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317 results for “Hierarchy”

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dryad32/100

Exploiting hierarchy in medical concept embedding

Objective To construct and publicly release a set of medical concept embeddings for codes following the ICD-10 coding standard which explicitly incorporate hierarchical information from medical codes into the embedding formulation. Materials and Methods We trained concept embeddings using several new extensions to the Word2Vec algorithm using a dataset of approximately 600,000 patients from a major integrated healthcare organization in the Mid-Atlantic US. Our concept embeddings included additional entities to account for the medical categories assigned to codes by the Clinical Classification Software Revised (CCSR) dataset. We compare these results to sets of publicly-released pretrained embeddings and alternative training methodologies. Results We found that Word2Vec models which included hierarchical data outperformed ordinary Word2Vec alternatives on tasks which compared naïve clusters to canonical ones provided by CCSR. Our Skip-Gram model with both codes and categories achieved 61.4% Normalized Mutual Information with canonical labels in comparison to 57.5% with traditional Skip-Gram. In models operating on two different outcomes we found that including hierarchical embedding data improved classification performance 96.2% of the time. When controlling for all other variables, we found that co-training embeddings improved classification performance 66.7% of the time. We found that all models outperformed our competitive benchmarks. Discussion We found significant evidence that our proposed algorithms can express the hierarchical structure of medical codes more fully than ordinary Word2Vec models, and that this improvement carries forward into classification tasks. As part of this publication, we have released several sets of pretrained medical concept embeddings using the ICD-10 standard which significantly outperform other well-known pretrained vectors on our tested outcomes.

opencc-zeroFeb 2022View details →

dryad32/100

Innate preference hierarchies coupled with adult experience, rather than larval imprinting or transgenerational acclimation, determine host plant use in Pieris rapae

The evolution of host range drives diversification in phytophagous insects, and understanding the female oviposition choices is pivotal for understanding host specialization. One controversial mechanism for female host choice is Hopkins' host selection principle, where females are predicted to increase their preference for the host species they were feeding upon as larvae. A recent hypothesis posits that such larval imprinting is especially adaptive in combination with anticipatory transgenerational acclimation, so that females both allocate and adapt their offspring to their future host. We study the butterfly Pieris rapae, for which previous evidence suggests that females prefer to oviposit on host individuals of similar nitrogen content as the plant they were feeding upon as larvae, and where the offspring show higher performance on the mother's host type. We test the hypothesis that larval experience and anticipatory transgenerational effects influence female host plant acceptance (no-choice) and preference (choice) of two host plant species (Barbarea vulgaris and Berteroa incana) of varying nitrogen content. We then test the offspring performance on these hosts. We found no evidence of larval imprinting affecting female decision-making during oviposition, but that an adult female experience of egg laying in no-choice trials on the less-preferred host Be. incana slightly increased the P. rapae propensity to oviposit on Be. incana in subsequent choice trials. We found no transgenerational effects on female host acceptance or preference, but negative transgenerational effects on larval performance, because the offspring of P. rapae females that had developed on Be. incana as larvae grew slower on both hosts, and especially on Be. incana. Our results suggest that among host-species preferences are guided by hard-wired preference hierarchies linked to species-specific host traits and less affected by larval experience or transgenerational effects, which may be more important for females evaluating different host individuals of the same species.

opencc-zeroOct 2021View details →

zenodo32/100

Dataset of "A hierarchy of global ocean models coupled to CESM1"

