
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 $DIALS/modules/cctbx_project/xfel/swissfel/jf16m_cxigeom2nexus.py unassembled_file=lyso009a_0087.JF07T32V01.h5 geom_file=16M_bernina_backview_optimized_adu_quads.geom wavelength=1.368479 detector_distance=97.830 mask_file=lyso009a_0087.JF07T32V01.mask.h5</p> <p>Geometry file is in CrystFEL format but has been realigned to group the modules hierarchically into quadrants.</p> <p>View the data using DIALS: dials.image_viewer lyso009a_0087.JF07T32V01_master.h5</p> <p>Process the data using DIALS, treating the images as stills, assuming 64 cores available on the system:<br> dials.stills_process mp.nproc=64 lyso009a_0087.JF07T32V01_master.h5 dispersion.gain=10 known_symmetry.space_group=P43212 known_symmetry.unit_cell=77,77,37,90,90,90 refinement_protocol.d_min_start=2.5</p> <p>Download DIALS at dials.github.io.</p> <p>After the DIALS run, for full NXmx compliance you will need the jungfrau portions of the script that was used to generate lyso009a_0087.JF07T32V01_master_rev.h5</p> <p> </p> <pre>#!/bin/bash cp Therm_6_2.nxs Therm_6_2_rev.nxs cp Therm_6_2_master.h5 Therm_6_2_master_rev.h5 cp jungfrau/lyso009a_0087.JF07T32V01_master.h5 jungfrau/lyso009a_0087.JF07T32V01_master_rev.h5 export curdat=`date +%FT%T.%3` export LD_LIBRARY_PATH=$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 "/entry/end_time" Therm_6_2_master.h5 | grep ":" | sed 's/^.........//'|sed 's/.\$//'` echo "end_time: $end_time" python << 'EOL' import h5py as h5 import numpy as np import os end_time=os.environ['end_time'] curdat=os.environ['curdat'] fvds = h5.File('Therm_6_2_rev.nxs','r+') fmaster = h5.File('Therm_6_2_master_rev.h5','r+') jungfrau= h5.File('jungfrau/lyso009a_0087.JF07T32V01_master_rev.h5','r+') fvds_keys=fvds.keys() fmaster_keys=fmaster.keys() jungfrau_keys=jungfrau.keys() fvds_entry=fvds['entry'] fmaster_entry=fmaster['entry'] jungfrau_entry=jungfrau['entry'] fvds_entry_keys=fvds_entry.keys() fmaster_entry_keys=fmaster_entry.keys() jungfrau_entry_keys=jungfrau_entry.keys() fvds_entry_instrument=fvds['entry']['instrument'] fmaster_entry_instrument=fmaster['entry']['instrument'] jungfrau_entry_instrument=jungfrau['entry']['instrument'] 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['entry']['instrument']['name']) fmaster_entry_instrument_name=(fmaster['entry']['instrument']['name']) jungfrau['entry']['instrument'].create_dataset("name", data=np.string_("Paul Scherrer Institute SwissFEL Aramis 1 (Alvra)")) jungfrau_entry_instrument_name=(jungfrau['entry']['instrument']['name']) fvds_entry_instrument_short_name=fvds_entry_instrument.attrs['short_name'] fmaster_entry_instrument_short_name=fmaster_entry_instrument.attrs['short_name'] jungfrau_entry_instrument_name.attrs.modify('short_name',np.string_("Alvra")) jungfrau_entry_instrument_short_name=jungfrau_entry_instrument_name.attrs['short_name'] zero_offset=fmaster_entry_instrument['detector']['module']['fast_pixel_direction'].attrs['offset'] fmaster_det_z=fmaster_entry_instrument['transformations']['det_z'] fvds_det_z=fvds_entry_instrument['transformations']['det_z'] print('fvds_keys: ',fvds_keys) print('fmaster_keys: ',fmaster_keys) print('jungfrau_keys: ',jungfrau_keys) print('fvds_entry_keys: ',fvds_entry_keys) print('fmaster_entry_keys: ',fmaster_entry_keys) print('jungfrau_entry_keys: ',jungfrau_entry_keys) print('fvds_entry_instrument_keys: ',fvds_entry_instrument_keys) print('fmaster_entry_instrument_keys: ',fmaster_entry_instrument_keys) print('jungfrau_entry_instrument_keys: ',jungfrau_entry_instrument_keys) print('fvds_entry_instrument_name: ',fvds_entry_instrument_name) print('fmaster_entry_instrument_name: ',fmaster_entry_instrument_name) print('jungfrau_entry_instrument_name: ',jungfrau_entry_instrument_name) print('fvds_entry_instrument_short_name: ',fvds_entry_instrument_short_name) print('fmaster_entry_instrument_short_name: ',fmaster_entry_instrument_short_name) print('jungfrau_entry_instrument_short_name: ',jungfrau_entry_instrument_short_name) print('fmaster_entry_instrument_detector_module_fast_pixel_direction_offset: ',zero_offset) print('fmaster_entry_instrument_detector_detector_z_det_z: ',fmaster_det_z) print('fmaster_entry_end_time: ',end_time) fmaster.attrs.modify('file_time',np.string_(end_time)) fmaster.attrs.modify('file_name',np.string_('Therm_6_2_master_rev.h5')) fmaster.attrs.modify('HDF5_Version',np.string_('hdf5-1.8.18')) fvds.attrs.modify('file_time',np.string_(end_time)) fvds.attrs.modify('file_name',np.string_('Therm_6_2_master_rev.h5')) fvds.attrs.modify('HDF5_Version',np.string_('hdf5-1.10.5')) jungfrau.attrs.modify('file_time',np.string_(curdat)) jungfrau.attrs.modify('file_name',np.string_('lyso009a_0087.JF07T32V01_master.h5')) jungfrau.attrs.modify('HDF5_Version',np.string_('hdf5-1.10.5')) fvds_entry_instrument_name.attrs.modify('short_name',np.string_(fvds_entry_instrument.attrs['short_name'])) fmaster_entry_instrument_name.attrs.modify('short_name',np.string_(fmaster_entry_instrument.attrs['short_name'])) fmaster_entry_instrument['attenuator']['attenuator_transmission'].attrs.modify('units',np.string_("")) fmaster_entry_instrument['detector']['count_time'].attrs.modify('units',np.string_("s")) fvds_entry_instrument_name.attrs.modify('short_name',np.string_(fvds_entry_instrument.attrs['short_name'])) fvds_entry_instrument['attenuator']['attenuator_transmission'].attrs.modify('units',np.string_("")) fvds_entry_instrument['detector']['count_time'].attrs.modify('units',np.string_("s")) fmaster_det_z.attrs.modify('offset',zero_offset) fvds_det_z.attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['phi'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['chi'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_x'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_y'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_z'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['omega'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['phi'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['chi'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_x'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_y'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_z'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['omega'].attrs.modify('offset',zero_offset) print(fmaster['entry']['instrument']['name'].attrs['short_name']) print(fmaster['entry']['instrument']['name'].attrs['short_name'].shape) print(fmaster['entry']['instrument']['name'].attrs['short_name'].dtype) print("/entry/instrument/ELE_D0/pixel_mask_applied :",jungfrau_entry_instrument['ELE_D0']['pixel_mask_applied']) del jungfrau_entry_instrument['ELE_D0']['pixel_mask_applied'] jungfrau_entry_instrument['ELE_D0'].create_dataset("pixel_mask_applied",dtype='int8', data=1) print("/entry/instrument/ELE_D0/pixel_mask_applied :",jungfrau_entry_instrument['ELE_D0']['pixel_mask_applied']) jungfrau_entry_source=jungfrau_entry.create_group('source') jungfrau_entry_source=jungfrau_entry['source'] jungfrau_entry_source.attrs.modify('NX_class',np.string_("NXsource")) jungfrau_entry_source.create_dataset("name",data=np.string_("Paul Scherrer Institute SwissFEL")) jungfrau_entry_source['name'].attrs.modify('short_name',np.string_("SwissFEL")) #jungfrau_entry_instrument.create_group['beam'] #jungfrau_entry_instrument['beam']=jungfrau_entry['sample']['beam'] jungfrau_entry_instrument['beam'].create_dataset('total_flux',dtype='float64',data=1000000000000.) jungfrau_entry_instrument['beam']['total_flux'].attrs.modify('units',np.string_('/pulse')) del jungfrau_entry['sample']['beam'] del fvds_entry_instrument.attrs['short_name'] del fmaster_entry_instrument.attrs['short_name'] del fmaster_entry_instrument['source'] 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> </p>
