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Fig. 2 in Identification of distinct evolutionary units in allopatric populations of Hypostomus cf. wuchereri Günther, 1864 (Siluriformes: Loricariidae): karyotypic evidence
Fig. 2. Chromosomal pairs of Hypostomus cf. wuchereri from Mutum River showing the C-bands and the digestion profiles using Alu I, Hae III, Dde I, and Bam HI.
Fig. 4. DAPI and CMA 3 in Identification of distinct evolutionary units in allopatric populations of Hypostomus cf. wuchereri Günther, 1864 (Siluriformes: Loricariidae): karyotypic evidence
Fig. 4. DAPI and CMA 3 stained chromosomal pairs from Mutum (a) and Una (b) Rivers, showing AT and GC-rich sites, respectively. The NOR-bearing pair is highlighted.
Absolute units in Arabidopsis clock models up to U2020.3
<p>The dataset presents mathematical models of the gene regulatory network of the circadian clock, in the plant <em>Arabidopsis thaliana.</em> The work is published in Urquiza-Garcia and Millar, <strong>Testing the inferred transcription rates of a dynamic, gene network model in absolute units</strong>, In Silico Plants, 2021.</p> <p>Starting from the P2011 model, this project corrects theoretical issues (EC steady state binding assumption) to form an intermediate model (published as U2019.1), rescales parameters to match absolute RNA levels from the TiMet WP1a RNA dataset of Flis et al. 2015 in U2019.2, then reoptimises globally to match RNA timeseries, period and amplitude constraints to produce the U2019.3 model.</p> <p>This project then replaces regulation of the PRRs with repression by later-expressed genes (instead of activation by earlier ones, as in P2011/P2012/U2019) in the U2019.1 model, to create the U2020.1 model, then also rescales to match the RNA levels in the U2020.2 model, and performs a global reoptimisation as above to form U2020.3.</p> <p>The input data files, the computational environment for model development (a Docker image in the 'Reproducibility Toolset' assay) and the relevant model files are included here, as published in Urquiza and Millar, In Silico Plants, 2021. The biorXiv preprint version of the publication is linked inside the archive structure. The DOI for the publication was not yet available but is now: 10.1093/insilicoplants/diab022.</p> <p>This version 3 replaces 10.5281/zenodo.5150823 (version 2), as model files for the earlier KF2014 models were omitted in error from versions 1 and 2. There are no other changes.</p>
Sensor data set of one electromechanical cylinder at ZeMA testbed (ZeMA DAQ and Smart-Up Unit)
<p><strong>General information on the data set</strong></p> <p>The dataset was generated with two different measurement systems at the ZeMA testbed for electromechanical cylinders.</p> <p> </p> <p><strong>All relevant information can be found within the hdf5 file.</strong></p> <p> </p> <p><strong>Example for reading out the metadata of the hdf5 file in MATLAB:</strong></p> <pre><code># available structures inside file dataset = 'axis11_2kHz_ZeMA_PTB_SI.h5'; h5disp(dataset) % general attributes about file attr = h5info(dataset).Attributes; project = jsondecode(attr(1,1).Value) person = jsondecode(attr(2,1).Value) publication = jsondecode(attr(3,1).Value) experiment = jsondecode(attr(4,1).Value)</code></pre> <p> </p> <p><strong>Example for reading out the metadata of the hdf5 file in Python:</strong></p> <pre><code>import h5py import json # open file h5file = h5py.File("axis11_2kHz_ZeMA_PTB_SI.h5", "r") # general attributes about file for key in h5file.attrs: print(key) val = json.loads(h5file.attrs[key]) for subkey, subval in val.items(): print(" ", subkey, " : ", subval) # available structures inside file h5file.visit(print) # proper exit h5file.close()</code></pre> <p> </p> <p><strong>Metadata output of the hdf5 file:</strong></p> <ul> <li><strong>For the dataset:</strong> <pre><code>HDF5 axis11_2kHz_ZeMA_PTB_SI.h5 Group '/' Attributes: 'Project': '{ "fullTitle":"Metrology for the Factory of the Future", "acronym":"Met4FoF", "websiteLink":"www.met4fof.eu", "fundingSource":"European Commission (EC)", "fundingAdministrator":"EURAMET", "funding programme":"EMPIR", "fundingNumber":"17IND12", "acknowledgementText":"This work