Data associated with the following publication: Hsu, T. Y., Primeau, F. W., & Magnusdottir, G. (2022). A Hierarchy of Global Ocean Models Coupled to CESM1. Paper Abstract: We develop a hierarchy of simplified ocean models for coupled ocean, atmosphere, and sea ice climate simulations using the Community Earth System Model version 1 (CESM1). The hierarchy has four members: a slab ocean model, a mixed-layer model with entrainment and detrainment, an Ekman mixed-layer model, and an ocean general circulation model (OGCM). Flux corrections of heat and salt are applied to the simplified models ensuring that all hierarchy members have the same climatology. We diagnose the needed flux corrections from auxiliary simulations in which we restore the temperature and salinity to the daily climatology obtained from a target CESM1 simulation. The resulting 3-dimensional corrections contain the interannual variability fluxes that maintain the correct vertical gradients of temperature and salinity in the tropics. We find that the inclusion of mixed-layer entrainment and Ekman flow produces sea surface temperature and surface air temperature fields whose means and variances are progressively more similar to those produced by the target CESM1 simulation. We illustrate the application of the hierarchy to the problem of understanding the response of the climate system to the loss of Arctic sea ice. We find that the shifts in the positions of the mid-latitude westerly jet and of the Inter-tropical Convergence Zone (ITCZ) in response to sea-ice loss depend critically on upper ocean processes. Specifically, heat uptake associated with the mixed-layer entrainment influences the shift in the westerly jet and ITCZ. Moreover, the shift of ITCZ is sensitive to the form of Ekman flow parameterization.   Methods Description of methods used for generation of data:  The data is generated with EMOM, a hierarchy of ocean models that are applied in CESM1. The detailed description of the model is in the paper the dataset is presented in (i.e. A Hierarchy of Global Ocean Models Coupled to CESM1). Methods for processing the data: This dataset consists of a set of atmospheric and oceanic fields produced by the NCAR CESM1 climate model. The data is in NETCDF format and has been post-processed and formatted using the NCO command language (see http://nco.sourceforge.net/ for more details). Software-specific information needed to interpret the data: The data is in NetCDF format. Usage Notes This README file was generated on 20200416 by Tien-Yiao Hsu Dataset of the paper A Hierarchy of Global Ocean Models Coupled to CESM1                      Email: tienyiah@uci.edu       OrcID: 0000-0002-8121-1525     Associate Contact Information       Name: Francois Primeau       Institution: University of California, Irvine       Institutions ROR: [UCI = https://ror.org/04gyf1771]       Address:                   Department of Earth System Science         Croul Hall         Irvine, CA 92697-3100         Email: fprimeau@uci.edu     Associate Contact Information       Name: Gudrun Magnusdottir       Institution: University of California, Irvine       Institutions ROR: [UCI = https://ror.org/04gyf1771]       Address:                   Department of Earth System Science         Croul Hall         Irvine, CA 92697-3100         Email: gudrun@uci.edu   3. Date of data organized : 20220201   4. Information about funding sources that supported the collection of the data:     Funder name: Department of Energy     Funder uri: https://www.energy.gov/   5. Contextual description of the data:          The data used to produce the figures in the paper.   -------------------------- SHARING/ACCESS INFORMATION --------------------------      Licenses/restrictions placed on the data:        CREATIVE COMMONS ATTRIBUTION 4.0 INTERNATIONAL CC-BY   --------------------- DATA & FILE OVERVIEW ---------------------   We separate sets of data in terms of folders.    1. AMOC      This directory contains the AMOC streamfunction output from     simulations OGCM_CTL and OGCM_EXP.   2. hierarchy_statistics          This directory contains the statistics (mean, variability, ...)    and diagnosed quantities (ex: EOF, heat transport) of the hierarchy    output.      The output of CTL run of year 21 to 120 is in CTL_21-120.    The output of EXP run of year 81 to 180 is in EXP_81-180.   3. hierarchy_average      This directory is similar to is similar to hierarchy_statistics,     containing CTL and EXP. The difference is that it is the raw, unprocessed    mean data that contains the complete output variables.     -------------------------- METHODOLOGICAL INFORMATION --------------------------     1. Description of methods used for generation of data:       The data is generated with EMOM, a hierarchy of ocean models that is    applied in CESM1. The detail description of the model is in the paper    the dataset is preseted in (i.e. A Hierarchy of Global Ocean Models     Coupled to CESM1).   2. Methods for processing the data:      The output data is mostly the mean and variance of the climate variables.   3. Software-specific information needed to interpret the data:      The data is in NetCDF format.   --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: AMOC ---------------------------------------------   # Filename: MOC_[CTL|EXP].nc   Variable list:     1. MOC        Unit: Sv        The monthly mean value of streamfunction of the meridional overturning      circulation in ocean basins.     # Filename MOC_[CTL|EXP]_timeseries.nc   Variable list:     1. AMOC_max       Unit: Sv       The annual maximum value of the Atlantic Meridional Overturning Circulation.     2. AMOC_max_lat       Unit: degree north       The latitude of the location where AMOC_max occurs.     3. AMOC_max_z       Unit: m       The depth of the location where AMOC_max occurs.   --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: hierarchy_average ---------------------------------------------   In this directory, each sub-directory is of the form [MODEL_NAME]_[CTL|EXP] where MODEL_NAME can be SOM, MLM, EMOM or POP2. A sub-directory has three files: atm.nc, ocn.nc and ocn_regrid.nc.    atm.nc is the averaged data of atmosphere model output of year 21-121 of each model run on f09 grid. ocn.nc is the averaged data of ocean model output of year 21-121 of each model run on g16 grid. ocn_regrid.nc is the regrided version ocn.nc from grid g16 onto f09.   Details of the atm.nc variables can be found in CAM4 documentation https://www.cesm.ucar.edu/models/cesm1.0/cam/docs/ug5_1/hist_flds_fv_cam4_trop_bam.html   Details of the ocn.nc variables can be found in POP2 documentation https://ncar.github.io/POP/doc/build/html/users_guide/model-diagnostics-and-output.html   --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: hierarchy_statistics ---------------------------------------------   This directory conatins CTL and EXP runs folder where the statistics time is 21-121 for CTL and 81-180 for EXP.   Each experiment folder contains sub-directories of the form [MODEL_NAME]_[CTL|EXP] where MODEL_NAME can be SOM, MLM, EMOM or POP2. Each of these directories has the same analysis listed below.   # Filename: atm_analysis_[AAO|AO|ENSO|NAO|PDO].nc     Description: This file contains the derived climate variability patterns (i.e.                 AAO, AO, ENSO, NAO, PDO).      Variable list:         1. PCAs(modes, Ny, Nx)            Unit: None            The normalized PCAs. Different modes of the PCAs are separated according          to the first dimension.         2. PCAs_ts(time, modes)            Unit: None            The timeseries of projected PCAs onto the anomalies (i.e. the inner product of PCAs and anomalous fields).   # Filename: atm_analysis_mean_anomaly_[VARNAME].nc     Description: This file contains the mean, standard deviation of the denoted field.                VARNAME = [ICEFRAC|TAUX|TAUY|SST]     Variable list:         1. [VARNAME]_[TIMESCALE]M            Unit: ICEFRAC = None                TAUX    = N / m^2                TAUY    = N / m^2                SST     = K            The mean values of each grid point. TIMESCALE = [M|S|A] where M stands for monthly,          S for seaonal (MAM, JJA, SON, and DJF), A for annnual.                    2. [VARNAME]_[TIMESCALE]A            Unit: ICEFRAC = None                TAUX    = N / m^2                TAUY    = N / m^2                SST     = K            The anomalous values of each grid point. TIMESCALE = [M|S|A] where M stands for monthly,          S for seaonal (MAM, JJA, SON, and DJF), A for annnual.            3. [VARNAME]_[TIMESCALE]ASTD            Unit: ICEFRAC = None                TAUX    = N / m^2                TAUY    = N / m^2                SST     = K            The standard deviation of the anomalous values of each grid point. TIMESCALE = [M|S|A]           where M stands for monthly, S for seaonal (MAM, JJA, SON, and DJF), A for annnual.          4. [VARNAME]_[TIMESCALE]ASTD            Unit: ICEFRAC = None                TAUX    = (N / m^2)^2                TAUY    = (N / m^2)^2                SST     = K^2            The variance of the anomalous values of each grid point. TIMESCALE = [M|S|A]           where M stands for monthly, S for seaonal (MAM, JJA, SON, and DJF), A for annnual.    # Filename: atm_analysis_mean_var_[T|U].nc     Description: This file contains the mean, standard deviation of the denoted field.                VARNAME = [T|U]     Variable list:         1. [VARNAME]_[TIMESCALE]M            Unit: T       = K                U       = m / s            The mean values of each grid point. TIMESCALE = [M|A] where M stands for monthly, A for annnual.          2. [VARNAME]_[TIMESCALE]ASTD            Unit: T       = K                U       = m / s            The standard deviation of the anomalous values of each grid point. TIMESCALE = [M|A]           where M stands for monthly, A for annnual.          3. [VARNAME]_[TIMESCALE]AVAR            Unit: T       = K                U       = m / s            The variance of the anomalous values of each grid point. TIMESCALE = [M|A]           where M stands for monthly, A for annnual.    # Filename: atm_analysis_SST_CORR.nc     Description: This file contains the year-to-year correlation of monthly anomalous SST.     Variable list:         1. CORR(months, Ny, Nx)            Unit: None            The year-to-year correlation of monthly anomalous SST.   # Filename: ice_analysis_mean_anomaly_[aice|vice].nc     Description: This file contains the mean, standard deviation of the denoted field.                