Exploring the ontological links between Human Ecodynamics and field Archaeology through the integration of archaeological reports into DataARC's landscape ontology (MPhil by research thesis)
<p>The Zip file contains another 8 different folders. In each one, you will find the images and charts from Skútustaðir’s Archaeological Reports (permalink to the reports: https://www.nabohome.org/cgi-bin/explore.pl?seq=3). Each folder corresponds to one report. File’s names are abbreviated. Please, see the attached metadata for a full understanding of the abbreviated names. These images and charts were produced as part of the creation of a dataset for my MPhil thesis.</p> <p>Project_files_metadata provides all the information necessary for understanding the Zip file (and its files) and the CSV.</p> <p>The CSV file contains the dataset created and used for my MPhil thesis.</p> <p>The project was carried out within the DataARC Project as an MPhil thesis. The thesis explores the creation of (archaeo-historical) knowledge using data within a general framework of Big Data. Human Ecodynamics is the <em>paradigm </em>which guides the DataARC Project. My project explores how to link grey data from archaeological reports into a general computational ontology developed for representing Human Ecodynamics by making use of multiple datasets.</p>
Figure 2 from: Tian J, Kathambi V, Kamau P, Mwachala G, Malombe I, Hu G-W (2019) The first report of Nervilia lilacea Jum. & H. Perrier (Orchidaceae, Epidendroideae) from Kenya and the Northern Hemisphere. PhytoKeys 135: 355-38. https://doi.org/10.3897/phytokeys.135.46629
Figure 2 Distribution map of Nervilia lilacea, with the new collection FOKP-1530 shown by a red star [other points re-drawn after Petersson (1990, 1991) and specimen records]
Figure 1 from: Tian J, Kathambi V, Kamau P, Mwachala G, Malombe I, Hu G-W (2019) The first report of Nervilia lilacea Jum. & H. Perrier (Orchidaceae, Epidendroideae) from Kenya and the Northern Hemisphere. PhytoKeys 135: 355-38. https://doi.org/10.3897/phytokeys.135.46629
Figure 1 Nervilia lilaceaA plant with flower B plant with leaf (adaxial and abaxial leaf) C flower, frontal view D floral pieces dissected (a dorsal sepal b lateral petal c lateral sepal d lip) E lip F column, lateral and ventral view G anther H pollinia I capsule (Jing Tian drew it from FOKP-1530 specimen).
FMO3 Suppressive Anti-Inflammatory Formula Novelty Report
<p>The WIPO Novelty Report of TR201304603B Patent Number</p> <p>Anti-Inflammatory Formulation currently being co-researched by TUSEB</p> <p>Inventors: Alkoclar , Erdal Can </p> <p> Yesil , Metehan</p>
Figure 2 from: Liao Y-L, Yang B, Xu M-F, Lin W, Wang D-S, Chen K-W, Chen H-Y (2019) First report of Telenomus remus parasitizing Spodoptera frugiperda and its field parasitism in southern China. In: Talamas E (Eds) Advances in the Systematics of Platygastroidea II. Journal of Hymenoptera Research 73: 95-102. https://doi.org/10.3897/jhr.73.39136
Figure 2 Phylogenetic analysis of Telenomus remus and related species by maximum likelihood method based on COI sequences. The six sequences generated from this study are indicated with codes and GenBank accession numbers (see Table 1). Boostrap values above 50 indicated on branches.
ScienceDex guides
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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.
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.