Find research datasets worth reusing

opencc-by-4.0Apr 2024View details →

DArTseq genetic dataset associated with the article "Hybrids as mirrors of the past: genomic footprints reveal spatio-temporal dynamics and extinction risk of alpine extremophytes in the mountains of Central Asia"

Description: This file stores genetic information on the single nucleotide polymorphism markers (SNPs) in the examined alkali grasses (Poaceae: Puccinellia). The dataset was generetad by Genome-Wide Restriction Fragment Analysis via the DArTseq platform (Diversity Arrays Technology Pty Ltd, Canberra, Australia), which combines complexity reduction methods, fragment size selection, and high-throughput sequencing, optimised for a target organism. The file contains raw data.   Usage notes: We used R (version 4.2.2, 2022-10-31; https://www.R-project.org/) and RStudio (version 2022.07.2+576 "Spotted Wakerobin" Release (e7373ef832b49b2a9b88162cfe7eac5f22c40b34, 2022-09-06; http://www.rstudio.com/) on Windows 8.1 to handle this file. We used the dartR R-package (version 2.7.2) with necessary dependencies to import, proccess and analyse this data file as an object of a class genlight (dartR) in the R environment. You may also handle the file as an object of a class genlight using the adegenet and ade4 R-packages. To learn more about installation procedure and how to use of the R-packages visit: https://cran.r-project.org/web/packages/available_packages_by_name.html.

Temporal and spatial variation of zooplankton in relation to environmental variables in the upper Santos estuary, São Paulo, Brazil

Zooplankton samples were collected through oblique trawls with a 200 µm net with a flow meter during low tide, at 4 sampling sites with different degrees of salinity in the upper Santos estuary, in 28 campaigns carried out between 2014 and 2023, to analyze the spatial and temporal variations in the structure and abundance of zooplankton in relation to environmental variables using data from a long-term monitoring, in this way, contributing to the update of knowledge about the zooplankton community of the Santos estuary.

Dataset Mayen et al_Temporal variations of water carbon and atmospheric carbon dioxide fluxes in a temperate salt marsh and influence of aquatic metabolism

Open the record for dataset details and reuse information.

Strategic Integration of Urban Excess Heat Sources in District Heating: A Spatio-Temporal Optimization Paradigm

This data repository supports the techno-economic and geospatial optimization analysis of district heating systems (DHS) in Stockholm, with a particular focus on integrating Urban Excess Heat (UEH) sources. The dataset includes both input data and results, organized by the key analytical tools employed in the study: GIS, OSeMOSYS, and the Hotmaps Dispatch Model. The folder structure is organized as follows: /Data │ ├── Input_Data/ │   ├── GIS/ │   ├── OSeMOSYS/ │   ├── Hotmaps_Dispatch/ │ └── Results/     ├── GIS/     │     ├── Optimized DHS network extensions for UEH sources     │     ├── Long-term capacity and emissions projections for DHS           ├── Dispatch outputs for different electricity price scenarios <h2>Dataset Details</h2> 1. Input Data Folder: This folder includes all data used as inputs across the three primary tools:    - GIS: Contains geospatial data on the DHS network paths, source locations, and spatial configurations for network optimization.    - OSeMOSYS: Provides techno-economic parameters for heat generation technologies, including capital and operational costs, fuel price projections, and emissions factors.    - Hotmaps Dispatch: Includes detailed demand profiles, projected electricity prices, and the supply temperature requirements essential for dispatch simulations. 2. Results Folder: This folder contains the results generated by each tool after processing the input data:    - GIS: Outputs optimized paths for network expansion and geospatial mapping of UEH sources.    -OSeMOSYS: Contains projections for DHS technology adoption, emissions reduction, and cost optimization over a 23-year model period.    - Hotmaps Dispatch: Stores dispatch model results under various electricity pricing and temperature scenarios, capturing the optimal deployment of DHS resources. <h2>Summary of Tools Used</h2> - GIS (Geospatial Information System): Used to locate UEH sources and map their proximity to the existing DHS network, guiding the optimization of network expansions. - OSeMOSYS (Open Source Energy Modelling System): Performs long-term optimization for the DHS, factoring in the addition of UEH sources, electricity price scenarios, and emissions constraints. - Hotmaps Dispatch Model: Simulates operational dispatch of heat generation technologies in response to varying electricity prices and network temperatures, providing insights into real-time heat supply optimization. This repository thus provides a comprehensive dataset to support the techno-economic and spatial analysis of DHS, facilitating the integration of UEH sources in urban heating systems through scenario-based optimizations.

