Find research datasets worth reusing

opencc-by-4.0Sep 2021View details →

Comparative genomics of eight aphid subfamilies reveals variable relationships between host horizontally-transferred genes and symbiont peptidoglycan metabolism.

Genome assemblies, annotations, and orthologs of aphids (Geopemphigus sp., Stegophylla sp., Chaitophorus viminalis, and Pemphigus obesinymphae) and their symbionts.  step1_final_assemblies_and_annotations.tar.gz: Aphid genomes and annotations step2_protein_evidence_used_for_genome_annotation.tar.gz: Protein evidence used for aphid genome annotation step3_amino_acid_inputs_for_aphid_orthologs: amino acid inputs for aphid ortholog assignmentt step5_buchnera_genomes_and_annotations.tar.gz: Buchnera genomes and annotations

opencc-by-4.0Jan 2022View details →

Supplement to "On the accuracy of RTTOV-SCATT for radiative transfer at all-sky microwave and submillimeter frequencies"

This supplement contains a set of realistic cloudy and precipitating conditions from the Integrated Forecast System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) and the corresponding simulated brightness temperatures for the journal article “On the accuracy of RTTOV-SCATT for radiative transfer at all-sky microwave and submillimeter frequencies”, which is under review at the Journal of Quantitative Spectroscopy and Radiative Transfer. These data are available as benchmark for model developers. The IFS profiles correspond to model data interpolated (using the IFS observation operator) to the locations of the Atmospheric Infrared Sounder (AIRS) for a 12 h period centred on 03 UTC on 1 November 2018. Note that only a subset of the full set of AIRS locations are supplied here. The IFS model version cycle 46r1 was used, in a research experiment, to generate these profiles. The model was configured to T1279co resolution  (about 8-9 km) with 137 levels in the vertical. The forecast was initialized from operational analyses at 18 UTC on 31 October 2018, and hence corresponds to the 12 h forecast "background" used in the data assimilation cycle. The following information concerns only the IFS model data: <ul> <li>Copyright statement: Copyright "© 2022 European Centre for Medium-Range Weather Forecasts (ECMWF)".</li> <li>Source: www.ecmwf.int.</li> <li>Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.</li> <li>Licence Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0), https://creativecommons.org/licenses/by/4.0/.</li> <li>Further information on the ECMWF data policy, and guidance on how to comply with it, is at https://apps.ecmwf.int/datasets/licences/general/.</li> </ul> The simulated brightness temperatures are also distributed under Creative Commons Attribution 4.0 International (CC BY 4.0). The full journal description of this dataset is currently under review, with the current citation: Barlakas, V., Galligani, V. S.,  Geer, A. J., Eriksson, P., 2022. On the accuracy of RTTOV-SCATT for radiative transfer at all-sky microwave and submillimeter frequencies. Submitted to Journal of Quantitative Spectroscopy and Radiative Transfer. The work of Vasileios Barlakas at Chalmers University of Technology is funded by a EUMETSAT fellowship program.

opencc-by-4.0Dec 2021View details →

Exchange scaling of ultrafast angular momentum transfer in 4f antiferromagnets

Raw data for "Exchange scaling of ultrafast angular momentum transfer in 4f antiferromagnets" The h5 files are provided: 1. Windsor_LnRh2Si2_2022_static.h5 This file provides the temperature dependences of the 4f moment, as collected from the (001) magnetic reflection (details described in the supplementary materials, section 1.1. The file is divided by Ln ion, for each LnRh2Si2 material. For each material the sample temperature and the moment (normalized) are given. 2. Windsor_LnRh2Si2_2022_dynamic.h5 This file provides the delay dependences of the 4f moments, as collected from the (001) magnetic reflection (details provided in the methods section). The file is divided by Ln ion, for each LnRh2Si2 material. This is further divided into datasets that correspond to different pump fluences. For each fluence the fields provided are the moment (normalized), the corresponding error (see methods), the delay, and the fluence value.

dryad40/100

Data from: Evaluating temporal and spatial transferability of a tidal inundation model for foraging waterbirds

