Find research datasets worth reusing

opencc-by-4.0May 2021View details →

Dataset for A Fast Frozen Phonon Algorithm Using Mixed Static Potentials

Multislice simulation dataset for "A Fast Frozen Phonon Algorithm Using Mixed Static Potentials"

opencc-by-4.0Jun 2022View details →

Dataset for "A capillary bundle model for the electrical conductivity of saturated frozen porous media"

This dataset supports the research study 'A capillary bundle model for the electrical conductivity of saturated frozen porous media' by H. L. Luo, D. Jougnot, A. Jost, J. D. Teng and L. D Thanh. We provide the experimental data from this study and  the published data from Coperey et al. (2019a,b) and Duvillard et al. (2018, 2021) for verifing the proposed model with different PSDs (lognormal and fractal distribution). Matlab code Description: Untitled 1- the code for determining the electrical conductivity as a function of temperature and the sensitive analysis; Untitled 2- the code for comparison between the experimental data and the proposed model; Untitled 3- the code for comparison of the contribution between the bulk conduction and surface conduction to the total electrical conductivity; Untiled 4- the code for evolution of the effective formation factor as a function of the temperature.

opencc-by-4.0Dec 2021View details →

The LOTUS Initiative for Open Natural Products Research: frozen dataset union wikidata (with metadata)

Dataset present on Wikidata used in the frame of the LOTUS Initiative: <a href="https://doi.org/10.7554/eLife.70780">https://doi.org/10.7554/eLife.70780</a>

opencc-by-4.0Feb 2023View details →

Dataset for "Predicting the electrical conductivity of partially saturated frozen porous media, a fractal model for wide ranges of temperatures and salinities"

This dataset supports the research study "Predicting the electrical conductivity of partially saturated frozen porous media, a fractal model for wide ranges of temperatures and salinities" by H. L. Luo, D. Jougnot, A. Jost, J. D. Teng, A. Mendieta, G. Lin, and L. D. Thanh. We provide the experimental data of electrical condutivity and unfrozen water saturation with different initial water saturations and salinities. Meanwhile, we also offer the matlab code for calculating the predicted values of electrical conductivity and apparent formation factor. Each file has its header, describing each column.

Appendix List of samples of deep frozen frog legs with purchase date, collection number, haplotype number, taxonomic identification, tibia length (TL) and estimated snout vent length (SVL). in Which frog's legs do froggies eat? The use of DNA barcoding for identification of deep frozen frog legs (Dicroglossidae, Amphibia) commercialized in France

Appendix List of samples of deep frozen frog legs with purchase date, collection number, haplotype number, taxonomic identification, tibia length (TL) and estimated snout vent length (SVL).

Fig. 2. Minimum spanning network depicting relationships among 16S in Which frog's legs do froggies eat? The use of DNA barcoding for identification of deep frozen frog legs (Dicroglossidae, Amphibia) commercialized in France

Fig. 2. Minimum spanning network depicting relationships among 16S haplotypes of Fejervarya cancrivora (Gravenhorst, 1829). The size of each circle is proportional to the haplotype frequency and the lengths of the connecting lines are proportional to the number of mutations. Colors refer to distinct regions (Indonesia: Java, Sumatra, Bali, Kalimantan, Bangka; Malaysia; Taiwan) and commercialized frogs of unknown origin are in black.

Fig. 3. Histograms. A in Which frog's legs do froggies eat? The use of DNA barcoding for identification of deep frozen frog legs (Dicroglossidae, Amphibia) commercialized in France

Fig. 3. Histograms. A. Snout vent length (in mm) in adult Fejervarya cancrivora (Gravenhorst, 1829) from samples collected for scientific purposes (Boulenger 1920) and collection specimens as mentioned in Material and methods. B. Snout vent length estimated from tibia length of genetically identified frog legs from French supermarkets (specimen list, see Appendix).

Fig. 1 in Which frog's legs do froggies eat? The use of DNA barcoding for identification of deep frozen frog legs (Dicroglossidae, Amphibia) commercialized in France

Fig. 1. Phylogeny of Indonesian species of Fejervarya and Limnonectes recovered by the Bayesian analysis (GTR + I + G model). Hoplobatrachus rugulosus (Wiegmann, 1834) and Occidozyga laevis (Günther, 1858) were used as outgroups. Numbers on nodes represent Bayesian posterior probabilities, * indicates a value higher than 0.98. Only values higher than 0.75 are represented. h01 to h18 indicate the 18 haplotypes from frozen frog legs recovered in this study.

opencc-by-4.0Jan 2022View details →

Dataset: Assessing MP2 frozen natural orbitals in relativistic correlated electronic structure calculations

This dataset collects the unprocessed (= outputs from calculations) results discussed in the paper titled "Assessing MP2 frozen natural orbitals in relativistic correlated electronic structure calculations", by Xiang Yuan, Luvas Visscher and Andre Severo Pereira Gomes. It also contains the figures used in the manuscript.

opencc-by-4.0Aug 2019View details →

Global datasets for duration of frozen ground with snow cover and without snow cover