VARNAME = [aice|vice].                  The variables are exactly of the same structure as described in                 atm_analysis_mean_anomaly_[VARNAME].nc                  The unit for aice = None                The unit for vice = m   # Filename: ocn_analysis_mean_anomaly_STRAT.nc     Description: This file contains the mean, standard deviation of the denoted field.                STRAT is the difference of mean ocean temperatures T_top - T_bot.                T_top is the mean temperature of the top 50m of the ocean where as                T_bot is the mean temperature of the ocean between depth 50m to 503.7m.                  The variables are exactly of the same structure as described in                 atm_analysis_mean_anomaly_[VARNAME].nc                  The unit for STRAT = K   # Filename: atm_analysis_AHT_OHT.nc     Description: This file contains the indirectly derived atmosphere heat transport.      Variable list:         1. AHT(time, lat_bnd)            Unit: W            The monthly atmospheric heat transport.         2. AHT_AM(year, lat_bnd)                     Unit: W                    The annual atmospheric heat transport.         3. AHT_MEAN(lat_bnd)                     Unit: W                    The time-averaged atmospheric heat transport.         4. AHT_TFLX_CONV(time, lat)                     Unit: W / m                    The monthly-zonally-averaged atmospheric heat convergence.         5. AHT_TFLX_CONV_MEAN(lat)                               Unit: W                    The time-zonally-averaged atmospheric heat convergence.   # Filename: ocn_analysis_OHT.nc     Description: This file contains the derived ocean heat transport.      Variable list:         1. ADVT(time, lat)            Unit: K / s / m^3            The monthly vertically-integrated temperature tendency due to advection and horizontal diffusion.         2. ADVT_MEAN(lat)            Unit: K / s / m^3            The time-averaged ADVT.         3. OHT(time, lat_bnd)            Unit: W            The total monthly ocean heat transport.         4. OHT_MEAN(lat_bnd)            Unit: W            The time-average of OHT.         5. OHT_ADVT(time, lat_bnd)            Unit: W            The monthly ocean heat transport due to advection and horizontal diffusion.         6. OHT_ADVT_MEAN(time, lat_bnd)            Unit: W            The time-averaged of OHT_ADVT.         7. OHT_ADVT(time, lat_bnd)            Unit: W            The monthly ocean heat transport due to advection and horizontal diffusion.         8. OHT_ADVT_MEAN(lat_bnd)            Unit: W            The time-averaged of OHT_ADVT.           9. OHT_WKRSTT(time, lat_bnd)            Unit: W            The monthly ocean heat transport due to weak-restoring.         10. OHT_WKRSTT_MEAN(lat_bnd)            Unit: W            The time-averaged of OHT_WKRSTT.         11. SHF(time, lat)            Unit: W            The monthly surface heat flux.         12. SHF_MEAN(lat)            Unit: W            The time-average of SHF.         13. WKRSTT(time, lat)            Unit: K / s / m^2            The vertically integrated monthly weak-restoring.         14. WKRSTT_MEAN(lat)            Unit: W            The time-average of WKRSTT.     --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/importance_of_KH ---------------------------------------------   This directory conatins the average of CAM4 and EMOM output of field during year 21-30.   The meaning of the variable can be found in official website https://www.cesm.ucar.edu/models/cesm1.0/cam/docs/ug5_1/hist_flds_fv_cam4_trop_bam.html     --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/ocean_mean_temp ---------------------------------------------   This directory conatins the annual average of ocean mean temperature of the top 33 layers (507.33m) in the EXP run (sea-ice loss run)   # Filename: paper2021_[MODEL_NAME]_EXP.ocn_mean_T.nc     Description: This file contains the annual average of ocean mean temperature of the top 33 layers (507.33m).        Variable list:         1. TEMP(time, Nz)            Unit: degC                    Ocean temperature.           2. SALT(time, Nz)            Unit: kg / m^3 (PSU)                    Ocean salinity.     --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/ocean_heat_content_trend ---------------------------------------------   This directory conatins the difference of the ocean state between the year 181 and year 81 of the EXP run.   # Filename: OHC_diff_[MODEL_NAME].nc     Description: The difference of the ocean state between the year 181 and year 81 of the EXP run.        Variable list:         1. TEMP(time, Nz)            Unit: degC                    Ocean temperature.   --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/vice_target_file ---------------------------------------------   This directory conatins the sea-ice forcing used to derive Q-flux (CTL) and the forcing applied in EXP run.    # Filename: forcing.vice.[GRID].paper2021_[RUN]_POP2.nc     Description: This the sea-ice forcing used in the [RUN] in the grid of [GRID].                GRID = [f09|gx1v6]                [RUN] = [CTL|EXP]        Variable list:         1. vice_target(time, nlat, nlon)            Unit: m^3 / m^2 (volume density)                    Total ice volume.