Aerial Images_Part 2_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria

Aerial Images_Part 2_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria

Aerial Images_Part 1_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria

Aerial Images_Part 1_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria

Dataset on Spatial Analysis and Clustering of Deforestation in the Amazon Biome: Spatio-Temporal Patterns and Priority Areas

The dataset was developed with the aim of facilitating the development of a methodology to identify and evaluate deforestation patterns and trends in the Amazon. This innovative method combines deforestation alerts from the Real-Time Deforestation Detection System (DETER) with detailed information on various land categories, including environmental protection areas, settlements, rural properties, undesignated public forests, indigenous lands, and conservation units. The integration of this robust data allowed for the precise identification of areas at risk of deforestation, significantly strengthening monitoring and control activities aimed at combating deforestation in the Amazon region.   Spatial resolution The data are available with a spatial resolution of 25 x 25 km (625 km²) and cover the Amazon biome.   Temporal resolution  Period of observed data: 2017 and 2021   Coordinate reference system  Geographic Coordinate System with Datum SIRGAS 2000 (EPSG:5880)   Data format Data is provided as Shapefile.   Dataset usage  It is free to use, but please make sure to cite the repository and our paper properly if you use this dataset.   Publication & further information For additional scenario information, please contact Francisco Gilney Silva Bezerra (franciscogilney@gmail.com).

opencc-by-4.0Apr 2024View details →

The role of spatial terms in time expressions: A case study of Chinese temporal words

This project provides the research data, namely all the Chinese temporal words collected from the BCC corpus for The role of spatial terms in time expressions: A case study of Chinese temporal words. There is one table here which includes all tokens taken from the BCC corpus. More specifically, it lists 692 unique Chinese temporal words along with their frequencies. All of these temporal words have been classified into three categories: explicitly spatialized temporal words, implicitly spatialized temporal words, and others. All explicitly spatialized temporal words have been annotated based on the type of spatial terms they are marked with, mainly including location/direction, motion, dimension, objects, deixis, and spatial mixtures.

opencc-by-4.0Dec 2023View details →

Supplementary: Spatial-Temporal Variation in Erosion Rate of Lohit Bomi-Chayu Batholith around Eastern Himalayan Syntaxis (SE of Namche Barwa massif)

It is supplementary material of Borgohain et al. (2025) in Tectonic journal and  contains: <ol> <li> Method descriptions (Low-temperature thermochronology, Bayesian erosion model, mixing model, mineral concentration factor, HeFTy thermal modeling, age2exhume thermal modeling, and topographic analysis). </li> <li> Dataset of detrital low-temperature thermochronology (AFT, ZFT), insitu low-temperature thermochronology (AFT, ZHe, ZFT, apatite track length). </li> <li> Modeling outputs. </li> <li> Sample codes and locations (latitude, longitude). </li> </ol>

opencc-by-4.0Jul 2024View details →

User evaluation results for a Wikidata-centric tool for temporal data in Humanities and Cultural Heritage (June 2024): raw tabular result data and web forms for two questionnaires from five online focus group workshops