These are the main datasets associated with the submitted manuscript--Climate change causes functionally colder winters for snow cover-dependent organisms. The datasets include duration of frozen ground with snow cover (Dsc) and without snow cover (Dfwos) for the historical (1982-2014) and future (2071-2100) periods, which are available with GeoTIFF format at 5-km resolution. Dsc and Dfwos are defined as the number of days during the frozen season when frozen ground is covered by snow or not, which are calculated using AVHRR/MODIS snow cover product and NASA MEaSUREs Global Record of Daily Landscape Freeze/Thaw Status dataset.

opencc-bySep 2019View details →

Resnet50 v2 frozen from slim dataset (batch size = 1)

Pretrained model from the following repository: <a href="https://github.com/tensorflow/models/tree/master/research/slim">https://github.com/tensorflow/models/tree/master/research/slim</a> batch size set to 1

dryad40/100

Data from: When the "selfish herd" becomes the "frozen herd": spatial dynamics and population persistence in a colonial seabird

Aggregations are common in ecological systems at a range of scales and may be driven by exogenous constraints such as environmental heterogeneity and resource availability or by 'self-organizing' interactions among individuals. One mechanism leading to self-organized animal aggregations is captured by Hamilton's 'selfish herd' hypothesis, which suggests that aggregations may be driven by an individual's effort to minimize their risk of predation by surrounding themselves with conspecifics. We demonstrate that aggregations observed in Adélie penguin (Pygoscelis adeliae) colonies are a convolution of both self-organized dynamics and external forcing arising from landscape terrain. In fluid, highly mobile aggregations, individuals are constantly moving in response to changing environmental conditions, the locations of predators, or the movements of conspecifics. However, when the ability to rearrange is limited and spatial reconfiguration occurs on slower time scales than changes in population size, systems may become trapped in sub-optimal arrangements. We use simulated annealing to demonstrate that Adélie penguin colonies are frozen in sub-optimal spatial arrangements, and employ an individual-based modelling approach to demonstrate that this sub-optimal spatial configuration is driven by a convolution of nest site fidelity and stochastic events at the level of individual nests. The resulting spatial dynamics are responsible for a hysteretic response to long-term changes in abundance. We find that declining abundance leads to fragmentation even in a homogeneous environment, which has population-level consequences for reproductive success because predation is biased towards colony edges. Strong edge effects from heterogeneous predation coupled with fragmentation in response to population declines creates a positive feedback cycle that can accelerate population decline. This work provides a mechanistic understanding of complex spatial structuring in penguin colonies, provides a link between current spatial patterning and past dynamics, and suggests the possibility of critical collapse in seabird populations.

opencc-zeroJul 2019View details →

opencc-by-4.0Dec 2021View details →

The LOTUS Initiative for Open Natural Products Research: frozen dataset

Dataset used in the frame of the LOTUS Initiative: <a href="https://doi.org/10.7554/eLife.70780">https://doi.org/10.7554/eLife.70780</a>

opencc-by-4.0Mar 2023View details →

Deidentified Frozen Seafood Dataset

About Dataset This dataset contains de-identified data on testing for infection in consignments of frozen seafood where importation into Australia was attempted. The data is from an approximately four-year period between 2015 and 2022. Each row represents a batch, where a batch is either a consignment, or a subdivision of a consignment. A sample of 13 bags was selected from each consignment. Each bag contains on average 40 items. A pool of 5 items was selected from each bag. Pools were then tested for the presence of infection. The result is a number of positive tests out of 13 pools tested. Deidentification steps included: <ul> <li>Tonnage was rounded to the nearest whole number and truncated above at 20.</li> <li>One year with relatively few observations was grouped with an adjacent year.</li> <li>Country of export, country of harvest, consignment and year were all randomly scrambled to arbitrary code numbers. The year numbers are not in chronological order.</li> </ul> Countries of harvest or export with less than 20 batches have been removed. Consignments with more than two batches have been removed. Variable Definitions <table> <tbody> <tr> <td> Variable Name </td> <td> Values </td> <td> Description </td> </tr> <tr> <td> batch.scrambled </td> <td> 1, …, 2850 </td> <td> Randomised batch identifier number. The data set contains one row per batch. </td> </tr> <tr> <td> consignment.scrambled </td> <td> 1, …, 2680 </td> <td> Randomised consignment identifier number. Consignments contain either one or two batches. (Consignments with more batches have been excluded from this dataset.) </td> </tr> <tr> <td> country.export.scrambled </td> <td> 1, …, 5 </td> <td> Randomised country of export identifier number. (Countries with relatively few batches have been excluded.) </td> </tr> <tr> <td> country.harvest.scrambled </td> <td> 1, …, 8 </td> <td> Randomised country of export identifier number. (Countries with relatively few batches have been excluded.) </td> </tr> <tr> <td> tonnage </td> <td> 0, …, 20 </td> <td> Weight of batch cargo in tonnes (rounded to the nearest whole tonne and truncated at 20). </td> </tr> <tr> <td> year.scrambled </td> <td> 1, 2, 3, 4 </td> <td> Randomised year of importation (note: not in temporal order) </td> </tr> <tr> <td> numPos </td> <td> 0, …, 10 </td> <td> Number of positive pooled tests (out of 13) </td> </tr> </tbody> </table> Author Field I am the uploader of this de-identified dataset, which is published with the permission of the data owner.