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10,767 results for “Contribution”
Figs 102-104 in A New Contribution to the Knowledge of the East Palaearctic Ichneumoninae (Hymenoptera, Ichneumonidae)
Figs 102-104: Virgichneumon paramonostagon nov.sp. ♀: (102) face from frontal, (103) propodeum from dorsal, (104) basal tergites from dorsal.
Figs 73-76 in A New Contribution to the Knowledge of the East Palaearctic Ichneumoninae (Hymenoptera, Ichneumonidae)
Figs 73-76: Basal tergites from dorsal of ♀♀: (73) Ichneumon nigroflagellator nov.sp., (74) I. nigrostriatus nov.sp., (75) I. nudicoxator nov.sp., (76) I. oehlkei nov.sp.
Figs 87-90 in A New Contribution to the Knowledge of the East Palaearctic Ichneumoninae (Hymenoptera, Ichneumonidae)
Figs 87-90: Paracoelichneumon mongolicus nov.sp. ♀: (87) face from frontal, (88) head from dorsal, (89) propodeum from dorsal, (90) basal tergites from dorsal.
Figs 61-64 in A New Contribution to the Knowledge of the East Palaearctic Ichneumoninae (Hymenoptera, Ichneumonidae)
Figs 61-64: Propodeum from dorsal of ♀♀: (61) Ichneumon nigroflagellator nov.sp., (62) I.nigrostriatus nov.sp., (63) I. nudicoxator nov.sp., (64) I. oehlkei nov.sp.
Figs 81-82 in A New Contribution to the Knowledge of the East Palaearctic Ichneumoninae (Hymenoptera, Ichneumonidae)
Figs 81-82: Face from frontal of ♀♀: (81) Monontos kasparyani nov.sp., (82) Rhadinodontoides rufiventris nov.sp. Figs 83-86: Rhadinodontoides rufiventris nov.sp. ♀: (83) head from dorsal, (84) pectinate hind claw, (85) propodeum from dorsal, (86) basal tergites from dorsal.
Figs 43-46 in A New Contribution to the Knowledge of the East Palaearctic Ichneumoninae (Hymenoptera, Ichneumonidae)
Figs 43-46: Hoplismenus brevigena nov.sp. ♀: (43) face from frontal, (44) face from lateral, (45) propodeum from dorsal, (46) basal tergites from dorsal.
Figs 55-56 in A New Contribution to the Knowledge of the East Palaearctic Ichneumoninae (Hymenoptera, Ichneumonidae)
Figs 55-56: Faces from frontal of ♀♀: (55) Ichneumon melanator nov.sp., (56) I. rufofacialis nov.sp. Figs 57-60: Propodeum from dorsal of ♀♀: (57) Ichneumon haladai nov.sp., (58) I. melanator nov.sp., (59) I. melanosomops nov.sp., (60) I. nigroantennator nov.sp.
Figs 34-35 in A New Contribution to the Knowledge of the East Palaearctic Ichneumoninae (Hymenoptera, Ichneumonidae)
Figs 34-35: Barichneumon nigrithorax nov.sp. ♀: (34) propodeum from dorsal, (35) basal tergites from dorsal. Figs 36-39: Amblysmenus nigriventris nov.sp. ♀: (36) face from frontal, (37) head from dorsal, (38) propodeum from dorsal, (39) basal tergites from dorsal.
Figs 27-31 in A New Contribution to the Knowledge of the East Palaearctic Ichneumoninae (Hymenoptera, Ichneumonidae)
Figs 27-31: Barichneumonops membraneus nov.sp. ♀: (27) face from frontal, (28) head from dorsal, (29) propodeum from dorsal, (30) basal tergites from dorsal, (31) metasoma apex from dorsal. Figs 32-33: Hind coxa from lateral: (32) Barichneumon nigrithorax nov.sp. ♀, (33) Ichneumon nudicoxator nov.sp. ♀.
Figs 40-42 in A New Contribution to the Knowledge of the East Palaearctic Ichneumoninae (Hymenoptera, Ichneumonidae)
Figs 40-42: Coelichneumon sugillatops nov.sp. ♀: (40) face from frontal, (41) propodeum from dorsal, (42) basal tergites from dorsal.
Figs 19-22 in A New Contribution to the Knowledge of the East Palaearctic Ichneumoninae (Hymenoptera, Ichneumonidae)
Figs 19-22: Habitus from dorsal: (19) Ichneumon melanosomops nov.sp. ♀, (20) Ichneumon nigroantennator nov.sp. ♀, (21) Ichneumon nigrostriatus nov.sp. ♀, (22) Ichneumon paraltaicola nov.sp. ♀.