has received funding within the project 17IND12 Met4FoF from the EMPIR program co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation program. The authors want to thank Clifford Brown, Daniel Hutzschenreuter, Holger Israel, Giacomo Lanza, Bj\u00f6rn Ludwig, and Julia Neumann fromPhysikalisch-Technische Bundesanstalt (PTB) for their helpful suggestions and support." }' 'Person': '{ "dc:author":[ "Tanja Dorst", "Maximilian Gruber", "Anupam Prasad Vedurmudi" ], "e-mail":[ "t.dorst@zema.de", "maximilian.gruber@ptb.de", "anupam.vedurmudi@ptb.de" ], "affiliation":[ "ZeMA gGmbH", "Physikalisch-Technische Bundesanstalt", "Physikalisch-Technische Bundesanstalt" ] }' 'Publication': '{ "dc:identifier":"10.5281/zenodo.5185953", "dc:license":"Creative Commons Attribution 4.0 International (CC-BY-4.0)", "dc:title":"Sensor data set of one electromechanical cylinder at ZeMA testbed (ZeMA DAQ and Smart-Up Unit)", "dc:description":"The data set was generated with two different measurement systems at the ZeMA testbed. The ZeMA DAQ unit consists of 11 sensors and the SmartUp-Unit has 13 differentsignals. A typical working cycle lasts 2.8s and consists of a forward stroke, a waiting time and a return stroke of the electromechanical cylinder. The data set does not consist of the entire working cycles. Only one second of the return stroke of every 100rd working cycle is included. The dataset consists of 4776 cycles. One row represents one second of the return stroke of one working cycle.", "dc:subject":[ "dynamic measurement", "measurement uncertainty", "sensor network", "digital sensors", "MEMS", "machine learning", "European Union (EU)", "Horizon 2020", "EMPIR" ], "dc:SizeOrDuration":"24 sensors, 4776 cycles and 2000 datapoints each", "dc:type":"Dataset", "dc:issued":"2021-09-10", "dc:bibliographicCitation":"T. Dorst, M. Gruber and A. P. Vedurmudi : Sensor data set of one electromechanical cylinder at ZeMA testbed (ZeMA DAQ and Smart-Up Unit), Zenodo [data set], https://doi.org/10.5281/zenodo.5185953, 2021." }' 'Experiment': '{ "date":"2021-03-29/2021-04-15", "DUT":"Festo ESBF cylinder", "identifier":"axis11", "label":"Electromechanical cylinder no. 11" }'</code></pre> <p> </p> </li> <li><strong>Example for one sensor (BMA 280, acceleration) of the PTB SmartUp Unit (SUU) and one sensor of ZeMA DAQ (pressure):</strong> <pre><code>HDF5 axis11_2kHz_ZeMA_PTB_SI.h5 Group '/PTB_SUU' Group '/PTB_SUU/BMA_280' Group '/PTB_SUU/BMA_280/Acceleration' Attributes: 'qudt:hasQuantityKind': '[ "qudt:Acceleration", "qudt:Acceleration", "qudt:Acceleration" ]' 'misc': '{ "interpolation_scheme":"cubic" }' 'si:unit': '"\\metre\\second\\tothe{-2}"' 'sosa:madeBySensor': '"BMA 280"' 'rdf:type': '"qudt:Quantity"' Dataset 'qudt:standardUncertainty' Size: 4766x1000x3 MaxSize: 4766x1000x3 Datatype: H5T_IEEE_F64LE (double) ChunkSize: [] Filters: none FillValue: 0.000000 Attributes: 'si:label': '[ "X acceleration uncertainty", "Y acceleration uncertainty", "Z acceleration uncertainty" ]' Dataset 'qudt:value' Size: 4766x1000x3 MaxSize: 4766x1000x3 Datatype: H5T_IEEE_F64LE (double) ChunkSize: [] Filters: none FillValue: 0.000000 Attributes: 'si:label': '[ "X acceleration", "Y acceleration", "Z acceleration" ]' Group '/ZeMA_DAQ' Group '/ZeMA_DAQ/Pressure' Attributes: 'qudt:hasQuantityKind': '"qudt:Pressure"' 'sosa:madeBySensor': '"Festo VPPM"' 'si:unit': '"\\pascal"' 'rdf:type': '"qudt:Quantity"' Dataset 'qudt:standardUncertainty' Size: 4766x2000 MaxSize: 4766x2000 Datatype: H5T_IEEE_F64LE (double) ChunkSize: [] Filters: none FillValue: 0.000000 Attributes: 'si:label': '"Pneumatic pressure uncertainty"' Dataset 'qudt:value' Size: 4766x2000 MaxSize: 4766x2000 Datatype: H5T_IEEE_F64LE (double) ChunkSize: [] Filters: none FillValue: 0.000000 Attributes: 'si:label': '"Pneumatic pressure"' 'misc': '{ "raw_data":false, "comment":"Converted from ADC values based on appropriate conversion." }'</code></pre> </li> </ul>
Fig. 62 in Review of the braconid parasitoid subfamily Doryctinae (Hymenoptera, Braconidae) from the United Arab Emirates and Yemen
Fig. 62. Rhaconotus (Rh.) vanharteni Belokobylskij sp. nov., holotype, ♀ (RMNH). A. Fore wing. B. Hind wing. C. Metasoma, dorsal view. D. Metasoma, lateral view.