opencc-by-4.0Jun 2022View details →

dryad32/100

Constructing the hierarchy of predictive auditory sequences in the marmoset brain

Our brains constantly generate predictions of sensory input that are compared with actual inputs, propagate the prediction-errors through a hierarchy of brain regions, and subsequently update the internal predictions of the world. However, the essential feature of predictive coding, the notion of hierarchical depth and its neural mechanisms, remains largely unexplored. Here, we investigated the hierarchical depth of predictive auditory processing by combining functional magnetic resonance imaging (fMRI) and high-density whole-brain electrocorticography (ECoG) in marmoset monkeys during an auditory local-global paradigm in which the temporal regularities of the stimuli were designed at two hierarchical levels. The prediction-errors and prediction updates were examined as neural responses to auditory mismatches and omissions. Using fMRI, we identified a hierarchical gradient along the auditory pathway: midbrain and sensory regions represented local, short-time-scale predictive processing followed by associative auditory regions, whereas anterior temporal and prefrontal areas represented global, long-time-scale sequence processing. The complementary ECoG recordings confirmed the activations at cortical surface areas and further differentiated the signals of prediction-error and update, which were transmitted via putatively bottom-up g and top-down b oscillations, respectively. Furthermore, omission responses caused by absence of input, reflecting solely the two levels of prediction signals that are unique to the hierarchical predictive coding framework, demonstrated the hierarchical predictions in the auditory, temporal, and prefrontal areas. Thus, our findings support the hierarchical predictive coding framework, and outline how neural circuits and spatiotemporal dynamics are used to represent and arrange a hierarchical structure of auditory sequences in the marmoset brain.

