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317 results for “Hierarchy”
Encyclopedia of Life v2: Taxon Hierarchies and Associated Taxon Concepts
<p>This archive contains a snapshot of the taxon hierarchies, and associated scientific name strings and image thumbnails, used by the Encyclopedia of Life v2 (Parr et al. 2014, http://eol.org). See https://github.com/jhpoelen/eol-globi-data/issues/274 and https://github.com/EOL/tramea/issues/366 for discussion threads. Taxon hierarchy providers include, but are not limited to, Integrated Taxonomic Information System (ITIS, http://itis.gov) and World Register of Marine Species (WoRMS, http://marinespecies.org).</p>
Timbral Hierarchy Dataset
<p>This dataset contains data generated as part of the AudioCommons project (DS 5.1.1). Data relate to Deliverable D5.1 which: (i) generated a hierarchy of terms describing the timbral attributes of audio; (ii) determined the search frequency for each of these terms on the www.freesound.org audio database.</p> <p>Data comprise excel and csv files, Python code, figures and documentation.</p> <p>This research made use of data from Deliverable D2.1</p> <p><strong>References</strong></p> <p>AES (2017): A.Pearce, T.Brookes, R.Mason, "Timbral attributes for sound effect library searching", AES International Conference on Semantic Audio, 22-24 June 2017, Erlangen, Germany.<br> - http://www.aes.org/e-lib/browse.cfm?elib=18754</p> <p>D2.1 (2016): M.Barthet, G.Fazekas, D.Juric, J.Pauwels, M.Sandler, L.Vetter, "Deliverable D2.1: Requirements Report and Use Cases", AudioCommons Deliverable Report, June 2016<br> - http://www.audiocommons.org/materials/</p> <p>D5.1 (2016): A.Pearce, T.Brookes, R.Mason, "Deliverable D5.1 Hierarchical ontology of timbral semantic descriptors", AudioCommons Deliverable Report, August 2016 <br> - http://www.audiocommons.org/materials/</p> <p> </p>
A Novel Model Hierarchy Isolates the Limited Effect of Supercooled Liquid Cloud Optics on Infrared Radiation
<p>This dataset contains data used in and resulting from an upcoming paper. For further detail on methodology and experiments, see that paper.</p> <h2>Supercooled liquid water optics</h2> <h3>Complex refractive indices (CRIs)</h3> <ul> <li>Water_DW_300.txt</li> <li>water_RFN_240K.txt</li> <li>water_RFN_253K.txt</li> <li>water_RFN_263K.txt</li> <li>water_RFN_273K.txt</li> </ul> <p>Water_DW_300.txt is sourced from Downing & Williams 1975 (https://doi.org/10.1029/JC080i012p01656). water_RFN_240K.txt, water_RFN_253K.txt, water_RFN_263K.txt, and water_RFN_273K.txt are sourced from Rowe et al. 2020 (https://doi.org/10.1029/2020JD032624).</p> <h3>CESM lookup tables of liquid water optics</h3> <ul> <li>CESM_CRI_RFN_240K.nc</li> <li>CESM_CRI_RFN_253K.nc</li> <li>CESM_CRI_RFN_263K.nc</li> <li>CESM_CRI_RFN_273K.nc</li> </ul> <p>These optics sets were created from the corresponding Rowe et al. 2020 CRI.</p> <p> </p> <h2>SCAM output</h2> <p>History files for the four MPACE SCAM runs.</p> <ul> <li>Control: tutorial.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>240K optics: cri240K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>263K optics: cri263K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>273K optics: cri273K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> </ul> <p> </p> <h2>F1850_UVnudge1980 data</h2> <p>Data used to create graphs shown in PAPER from the F1850_UVnudge1980 experiment. For each optics set there is a mean, count (n), and standard deviation file. These statistics are calculated over the 1 year of the model run and across all 10 ensemble members for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>240K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>273K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>F1850_UVnudge1980-2018 data</h2> <p>Data used to create graphs shown in PAPER from the F1850_UVnudge1980-2018 experiment. For the variable FLDS (downwelling longwave flux at the surface), each optics set has a mean, count (n), and standard deviation file. These statistics are calculated over the 39 years of the model run and across all 3 ensemble members. </p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>B1850_UVnudge1980 data</h2> <p>Data used to create graphs shown in PAPER from the B1850_UVnudge1980 experiment. For each optics set there is a mean, count (n), and standard deviation file. These statistics are calculated over the 1 year of the model run and across all 10 ensemble members for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>F1850 data</h2> <p>Data used to create graphs shown in PAPER from the F1850 experiment. For each optics run there is a mean, count (n), and standard deviation file. These statistics are calculated over the 40 years of the model run for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.avg.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>240K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.avg.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.avg.All_data.non_filtered.nc </li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>273K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.avg.All_data.non_filtered.nc </li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.std.All_data.non_filtered.nc</li> </ul>
Fig. 4 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 4. Distribution of pairwise values of genetic distances amongst species with allopatric areas: 1 — for the Western Palearctic genus Sylvaemus; 2 — for the Eastern Palearctic genera Apodemus and Alsomys; 3 — for the Palearctic Muridae as a whole, including species of genera Micromys and Mus.