Fig. 60 in Review of the braconid parasitoid subfamily Doryctinae (Hymenoptera, Braconidae) from the United Arab Emirates and Yemen
Fig. 60. Rhaconotus (Rh.) sudanensis Wilkinson, 1927, ♀ (RMNH). A. Wings. B. Metasoma, dorsal view. C. Metasoma, lateral view.
Fig. 63 in Review of the braconid parasitoid subfamily Doryctinae (Hymenoptera, Braconidae) from the United Arab Emirates and Yemen
Fig. 63. Aivalykus microaciculatus Ranjith & Belokobylskij, 2020, paratypes. A, C-J. ♀ (RMNH). B. ♂ (RMNH). A–B. Habitus, lateral view. C. Basal segments of antenna. D. Antennal segments of antenna. E. Head and mesoscutum, dorsal view. F. Head and mesosoma, lateral view. G. Head, front view. H. Mesosoma and first metasomal tergite, dorsal view.. I. Hind leg. J. Propodeum and first metasomal tergite, dorsal view.
Fig. 57 in Review of the braconid parasitoid subfamily Doryctinae (Hymenoptera, Braconidae) from the United Arab Emirates and Yemen
Fig. 57. Rhaconotus (Rh.) scirpophagae Wilkinson, 1927, ♀ (RMNH). A. Habitus, dorsal view. B. Head, dorsal view. C. Head, front view. D. Head and basal segments of antenna, lateral view. E. Hind coxa and femur. F. Hind tibia. G. Mesosoma, dorsal view. H. Mesosoma, lateral view. I. Mesoscutum and scutellum, dorsal view.
Fig. 56 in Review of the braconid parasitoid subfamily Doryctinae (Hymenoptera, Braconidae) from the United Arab Emirates and Yemen
Fig. 56. Rhaconotus (Rh.) microexcavatus Belokobylskij sp. nov., holotype, ♀ (RMNH). A. Wings. B. Metasoma, dorsal view. C. Metasoma, lateral view.
Fig. 53 in Review of the braconid parasitoid subfamily Doryctinae (Hymenoptera, Braconidae) from the United Arab Emirates and Yemen
Fig. 53. Rhaconotus (Rh.) manolus Nixon, 1941, ♀ (RMNH). A. Habitus, lateral view. B. Head and mesoscutum, dorsal view. C. Head, front view. D. Apical segments of antenna. E. Basal segments of antenna. F. Head and mesosoma, lateral view. G. Mesosoma, dorsal view. H. Hind leg.
Fig. 50 in Review of the braconid parasitoid subfamily Doryctinae (Hymenoptera, Braconidae) from the United Arab Emirates and Yemen
Fig. 50. Rhaconotus (Rh.) brevicellularis Belokobylskij sp. nov., holotype, ♀ (RMNH). A. Wings. B. Metasoma, dorsal view. C. Metasoma, lateral view.