For ecosystem models to be applicable outside their context of development, temporal and spatial transferability must be demonstrated. This presents a challenge for modeling intertidal ecosystems where spatiotemporal variation arises at multiple scales. Models specializing in tidal dynamics are generally inhibited from having wider ecological applications by coarse spatiotemporal resolution or high user competency. The Tidal Inundation Model of Shallow-water Availability (TiMSA) uniquely simulates tides to empirically derive a time-integrated measure of availability for a shallow water depth range defined by the user. To evaluate temporal and spatiotemporal transferability, we employed TiMSA at the development site in the Florida Keys and at novel sub-sites in the Florida Bay (application site) under a different time period (application period). We used foraging Little Blue Herons (Egretta caerulea) as the ecological unit with which to constrain the model's 'water depth window', i.e., range of water depths to estimate shallow-water availability. At the development site, temporally consistent water depth windows contrasted with interannual variation in shallow-water availability which revealed short-term changes in Little Blue Heron foraging habitat. At the application site, water depth accuracy varied by sub-site and was correlated with spatial error in bathymetric elevation. Although TiMSA parameters were sensitive to environmental temporal variation and uncertainty in spatial data, a spatially-explicit water depth window generated reliable estimates of shallow-water conditions over space and time at the development and application sites. By exploring the contributing factors to model error, we provide solutions to reduce uncertainty of TiMSA parameters at potential application sites and recommendations for addressing bathymetric inaccuracy in digital elevation models. Accurately quantifying spatiotemporal changes of shallow-water has implications for monitoring habitat conditions for tidally-influenced species and projecting future changes to coastal ecosystems in response to anthropogenic stressors and natural disturbances such as sea level rise.

opencc-zeroMar 2022View details →

SIMSI-Transfer: SIMSI and MaRaCluster output files

Dataset 2 for the publication "SIMSI-Transfer: Software-assisted reduction of missing values in phosphoproteomic and proteomic isobaric labeling data using tandem mass spectrum clustering".   This dataset contains all output files produced by SIMSI-Transfer, including the intermediate MaRaCluster output files.

SIMSI-Transfer: MaxQuant output files

Dataset 1 for the publication "SIMSI-Transfer: Software-assisted reduction of missing values in phosphoproteomic and proteomic isobaric labeling data using tandem mass spectrum clustering".   This dataset contains all files generated by MaxQuant from database searches of the studies used for assessment of SIMSI-Transfer in the publication.

Dataset: Decentralized Cross-Blockchain Asset Transfers with Transfer Confirmation

Dataset resulting from the experiments conducted in "Decentralized Cross-Blockchain Asset Transfers with Transfer Confirmation"

Simple OMERO project packed with omero-cli-transfer

This is a simple zip file with a "minimal" OMERO project packed using <a href="http://pypi.org/project/omero-cli-transfer">omero-cli-transfer</a>. It contains 4 datasets with examples of "regular" images, single-file-multiple-images (.ndpi), multiple-files-multiple-images (.vsi) and an image without an underlying file (exported as a tiff - obviously, upon reimporting, it WILL have an underlying file!). These have multiple annotations, tags and ROIs of multiple types, to cover most of the existing/common use cases in an OMERO instance. To unpack it into a project in your OMERO server, use "omero transfer unpack simple_project.zip".

Data generated by the model presented in the research article entitled "Simulation of mass and heat transfer in an evaporatively cooled PEM fuel cell"

This repository provides all the data and scripts necessary to reproduce the line plots shown in the manuscript entitled "Simulation of mass and heat transfer in an evaporatively cooled PEM fuel cell".

dryad40/100

Factors influencing transferability in species distribution models

Species distribution models (SDMs) provide insights into species' ecology and distributions and are frequently used to guide conservation priorities. However, many uses of SDMs require model transferability, which refers to the degree to which a model built in one place or time can successfully predict distributions in a different place or time. If a species' model has high spatial transferability, the relationship between abundance and predictor variables should be consistent across a geographical distribution. We used Breeding Bird Surveys, climate and remote sensing data, and a novel method for quantifying model transferability to test whether SDMs can be transferred across the geographic ranges of 129 species of North American birds. We also assessed whether species' traits are correlated with model transferability. We expected that prediction accuracy between modeled regions should decrease with 1) geographical distance, 2) degree of extrapolation, and 3) were affected by a 'core-boundary' effect, which assesses distances to the boundary of a distribution. Our results suggest that very few species have a high model transferability index (MTI). Species with large distributions, with distributions located in areas with low topographic relief, and with short lifespans are more likely to exhibit low transferability. Transferability between modeled regions also decreased with geographical distance and degree of extrapolation. We expect that low transferability in SDMs potentially resulted from both ecological non-stationarity (i.e., biological differences within a species across its range) and over-extrapolation. Accounting for non-stationarity and extrapolation should substantially increase prediction success of species distribution models, therefore enhancing the success of conservation efforts.