opencc-zeroMay 2022View details →

dryad32/100

Hierarchy of the factors influencing the broad-scale waterbirds functional diversity gradients in temperate China

Geographical gradients in species diversity have long fascinated biogeographers and ecologists. However, the extent and generality of the positive/negative effects of the important factors governing functional diversity (FD) patterns are still debated, especially for the freshwater domain. We examined lake productivity and functional richness (FRic) of waterbirds sampled from 35 lakes and reservoirs in northern China with a geographic coverage of over 5 million km2. We used structural equation modelling (SEM) to explore the causal relationships between geographic position, climate, lake productivity and waterbirds FRic. We found unambiguous altitudinal and longitudinal gradients in lake productivity and waterbirds FD, which were strongly mediated by local environmental factors. Specifically, we found 1) lake productivity increased northeast but decreased with altitude, and the observed gradients were driven by climate and nutrient availability, with 93% of variation explained in the individual SEM; 2) waterbirds FD showed similar geographic and elevational gradients.; the environmental factors which had direct and/or indirect effects on these geographic and elevational gradients included climate, lake productivity and morphology, which collectively explained more than 56% of the variation in waterbirds FD; and 3) a significant (P = 0.029) causality between lake productivity and waterbirds FD was confirmed. Nevertheless, the causality link was relatively weak in comparison with climate and lake area (standardized path coefficient was 0.65, 0.21, and 0.17 for climate, area, and productivity, respectively). Through articulating the dominant causality paths, our results could contribute to the mechanistic explanations underlying the observed broad–scale biodiversity gradients.

opencc-zeroAug 2022View details →

zenodo32/100

Result of Show View updates to Non Nested View Hierarchy

This video describes the results of applying the show view updates developer option to the android non-nested UI layout.

opencc-by-4.0Aug 2022View details →

zenodo32/100

Random Hierarchy Model

Code for the paper "How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model", preprint available here: <a href="https://arxiv.org/abs/2307.02129" rel="nofollow">arXiv:2307.02129</a>.

opencc-by-4.0Jun 2024View details →

zenodo32/100

OTTR standard type hierarchy

The standard type hierarchy of the OTTR framework.

opencc-by-4.0Jun 2024View details →

zenodo32/100

Kinematic Flexibility Analysis: Hydrogen Bonding Patterns Impart a Spatial Hierarchy of Protein Motion

KGS conformational ensembles of 100 substates from sampling ADK starting from the open conformation (PDB ID 4ake). Hydrogen bonds were included at thresholds of -1, -2, and -4 kcal/mol. Hydrogen bond network constraint relaxation was set to 1e-10 (nullspace floppy modes) and 1e-2 (kinematic flexibility modes)

opencc-by-4.0Jul 2018View details →

zenodo32/100

FoodEx2vec: Poincaré Embeddings of a FoodEx2 Hierarchy for Advanced Food Data Analysis

A dataset consisting of Poincaré embeddings of the FoodEx2 hierarchy, as well as the clustering results obtained with the Poincaré embeddings. It consists of two folders: <ol> <li>foodex2vec_clusters - where the clustering using the Poincaré embeddings can be seen;</li> <li>foodex2vec_poincare_embeddings - the actual Poincaré embeddings with different dimensions. </li> </ol>

opencc-by-nc-sa-4.0Aug 2019View details →

zenodo32/100

Hierarchy entries: hierarchy_entries 2017-01-04

Will put in this dataset versions of hierarchy_entries.This is 316.6 MB in size.

opennotspecifiedAug 2024View details →

zenodo32/100

EOL China: Taxonomic Hierarchy of COL-China 2012

Open the record for dataset details and reuse information.

opennotspecifiedAug 2024View details →

zenodo32/100

FIGURE 1 in Implementation as theory, hierarchy as transformation, homology as synapomorphy