Fig. 52 in Review of the braconid parasitoid subfamily Doryctinae (Hymenoptera, Braconidae) from the United Arab Emirates and Yemen
Fig. 52. Rhaconotus (Rh.) magniareolus Belokobylskij sp. nov., holotype, ♀ (RMNH). A. Wings. B. Metasoma, dorsal view. C. Metasoma, lateral view.
Fig. 46 in Review of the braconid parasitoid subfamily Doryctinae (Hymenoptera, Braconidae) from the United Arab Emirates and Yemen
Fig. 46. Rhaconotinus (Rh.) menippus (Nixon, 1939), ♀ (RMNH). A. Wings. B. Propodeum and metasoma, dorsal view. C. Metasoma, lateral view.
Fig. 44 in Review of the braconid parasitoid subfamily Doryctinae (Hymenoptera, Braconidae) from the United Arab Emirates and Yemen
Fig. 44. Rhaconotinus (Rh.) albosetosus Belokobylskij sp. nov., holotype, ♀ (RMNH). A. Wings. B. Metasoma, dorsal view. C. Metasoma, lateral view.
Fig. 43 in Review of the braconid parasitoid subfamily Doryctinae (Hymenoptera, Braconidae) from the United Arab Emirates and Yemen
Fig. 43. Rhaconotinus (Rh.) albosetosus Belokobylskij sp. nov., holotype, ♀ (RMNH). A. Habitus, lateral view. B. Head, front view. C. Head, dorsal view. D. Head, lateral view. E. Basal segments of antenna. F. Apical segments of antenna. G. Hind leg. H. Mesosoma, dorsal view. I. Mesosoma, lateral view. J. Propodeum. K. Six metasomal tergite, dorsal view. L. First to third metasomal tergites, dorsal view.
Fig. 39 in Review of the braconid parasitoid subfamily Doryctinae (Hymenoptera, Braconidae) from the United Arab Emirates and Yemen
Fig. 39. Platyspathius (P.) brevis Belokobylskij sp. nov., holotype, ♀ (RMNH). A. Habitus, lateral view. B. Head, front view. C. Head, dorsal view. D. Head, lateral view. E. Apical segments of antenna. F. Basal segments of antenna. G. Hind leg. H. Mesosoma, dorsal view. I. Mesosoma, lateral view. J. First metasomal tergite, dorsal view. K. First metasomal tergite, lateral view. L. Second and third metasomal tergites, dorsal view.
Fig. 42 in Review of the braconid parasitoid subfamily Doryctinae (Hymenoptera, Braconidae) from the United Arab Emirates and Yemen
Fig. 42. Platyspathius (P.) longicaudis Belokobylskij sp. nov., holotype, ♀ (RMNH). A. Wings. B. Metasoma, dorsal view. C. Metasoma, lateral view.
Fig. 32. Hecabalodes xylophagi Fischer, 1962 in Review of the braconid parasitoid subfamily Doryctinae (Hymenoptera, Braconidae) from the United Arab Emirates and Yemen
Fig. 32. Hecabalodes xylophagi Fischer, 1962, ♀ (RMNH). A. Habitus, lateral view. B. Head and mesoscutum, dorsal view. C. Head, front view. D. Basal segments of antenna. E. Apical segments of antenna. F. Head and mesosoma, lateral view. G. Mesosoma, dorsal view. H. Hind leg.
Fig. 28 in Review of the braconid parasitoid subfamily Doryctinae (Hymenoptera, Braconidae) from the United Arab Emirates and Yemen
Fig. 28. Hecabalodes maculatus Belokobylskij sp. nov., holotype, ♀ (RMNH). A. Habitus, lateral view. B. Head and anterior part of mesosoma, lateral view. C. Head, front view. D. Head, dorsal view. E. Basal segments of antenna. F. Apical segments of antenna. G. Mesosoma, dorsal view. H. Head and mesosoma, lateral view. I. Hind leg.
Fig. 35 in Review of the braconid parasitoid subfamily Doryctinae (Hymenoptera, Braconidae) from the United Arab Emirates and Yemen
Fig. 35. Parallorhogas testaceus (Szépligeti, 1914). A, C. Holotype, ♀ (MNBG). B. Arabian specimen (RMNH). A. Fore wing. B. Wings. C. Metasoma, dorsal view.
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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.