Introduction This resource is created for the article: "Wikidata Visualization for Event and Temporal Data Exploration in Digital Humanities and Cultural Heritage" in Semantic Web Journal Special issue on the Semantic Web and Ontology Design for Cultural Heritage. It contains materials use for the user evaluation (June 2024) of a Wikidata visualization tool (<a title="ReKisstory" href="https://rekisstory.labs.vu.nl/" target="_blank" rel="noopener">ReKisstory</a>) described in the article.  Summary of the user evaluation The structrue of the user evalution is summarized in the table below: <table style="border-collapse: collapse; width: 99.9708%;"><colgroup><col style="width: 28.0622%;"><col style="width: 26.893%;"><col style="width: 27.0309%;"><col style="width: 17.9856%;"></colgroup> <tbody> <tr> <td> </td> <td>Objectives</td> <td>Setup  </td> <td># of people participating</td> </tr> <tr> <td>Pre-workshop survey</td> <td>Understanding potential user profiles before workshop</td> <td>Online questionnaire (Q1)</td> <td>51</td> </tr> <tr> <td>Workshop (Focus Group)</td> <td>Introducing and testing the tool. Obtaining feedback about it</td> <td>Online demo, testing, discussion</td> <td>16</td> </tr> <tr> <td>Post-workshop survey</td> <td>Understanding user needs after testing</td> <td>Online questionnaire (Q2)</td> <td>11</td> </tr> </tbody> </table>   The structure of the data is as follows: 1. The PDF and PPTX files, containing materials prepared for the pre-workshop survey, workshop, and post-workshop survey: <ul> <li>Pre-workshop survey: Questionnaire (Q1) screenshot from GoogleForms <a href="https://zenodo.org/records/14960584/files/1stQuestionnaire.pdf?download=1&preview=1">1stQuestionnaire.pdf</a></li> <li>Post-workshop survey: Questionnaire (Q2) screenshot from GoogleForms <a href="https://zenodo.org/records/14960584/files/2ndQuestionnaire.pdf?download=1&preview=1">2ndQuestionnaire.pdf</a></li> <li>Documents distributed to the participants of the Focus Group Workshop: <ul> <li>ReKisstory Compare section manual <a href="https://zenodo.org/records/14960584/files/rekisstory_compare_manual.pdf?download=1&preview=1">rekisstory_compare_manual.pdf</a></li> <li>ReKisstory Find section manual <a href="https://zenodo.org/records/14960584/files/rekisstory_find_manual.pdf?download=1&preview=1">rekisstory_find_manual.pdf</a></li> <li>ReKisstory Find example search patterns <a href="https://zenodo.org/records/14960584/files/rekisstory_group_search_example_search_patterns.pdf?download=1&preview=1">rekisstory_group_search_example_search_patterns.pdf</a></li> <li>Workshop slides <a href="https://zenodo.org/records/14960584/files/Focus_Group_Workshop_slides.pptx?download=1&preview=1">Focus_Group_Workshop_slides.pptx</a></li> </ul> </li> </ul> 2. The Excel spreadsheets consists of the results of two questionnaires (Q1 and Q2) (<a href="https://zenodo.org/records/14960584/files/Two_questionnaires_online_workshops.xlsx?download=1&preview=1">Two_questionnaires_online_workshops.xlsx</a>):  <ul> <li>Pre-workshop survey: Questionnaire (Q1), containing information about potential users: <ul> <li>Demographics</li> <li>Experience of <ul> <li>Time-related data</li> <li>Wikidata</li> <li>SPARQL</li> </ul> </li> </ul> </li> <li>Post-workshop survey: Questionnaire (Q2), containing information about feedback from the Workshop participants : <ul> <li>Comparison with Q1</li> <li>Questions about time-related functionalities</li> <li>Questions about Compare and Find searches</li> <li>Overall comment</li> </ul> </li> </ul> Please see "Wikidata Visualization for Event and Temporal Data Exploration in Digital Humanities and Cultural Heritage" for more details

opencc-by-4.0Sep 2024View details →

Data and models for "Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery" by Lagerquist et al.