FIGURE 1. The anatomy of a cladistic analysis. a. A cladistic analysis recovers branching diagrams (cladograms) from a data matrix (e.g., binary or parenthesis matrix). The characater-state relationships (homologs) may be interpreted phylogenetically as transformations; b.The data matrix is analysed by a computer program that produces a cladogram. The phylogenetic tree is created through human interpretation only; c.#A data matrix contains ordered data, which is converted to a branching diagram (cladogram) using a computer program. The cladogram depicted here is based on character 1 (namely, a character tree). The character-states are treated as synapomorphies within A{B{C,D}, where C and D share character-state 0 or, the states can be shown as a relationship, namely 0{1,1}. In the phylogenetic tree, the character-states are shown as grouped plesiomorphies and apomorphies. The transformation is inferred by the person viewing the tree; d. The function of the data matrix is to show which character-states are ascribed to taxa. The cladogram represents a classification in order to identify monophyly, while a phylogenetic tree interprets a classification through transformation; e. the data matrix and cladogram represent homologs. In the phylogenetic tree, homologs are interpreted to be derived or reversed (apomorphic) or plesiomorphic (primitive).

opennotspecifiedApr 2013View details →

zenodo32/100

Single-cell analysis of patient-derived PDAC organoids reveals cell state heterogeneity and a conserved developmental hierarchy

scRNA-seq read count matrices from patient-derived PDAC organoids.

opencc-by-4.0Oct 2021View details →

zenodo32/100

Encyclopedia of Life Taxonomy Patch for Dynamic Hierarchy Version 3.1

A taxonomic patch for the <a href="https://eol.org/docs/eol-dynamic-hierarchy">EOL Dynamic Hierarchy</a>.

opencc-zeroNov 2024View details →

zenodo32/100

EOL Dynamic Hierarchy version 3.1 with higher classification

The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented. Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy (<a title="EOL Dynamic Hierarchy all versions" href="https://doi.org/10.5281/zenodo.13239662">see current version here</a>) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see <a href="https://eol.org/docs/eol-dynamic-hierarchy">EOL Dynamic Hierarchy</a>.

openother-pdAug 2024View details →

zenodo32/100

CD4 T cell receptor hierarchies are stable and independent of HIV-mediated dysregulation of immune homeostasis

LT-ART and A5248 folders contain preprocessed CyTOF files (FCS format) from a 31-marker mass cytometry panel to examine all major PBMC lineages and specifically CD4 and CD8 T cell memory dynamics in people with HIV (PWH) who are durably ART suppressed for an average of 6.7 years (LT-ART, n = 10) and PWH in the first 500 days following ART initiation (A5248, n = 10). The panel also includes markers of activation (HLA-DR, CD38, CCR5), activation/exhaustion (PD-1), proliferation (Ki67), survival (Bcl-2) and long-lived memory (CD127). Preprocessed annotated data objects (A5248_subsample.h5ad, LT-ART_subsample.h5ad) for unsupervised analysis can be accessed using the 'read_h5ad' function in Scanpy. CD4_MSI and CD8_MSI folders contain the MSI data for the Gamma fixed-effects regression models.  All source code for reproduction of the results can be found in the GitHub repository: <a href="https://github.com/glab-hiv/immune-recovery">https://github.com/glab-hiv/immune-recovery</a>

opencc-by-4.0Dec 2022View details →

dryad32/100

Data from: Inferring longitudinal hierarchies: framework and methods for studying the dynamics of dominance

Open the record for dataset details and reuse information.

publicJan 2019View details →

dryad32/100

Data from: The evolutionary origins of hierarchy

Open the record for dataset details and reuse information.

publicMar 2017View details →

dryad32/100

Data from: Coexistence through mutualist-dependent reversal of competitive hierarchies

Open the record for dataset details and reuse information.

publicNov 2018View details →

ScienceDex guides

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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