Fig. 3 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 3. Distribution of pairwise intraspecies genetic distances within: 1 — the Western Palearctic genus Sylvaemus; 2 — the Eastern Palearctic genera Apodemus and Alsomys; 3 — in general for the Palearctic Muridae, including Micromys and Mus.
Fig. 2 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 2. Phenogram of genetic distances (Tamura, Nei, 1993) calculated from cytb sequences amongst representatives of the genera/subgenera Alsomys, Apodemus and genera Micromys, Mus, Rattus, constructed using the UPGMA algorithm. Representatives of the Arvicolidae and Cricetidae as well as S. s. dichrurus, S. flavicollis, S. (K.) mystacinus and S. (K.) epimelas were taken as outgroups.
Fig. 5 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 5. Distribution of pairwise genetic distances amongst taxa: 1 — Western Palearctic genus Sylvaemus, 2 — Eastern Palearctic genera Apodemus, Alsomys, 3 — Western Palearctic genus Sylvaemus and contrarily Eastern Palearctic genera Apodemus, Alsomys.
Fig. 1 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 1. Phenogram of genetic distances calculated from cytb sequences amongst representatives of the genera Sylvaemus, Rattus, constructed using the UPGMA algorithm, as mentioned above. Microtus arvalis (Arvicolidae) and Cricetus cricetus (Cricetidae) are used as outgroups.
U.S. Appeals and Supreme Court dataset for: Judicial hierarchy and discursive influence
<p><span>This dataset contains written opinions from the 11 numbered Courts of Appeals and the DC Circuit Court of Appeals (not </span><span>including the Federal Circuit), as well as the SCOTUS. It also contains metadata pertaining to each opinion, such as author, year, etc.</span></p> <p><span>It also contains the processed outputs of the rDIM model (Gerow et. al. 2018) pertaining to the experiments performed in </span><span>our paper. These results contain the assigned influence and topic distribution for each case.</span></p>
Dataset accompanying the paper "Multimodal laminar characterization of visual areas along the cortical hierarchy"
<p>This dataset accompanying the manuscript "<strong>Multimodal laminar characterization of visual areas along the cortical hierarchy</strong>". </p> <p>It contains:</p> <ul> <li>final processed data that are used to compute (and plot) laminar profiles. Laminar profiles are shown as main figures in the manuscript.</li> <li>raw resting-state fMRI data + scanning protocol file</li> </ul>
Stem cell hierarchies and developmental origins of rhabdomyosarcoma
<p>Rhabdomyosarcoma (RMS) is the most common soft-tissue sarcoma of childhood and is comprised of two major molecular subtypes. Despite sharing features with skeletal muscle, the conservation of underlying cellular hierarchy with human muscle development and the identification of molecularly-defined tumor-propagating cells have not been reported. Using single-cell RNA sequencing of patient-derived RMS, DNA-barcode cell fate mapping, and antibody enrichment and functional stem cell assays using <em>in vitro</em> culture and mouse xenografts, we have uncovered tumor cell hierarchies in Fusion-negative (FN-) RMS that are shared with normal human muscle development. We also identified common developmental stages at which tumor cells become arrested. FN-RMS resemble early muscle found in embryonic and larval development, while fusion-positive (FP-)RMS express a highly specific developmental gene program found in muscle cells transiting from embryonic to fetal development at 7-7.75 weeks of age. FP-RMS also have neural-pathway enriched cell states, suggesting less-rigid adherence to muscle development hierarchies in this disease. Finally, we identify a molecularly-defined tumor-propagating cell in FN-RMS that shares remarkable similarity to the newly described bi-potent, muscle mesenchyme stem/progenitor cell that makes both muscle and osteogenic cells.</p>
Fig. 1 in Dominance Hierarchy And Status Signalling In Captive Tree Sparrow (Passer Montanus) Flocks
Fig. 1. The relationship between badge size and dominance ranks in flock B (n= 9 birds, for statistics see Table 2). The most subordinate individual has rank 1
Repackaged Encyclopedia of Life (EOL) Dynamic Hierarchy hash://sha256/f91877189f3cd14f4066b16b693a5f93105fd23b881b0825d6781be6ac674b88 hash://md5/18ca6625cf1d24093dc104851ab5722b
<p>This publication contains a repackaged publication of the Encyclopedia of Life Dynamic (Taxon) Hierarchy v2.1 as retrieved on 8 July 2024 via https://editors.eol.org/uploaded_resources/00a/db4/7b-57ed-4f6b-8f66-83bfdb5120e8 .</p> <p>File included in this publication:</p> <p>1. dh21.zip - original file retrieved via https://editors.eol.org/uploaded_resources/00a/db4/7b-57ed-4f6b-8f66-83bfdb5120e8</p> <p>2. taxon.tab.gz - extracted, and gzipped, taxon.tab as extracted from dh21.zip</p> <p>3. taxon-first10.tab - first 10 lines of taxon.tab.gz</p> <p>3. meta.xml - as extracted from dh21.zip</p> <p>4. prov.nq - log documenting the provenance of dh21.zip</p>
Fig. 3 in New Insights into the Male Morphotypes of the Amphidromous Shrimp (Weigmann, 1836) (Caridea: Palaemonidae) and a Discussion on Social Dominance Hierarchies.