FIG. 8 in Review of Paradiscocyrtus Mello-Leitão, 1927 (Gonyleptidae, Opiliones), with the transfer of Paradiscocyrtus cerayanus Roewer, 1929 to Discocyrtus Holmberg, 1878 and a new interpretation of its type locality

FIG. 8. — Discocyrtus cerayanus (Roewer, 1929) n. comb., MNRJ 7244†, male genitalia, distal part: A, dorsal view; B, lateral view; C, ventral view. Scale bars: A, 50 μm; B, C, 100 μm. Colored features are the genitalic macrosetae of VP: light blue, MS A; dark blue, MS B; magenta, MS C; yellow, MS D; green, MS E.

FIG. 6 in Review of Paradiscocyrtus Mello-Leitão, 1927 (Gonyleptidae, Opiliones), with the transfer of Paradiscocyrtus cerayanus Roewer, 1929 to Discocyrtus Holmberg, 1878 and a new interpretation of its type locality

FIG. 6. — Habitus of Discocyrtus cerayanus (Roewer, 1929) n. comb., MNRJ 7244†, in alcohol, male from Caeté, Minas Gerais, Brazil: A, dorsal view; B, ventral view; C, lateral view; D, anterior view. Scale bars: 1 cm. Photos: R. Carvalho.

FIG. 1 in Review of Paradiscocyrtus Mello-Leitão, 1927 (Gonyleptidae, Opiliones), with the transfer of Paradiscocyrtus cerayanus Roewer, 1929 to Discocyrtus Holmberg, 1878 and a new interpretation of its type locality

FIG. 1. — Previous state of knowledge of the Paradiscocyrtus Mello-Leitão, 1927 species composition between 1929-2020: A, B, Paradiscocyrtus neglectus Mello- Leitão, 1927 (male), in vivo, from Itatiaia, Rio de Janeiro, Brazil; C, Paradiscocyrtus neglectus Mello-Leitão, 1927 (female), in vivo, same locality; D, Discocyrtus cerayanus (Roewer, 1929) n. comb. [previously Paradiscocyrtus cerayanus] (male), MNRJ 7245†, in alcohol, from Caeté, Minas Gerais, Brazil; E, F, Bunopachylus orientalis (Roewer, 1913) [of which Paradiscocyrtus trochanteralis Roewer, 1929 is recognized as junior synomyn] (male), in vivo, from Águas Mornas, Santa Catarina, Brazil. Scale bar: 1 cm. Photos: A-C, A. Kury; D, R. Carvalho; E, F, M. Medrano.

FIG. 3 in Review of Paradiscocyrtus Mello-Leitão, 1927 (Gonyleptidae, Opiliones), with the transfer of Paradiscocyrtus cerayanus Roewer, 1929 to Discocyrtus Holmberg, 1878 and a new interpretation of its type locality

FIG. 3. — Cladogram depicting proposed external and internal phylogenetic relationships of Paradiscocyrtus Mello-Leitão, 1927, with synapomorphies for each clade mapped using ACCTRAN. This is the most frequent topology (k-values = 5, 6, 10, 15 and 20) obtained by TNT. Blue squares, nonhomoplastic synapomorphies; white circles, homoplastic synapomorphies. Number of characters above and number of states below symbols.

FIG. 5 in Review of Paradiscocyrtus Mello-Leitão, 1927 (Gonyleptidae, Opiliones), with the transfer of Paradiscocyrtus cerayanus Roewer, 1929 to Discocyrtus Holmberg, 1878 and a new interpretation of its type locality

FIG. 5. — Brazil, showing distribution of Discocyrtus cerayanus (Roewer, 1929) n. comb. and Paradiscocyrtus neglectus Mello-Leitão, 1927. In the main map: 1) shaded areas in the background represent the regionalization ("Provinces") of the Neotropics (Morrone 2014); 2) the red-checkered area shows the Brazilian state of Ceará, misinterpreted by Mello-Leitão (in Roewer 1931) as the real meaning of "Ceraya" (type locality of D. cerayanus n. comb.) recorded by Roewer (1929). Here, "Ceraya" is interpreted as "Serra do Caraça", a mountain range from the Minas Gerais state. The inset shows the areas of endemism of the Brazilian Atlantic Rain Forest used here follow the concept exposed by DaSilva et al. (2017).