Figs 98-101 in A New Contribution to the Knowledge of the East Palaearctic Ichneumoninae (Hymenoptera, Ichneumonidae)
Figs 98-101: Triptognathus pakistanicus nov.sp. ♀: (98) face from frontal, (99) head from dorsal, (100) propodeum from dorsal, (101) basal tergites from dorsal.
Figs 14-18 in A New Contribution to the Knowledge of the East Palaearctic Ichneumoninae (Hymenoptera, Ichneumonidae)
Figs 14-18: Habitus from dorsal: (14) Barichneumon nigrithorax nov.sp. ♀, (15) Coelichneumon sugillatops nov.sp. ♀, (16) Hoplismenus brevigena nov.sp. ♁, (17) Ichneumon haladai nov.sp. ♀, (18) Ichneumon melanator nov.sp. ♀.
Figs 65-68 in A New Contribution to the Knowledge of the East Palaearctic Ichneumoninae (Hymenoptera, Ichneumonidae)
Figs 65-68: Propodeum from dorsal of ♀♀: (65) Ichneumon paraltaicola nov.sp., (66) I. rufofacialis nov.sp., (67) I. rufomesonator nov.sp., (68) Monontos kasparyani nov.sp.
Figs 77-80 in A New Contribution to the Knowledge of the East Palaearctic Ichneumoninae (Hymenoptera, Ichneumonidae)
Figs 77-80: Basal tergites from dorsal of ♀♀: (77) Ichneumon paraltaicola nov.sp., (78) I. rufofacialis nov.sp., (79) I. rufomesonator nov.sp., (80) Monontos kasparyani nov.sp.
Figs 1-4 in A New Contribution to the Knowledge of the East Palaearctic Ichneumoninae (Hymenoptera, Ichneumonidae)
Figs 1-4: Apaeleticus nigrithorax nov.sp. ♁: (1) habitus from lateral, (2) basal tergites from dorsal, (3) head from frontal, (4) head from dorsal. Figs 5-6: Anisobas mongolicus nov.sp. ♀: (5) face from frontal, (6) propodeum from dorsal.
FIGURES 30 – 37. Scirtes seychellensis Champion. 30 in Contribution to the knowledge of Seychellois Scirtidae (Coleoptera: Scirtoidea)
FIGURES 30 – 37. Scirtes seychellensis Champion. 30) habitus; 31) lectotype labels; 32) male genitalia; 33) penis; 34) tegmen; 35) sternite 9; 36) tergite 8; 37) tergite 9. Scale bar = 0.2 mm (for Figs. 33 – 37).
Scholix Report: Who Contributes to Scholix?
<p>This dataset contains the analysis of Scholix JSON Dump (DOI: <a href="https://doi.org/10.5281/zenodo.1200253">10.5281/zenodo.1200253</a>). The aim is to discover which journals contributing to Scholix Hubs. The scope of this analysis is limited to:</p> <ul> <li>OpenAIRE ScholeXplorer Service: Scholix JSON Dump (March 16, 2018) [DOI: <a href="https://doi.org/10.5281/zenodo.1200253">10.5281/zenodo.1200253</a>]</li> <li>Only included DOI from Crossref and excluded DataCite DOIs.</li> </ul> <p>This dataset includes three files:</p> <ol> <li>scholix-crossref-doi-metadata.tar.gz: Complete Crossref metadata for DOIs in Scholix JSON Dump resolved using https://api.crossref.org/works/{doi}</li> <li>scholix-crossref-doi-prefixes.csv: List of DOI prefixes extracted from scholix-crossref-doi-metadata.json</li> <li>scholix-journals-report.csv: Aggregated record from the number of unique DOIs per journal. This analytics is also derived from scholix-crossref-doi-metadata.json</li> </ol>
Data underpinning "Coupling between cerebral blood flow and cerebral blood volume: Contributions of different vascular compartments"
<p>The data in this archive was acquired to investigate the coupling between cerebral blood flow and cerebral blood volume across different vascular compartments. These results will form the basis of a forthcoming publication. Please reference this dataset (version v1.1.0) if you use it in your work. Wesolowski R, Blockley NP, Driver ID, Francis ST, Gowland PA. Data underpinning "Coupling between cerebral blood flow and cerebral blood volume: Contributions of different vascular compartments". Zenodo 2018. doi: 10.5281/zenodo.1411018. </p> <p>This dataset contains measurements of the haemodynamic responses of different compartments to a visual stimulus (8Hz red LED goggles, 19.2s ON, 40.8s OFF). Time course data are cycle averaged for the following haemodynamic properties;</p> <ol> <li>Arterial cerebral blood volume (CBVa) measured using Look-Locker Flow-sensitive Alternating Inversion Recovery (LL-FAIR) sensitised to CBVa.