The file geocenter_models.tar contains all models comprising the GeoCenter ensemble: 3 convolutional neural networks (CNN), 3 isotonic-regression files (one for correcting each CNN’s mean estimate), and 3 more isotonic-regression files (one for correcting each CNN’s ensemble spread). Every model is found in a subdirectory whose names indicate which infrared (IR) wavelengths are used as input to the CNN. For example: <ul> <li> wavelengths-microns=3.900-7.340-13.300/model.weights.h5: An HDF5 file containing the trained CNN that uses data from bands 7, 10, 16 (corresponding to 3.9, 7.34, and 13.3 microns on the GOES ABI imager). The trained CNN can always be read by neural_net_utils.read_model() in the ml4tccf library (https://doi.org/10.5281/zenodo.15116854). </li> <li> wavelengths-microns=3.900-7.340-13.300/model_metadata.p: A Pickle file containing metadata for the trained CNN. This file is needed to read the CNN itself with neural_net_utils.read_model(). Otherwise, you will probably never need to access this metafile directly. </li> <li> wavelengths-microns=3.900-7.340-13.300/isotonic_regression/isotonic_regression.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble mean from the same CNN. The trained isotonic-regression models can always be read by scalar_isotonic_regression.read_file() in the ml4tccf library. Note that there are technically two isotonic-regression models for every CNN’s ensemble mean: one that bias-corrects the x-coordinate of the TC-center, another that bias-corrects the y-coordinate. </li> <li> wavelengths-microns=3.900-7.340-13.300/uncertainty_calibration/uncertainty_calibration.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble spread from the same CNN. In the ml4tccf code, I make a distinction between “isotonic_regression” (correcting the ensemble mean) and “uncertainty_calibration” (correcting the ensemble spread), but note that both models are isotonic regression and use the sklearn.isotonic.IsotonicRegression class. The trained uncertainty-calibration models can always be read by scalar_uncertainty_calibration.read_file() in the ml4tccf library. Again, note that there are technically two uncertainty-calibration models per CNN: one for spread in the x-coordinate, one for spread in the y-coordinate. </li> </ul>   As mentioned above, every trained CNN can be read by neural_net_utils.read_model(). Also, every trained CNN can be applied to new data (inference mode) by neural_net_utils.apply_model(). The input argument model_object should be the object returned by neural_net_utils.read_model(), and I suggest setting num_examples_per_batch = 10 to avoid out-of-memory errors. The only other input argument is predictor_matrices, which is a list of two numpy arrays. The first numpy array contains IR imagery centered at the first-guess TC center, and the second numpy array contains ATCF scalars. The first numpy array should have dimensions S (number of TC samples) x 300 (grid rows) x 300 (grid columns) x 9 (lag times) x 3 (wavelengths). Lag times should be in the following order: 240, 210, 180, 150, 120, 90, 60, 30, 0 min ago.  Wavelengths should be in the order indicated by the subdirectory name.  The numpy array itself should contain normalized brightness temperatures at the given lag times and wavelengths, following the grid specifications laid out in the journal paper (a plate carrée grid with 2-km spacing). The original IR data (brightness temperatures) must be normalized to z-scores using the same normalization parameters as in the journal paper, i.e., those based on the training data. See details below. The second numpy array in predictor_matrices should have dimensions S (number of TC samples) x 9 (variables). The variables must in the order: absolute latitude, cosine of longitude, sine of longitude, TC intensity, minimum central pressure, tropical flag, subtropical flag, extratropical flag, disturbance flag. The journal paper contains details on all these variables in one table. These variables must come from A-deck files at the second-most recent synoptic time. Like the IR data, these ATCF scalars must be normalized to z-scores using the same normalization parameters as in the journal paper. See details below.   Once you have predictions (estimated TC-center locations) from a CNN, you can bias-correct these predictions. To read the isotonic-regression model for the given CNN’s ensemble mean, use scalar_isotonic_regression.read_file() in the ml4tccf library. To apply the same model, use scalar_isotonic_regression.apply_models(). For the CNN’s ensemble spread, use scalar_uncertainty_calibration.read_file() and scalar_uncertainty_calibration.apply_models().   To normalize the IR data, you will need the file ir_satellite_normalization_params.tar included with this dataset. Within the tar file is a single zarr file. You can read the zarr file with normalization.read_file() in the ml4tccf library; then you can normalize new data with normalization.normalize_data().   To normalize the ATCF data, you will need the file a_deck_normalization_params.nc included with this dataset. This is a NetCDF file, containing the full set of training values for all 5 ATCF variables that are normalized (the binary storm-type flags are not normalized). You can read this file using any of the standard Python methods for reading NetCDF files, such as xarray.open_dataset(). To normalize new ATCF data, you can use the method normalization._normalize_one_variable(), where the argument actual_values_training is the list of training values from a_deck_normalization_params.nc for the given variable, while actual_values_new is the list of values to be normalized (currently in physical units, to be converted to z-score units).