FIG. 4 in Review of Paradiscocyrtus Mello-Leitão, 1927 (Gonyleptidae, Opiliones), with the transfer of Paradiscocyrtus cerayanus Roewer, 1929 to Discocyrtus Holmberg, 1878 and a new interpretation of its type locality

FIG. 4. — Diagnostic character states of Paradiscocyrtus Mello-Leitão, 1927 (A, C, E) in contrast to Discocyrtus s. str. (B, D, F): A, B, stylus of glans (yellow); C, D, ocularium pair of spines (green); E, F, DS shape (gray), area III armature (blue), comparative shape between area IV posterior and DS posterior borders (red), Cx IV retrolateral apophysis (black); Tr IV prolateral medial apophysis (magenta, absent in Discocyrtus s. str.). Scale bars: A, B, 50 μm; C, D, E, F, 1 mm.

FIG. 7 in Review of Paradiscocyrtus Mello-Leitão, 1927 (Gonyleptidae, Opiliones), with the transfer of Paradiscocyrtus cerayanus Roewer, 1929 to Discocyrtus Holmberg, 1878 and a new interpretation of its type locality

FIG. 7. — Discocyrtus cerayanus (Roewer, 1929) n. comb., (SMF RII 996/53), male holotype, from "Ceraya" [Serra do Caraça, Minas Gerais], Brazil: A, habitus, dorsal view; B, same, lateral view; C, ocularium, anterior view; D, Cx IV, ventral view; E, right Tr-Fe IV, dorsal view; F, same, prolateral view; G, same, ventral view; H, same, retrolateral view; I, right Pa-Ti IV, prolateral view; J, same, ventral view. Scale bars: 1 cm.