Fig. 3. Macrobrachium olfersii (Wiegmann, 1836). Discrimination of juveniles and adult morphotypes (M1, M2, and M3) according to the most explanatory morphometric variables from the principal component analysis, propodus length (PrL), and major cheliped length (ChL).
Fig. 4 in New Insights into the Male Morphotypes of the Amphidromous Shrimp (Weigmann, 1836) (Caridea: Palaemonidae) and a Discussion on Social Dominance Hierarchies.
Fig. 4. Macrobrachium olfersii (Wiegmann, 1836). (A) Regression of the morphometric relationship of the propodus length (PrL) Vs. carapace length (CL) demonstrates the separation between juvenile and adult males. (B) A logistic curve shows the size at which 50% of males reach sexual maturity (CL50).
Fig. 2 in New Insights into the Male Morphotypes of the Amphidromous Shrimp (Weigmann, 1836) (Caridea: Palaemonidae) and a Discussion on Social Dominance Hierarchies.
Fig. 2. Macrobrachium olfersii (Wiegmann, 1836). Principal Component Analysis (PCA) of morphometric variables. Values indicate the projection of components 1 and 2 (PC1 and PC2).
Fig. 1 in New Insights into the Male Morphotypes of the Amphidromous Shrimp (Weigmann, 1836) (Caridea: Palaemonidae) and a Discussion on Social Dominance Hierarchies.
Fig. 1. (A) Carapace of Macrobrachium olfersii (Wiegmann, 1836). Dimension of carapace length (CL) measurements. (B) Major cheliped of Macrobrachium olfersii. Exemplification of the dimensions used to measure the length and height of the articles of the larger cheliped. The same measurements were used for the smaller cheliped. (C) Propodus of the larger cheliped of Macrobrachium olfersii in the standard position used in the geometric morphometric analyses. Red and blue circles are the landmarks and semilandmarks, respectively. CL = Carapace length; IL = Ischium length; ML = Merus length; CaL = Carpus length; PrL = Propodus length; DL = Dactylus length; PrH = Propodus height.
Fig. 5 in New Insights into the Male Morphotypes of the Amphidromous Shrimp (Weigmann, 1836) (Caridea: Palaemonidae) and a Discussion on Social Dominance Hierarchies.
Fig. 5. Macrobrachium olfersii (Wiegmann, 1836). Specimens and chelipeds of the male morphotypes, (A and B) Juvenile, (C and D) Morphotype 1, (E and F) Morphotype 2, and (G and H) Morphotype 3. All scale bars correspond to 10 mm, except for the scale bar present in 6B, which corresponds to 5 mm.
Fig. 6 in New Insights into the Male Morphotypes of the Amphidromous Shrimp (Weigmann, 1836) (Caridea: Palaemonidae) and a Discussion on Social Dominance Hierarchies.
Fig. 6. Macrobrachium olfersii (Wiegmann, 1836). (A) Scatter plot of canonical variation analysis (CVA) performed with the coordinates of variation in the propodus shape of the male morphotypes. (B) Variation in the propodus shape of each male morphotype. M3 presents evident differences in the shape of the palm region and also in the fixed finger in relation to the other morphotypes.
New Ideas for Brain Modelling 4-Figure 4. LHS relates to neuron binding ensemble mass, with central column activated. RHS relates to hierarchy, with a direct mapping. The two red lines show where the ensemble is missing and so it needs to be learned. The blue lines show extra neurons from the hierarchy back to the ensemble, but can be removed as error. The other paired black squares represent where the patterns match and can oscillate together.
<p>This paper continues the research that considers a new cognitive model based strongly on the human brain, last updated in Greer (2016). In particular, it considers figure 4 of that paper (Figure below) and how it might be useful in practice. The paper also describes some new methods in the areas of image processing and behaviour simulation. The image processing introduces a most classical form of pattern cross-referencing, while the behaviour equations used feedback for a memory-type of cross-referencing. The work is all based on earlier research by the author and the new additions are intended to fit in with the overall design. For image processing, a grid-like structure is used with ‘full linking’, if you like. Each cell in the classifier grid stores a list of all other cells it gets associated with and this is used as the learned image that new input is compared with. For the behaviour metric, a new prediction equation is suggested, as part of a simulation, that uses feedback and history to dynamically determine its current state and course of action. While the new methods are from widely different topics, both can be compared with the binary-analog type of interface that is the main focus of the paper. Sensory input may be static and binary, but cross- references result in variable comparisons that make the input more dynamic. It is suggested that the simplest of linking between a tree and ensemble can explain neural binding and variable signal strengths.</p>
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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.