</li> <li>Cerebral blood flow (CBF) measured using Look-Locker Flow-sensitive Alternating Inversion Recovery (LL-FAIR) sensitised to CBF.</li> <li>Total cerebral blood volume (CBVtot) measured using bolus injections of a Gadolinium based contrast agent combined with T2* weighed gradient echo EPI.</li> </ol> <p>In addition, weighted mean and standard deviation of the changes in these parameters are presented using time windows of 9.6–19.2s and 40.8–60s for ON and OFF, respectively. Weighting is performed with respect to the number of voxels present in each of the subjects regions of interest.</p> <p>Time course data were extracted from two different regions of interest;</p> <ol> <li>ROI<sub>CBF</sub>: defined using a CBF localiser</li> <li>ROI<sub>COMMON</sub>: defined using the overlap of CBF, CBV<sub>a</sub> and CBV<sub>tot</sub> localisers</li> </ol> <p>Furthermore, the transit times for CBF and CBVa were estimated for an average stimulus cycle and mean values extracted using time windows of 9.6–19.2s and 40.8–60s for ON and OFF, respectively. These data were extracted from ROI<sub>CBF</sub>.</p> <p>Changes</p> <p>- Addition of propagation of uncertainty for CBVv and the Grubb constants alpha_tot and alpha_a.</p> <p> </p>
Group contribution models for heat of formation (SUB 2018)
<p>We present a set of group contribution models for predicting heat of formation of organic compounds. A dataset containing 1004 molecular structures from DIPPR database was split into a learning and a test sets further used for model training and validation. The model building was performed with software Ambit-GCM (<a href="https://doi.org/10.5281/zenodo.1470793">https://doi.org/10.5281/zenodo.1470793</a>). A set of preliminary models were build according to various fragmentation schemes, with and without use of correction factors and external descriptors. Different orders of additive schemes were studied. Every model in the set was validated using leave-one-out procedure and Y-scrambling technique as well as model performances were tested using the external dataset. The best five models full data and corresponding statistical characteristics are available in <a href="https://zenodo.org/api/files/e0160501-0f43-4617-b0d8-eb97f717f60a/models.zip?versionId=e5725c6a-857a-499f-8a97-7109ca66a5df">models.zip</a>. The model 2 is available also as a JSON file in the archive and can be used for theoretical prediction of heat of formation.</p> <p>To use the model 2 please download gcm-predict.jar from <a href="https://doi.org/10.5281/zenodo.1470793">https://doi.org/10.5281/zenodo.1470793</a>). Example application of gcm-predict (Ambit-GCM) module for a single structure is given below:</p> <p>java -jar gcm-predict-v1.1.jar -s CC(C)OCC(C)O -c model_2.json<br> GCM value (Hf) for CC(C)OCC(C)O is -528.7163407123614</p> <p>The gcm-predict (Ambit-GCM) module can also be applied for a set of structures. An example follows with 5 molecules inputted as a *.csv file:<br> <br> java -jar gcm-predict-v1.1.jar -i Prediction_Examples.csv -c model_2.json<br> GCM calculateting property Hf for 5 molecules ...<br> Mol#,ModelValue(Hf),SMILES,CalcStatus<br> 1,-126.23055043388635,CCCC,OK<br> 2,-353.4883670381994,c1ccc(c(c1)O)O,OK<br> 3,-524.1758445616103,CCC(CO)O,OK<br> 4,-220.91676720730212,CC(Cc1ccccc1)O,OK<br> 5,-728.1309488149211,C(C(Cl)(Cl)F)(F)F,OK</p> <p>The output lines contain: molecule number, predicted Hf, SMILES, and calculation status</p> <p>The full data for training and validation is available in <a href="https://zenodo.org/api/files/e0160501-0f43-4617-b0d8-eb97f717f60a/learn-test-sets.zip?versionId=7fae2b74-2794-4f96-a76a-93008f0f0f88">learn-test-sets.zip. </a>Data can be used for retraining or improving the models.</p> <p>More examples for using Ambit-GCM software for group contribution modeling and property prediction are available in <a href="https://doi.org/10.5281/zenodo.1471646">https://doi.org/10.5281/zenodo.1471646</a></p>
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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.