opencc-by-4.0Dec 2021View details →

Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models

Supporting data for <a href="https://arxiv.org/abs/2111.13736">https://arxiv.org/abs/2111.13736</a>. ACEhamiltonians.jl code This is an archived copy of the ACEhamiltonians.jl code to accompany the paper <a href="https://arxiv.org/abs/2111.13736">arXiv:2111.13736</a>. See <a href="https://github.com/ACEsuit/ACEhamiltoniansExamples">https://github.com/ACEsuit/ACEhamiltoniansExamples</a> for examples of how to use this code. The code is written in <a href="https://julialang.org/">Julia</a> and requires v1.6 or later. To install the Julia depenendencies: <pre><code><code>$ cd ACEhamiltonians.jl $ julia julia> import Pkg julia> Pkg.activate(".") julia> Pkg.instantiate() </code></code></pre> The scripts <code>test/plots.jl</code>, <code>test/fcc-to-bcc.jl</code> and <code>test/vacancy.jl</code> which produce all the plots in the paper can then run as, e.g. <pre><code><code>julia --project=. test/plots.jl </code></code></pre> Training data The <code>training_data</code> folder contains the atomic structure, Hamiltonian and overlap matrices stored in HDF5 format with the following schema: <ul> <li>Data Group : aitb/</li> <li>Datasets : <ul> <li>H : Real-space Hamiltonian Matrix. Type: Float64. Shape: Tensor(# of TB Cells, # of Rows, # of Columns)</li> <li>S : Real-space Overlap Matrix. Type: Float64. Shape: Tensor(# of TB Cells, # of Rows, # of Columns)</li> <li>energy : Energy. Unit: eV. Type: Float64. Shape: Scalar</li> <li>freeenergy : Free Energy. Unit: eV. Shape: Scalar</li> <li>unitcell : Unit cell vectors. Type: Float64. Shape: Matrix(3,3)</li> <li>positions : Atom positions. Type: Float64. Shape: Array(3)</li> <li>forces : (Optional, if available) Forces. Type: Float64. Shape: Array(3)</li> <li>metadata : JSON String including dictionary of information of FHIaims calculation (k-points, basis sets), TB Cells, Cutoff, Orbital definitions.,</li> </ul> </li> </ul> The molecular dynamics and FHI-aims parameters are described in the manuscript. On-site models The <code>onsite_models_ord2</code> folder contains our correlation order 2 models for the on site blocks of the Hamiltonian, in a JSON format readable by the <a href="https://github.com/acesuit/ACE.jl">ACE.jl</a> and <a href="https://github.com/ACEsuit/ACEhamiltonians.jl">ACEhamiltonians.jl</a> Julia packages. There are separate files for the Hamiltonian (<code>*_H.json</code>) and overlap (<code>*_S.json</code>) models. The JSON files also contain training and test sets and associated errors as plotted in Figure 3 in our manuscript. Models have a unique identifier (UUID) which is a hash of the input parameters and training data. The mapping from (order, max_degree) to UUID is as follows: <pre><code><code>(2,4) - 13427527590286463256 (2,5) - 10538156191357510769 (2,6) - 1646489440533135164 (2,7) - 12130775482127724115 (2,8) - 12487060958610974041 (2,9) - 2653067664384673997 (2,10) - 1143382251563115664 (2,11) - 4564001820340015372 (2,12) - 9474261500251782658 </code></code></pre> Off-site models The <code>offsite_models_ord1</code> and <code>offsite_models_ord2</code> folders contain our order 1 and order 2 offsite models for Hamiltonian and overlap matrices. The mapping from (H_order, H_max_degree) + (S_order, S_max_degree) to UUID is as follows: <pre><code><code>(1,6) + (1,8) - 7014526518680934587 (1,7) + (1,9) - 8594416159488562244 (1,8) + (1,10) - 10204186688118368371 (1,9) + (1,11) - 13078304848585360574 (1,10)+ (1,12) - 14750835312950641338 (1,11)+ (1,13) - 9883802224093245794 (1,12)+ (1,14) - 3907899412408606585 (1,13)+ (1,15) - 201683837542179657 (1,14)+ (1,16) - 277744202775070779 (2,6) + (1,8) - 4699475053563592071 (2,7) + (1,9) - 489637409713831432 (2,8) + (1,10) - 18034631670613263469 (2,9) + (1,11) - 720654516759450160 (2,10)+ (1,12) - 15214900801060024044 (2,11)+ (1,13) - 13798832597295943078 (2,12)+ (1,14) - 13162803789413134473 </code></code></pre> FCC only Onsite models: <pre><code><code>2 6 5311732756869418284 2 7 13030014632886405308 2 8 5820099621734447846 2 9 10161014511878227635 2 10 11298425190201843107 2 11 9932031839231628354 2 12 9447261873515969583 </code></code></pre> Optimised FCC model <code>16110190062237887798</code> BCC only Onsite models: <pre><code><code>2 6 8949023800586845770 2 7 8045797268444730200 2 8 6919809282139600809 2 9 9935027806122780319 2 10 6376963380608532713 2 11 5001375576268070883 2 12 9678585765722197901 </code></code></pre> Optimised BCC model <code>10293566074413000591</code> FCC+BCC optimised models Onsite models <pre><code><code>2 6 2154760103892646619 2 7 6450474921309693835 2 8 14227277988574899288 2 9 476820595195218567 2 10 5364136683220082110 2 11 14619519825012606580 2 12 14181614899005838824 </code></code></pre> Offsite FCC+BCC optimised model - <code>4570230078043807257</code> Model errors The <code>model_errors</code> directory contains summarised model errors for the training and testing errors for the models listed above. Reference data Reference electronic structure data computed for the BCC and FCC crystals, along the Bain path and for the relaxed vacancy is stored in the <code>reference_data</code> folder. The Hamiltonian and overlap matrices are stored as compressed binary HDF5 files. The format and metadata can be viewed with the <code>h5dump</code> utility, or read in using the supplied Julia code (or indeed from other languages). Predicted data The <code>predicted_data/FCC</code> and <code>predicted_data/BCC</code> folders contain HDF5 files with the results of all model predictions shown in the manuscript on the FCC and BCC crystal structures. <code>predicted_data/FCC-to-BCC</code> contains the results of predictions along the Bain path with the optimized model described in the manuscript and <code>predicted_data/vacancy</code> contains the vacancy calculations.