opencc-zeroNov 2021View details →

The invasion paradox dissolves when using phylogenetic and temporal perspectives

1. Elton's prediction that higher biodiversity leads to denser niche-packing and thus higher community resistance to invasion has long been studied, with species richness as the predominant measure of diversity. However, few studies have explored how phylogenetic and functional diversity, which should represent niche space more faithfully than taxonomic diversity, influence community invasibility, especially across longer time frames and over larger spatial extents. 2. We used a 15-year, 150-site grassland dataset to assess relationships between invasive plant abundance and phylogenetic, functional, and taxonomic diversity of recipient native plant communities. We analyzed the dataset both pooled across all surveys and longitudinally, leveraging time-series data to compare observed patterns in invasion with those predicted by two community assembly processes: biotic resistance and competitive exclusion. We expected more phylogenetically and functionally diverse communities to exhibit greater resistance to invasion. 3. With the pooled dataset, we found support for the longstanding observation that communities with more native species have lower abundance of invasive species, and a more novel finding that more phylogenetically diverse communities had higher abundance of invasive species. We found no influence of aggregate (multivariate) functional diversity on invasion, but assemblages with taller plants, lower variability in plant height, and lower seed mass were less invaded. Viewed longitudinally, the phylogenetic diversity relationship was reversed: the most phylogenetically diverse communities were most resistant to invasion. This apparent discrepancy suggests invasion dynamics are influenced by both site attributes and biotic resistance and emphasizes the value in studying invasion across time. 4. Synthesis: Our results provide insight into the nuances of the diversity-invasibility relationship: invasion dynamics differed for different dimensions of diversity and depending on whether the relationship was evaluated longitudinally. Our findings highlight the limitations of using single time-point "snapshots" of community composition to infer invasion mechanisms.

opencc-zeroNov 2021View details →

Temporal and spatial variation in sex-specific abundance of the avian vampire fly (Philornis downsi)

Understanding the range and behaviour of an invasive species is critical to identify key habitat areas to focus control efforts. Patterns of range use in parasites can differ temporally, across life stages and between sexes. The invasive avian vampire fly, Philornis downsi, spends the larval stage of its life within bird nests, feeding on developing nestlings and causing high levels of mortality and deformation. However, little is known of the ecology and behaviour of the non-parasitic adult fly life stage. Here, we document sex-specific temporal and spatial patterns of abundance of adult avian vampire flies during a single Darwin's finch breeding season. We analyse fly trapping data collected across 7 weeks in the highlands (N = 405 flies) and lowlands (N = 12 flies) of Floreana Island (Galápagos). Lowland catches occurred later in the season, which supports the hypothesis that flies may migrate from the food-rich highlands to the food-poor lowlands once host breeding has commenced. Fly abundance was not correlated with host nesting density (oviposition site) but was correlated with distance to the agricultural zone (feeding site). We consistently caught more males closer to the agricultural zone and more females further away from the agricultural zone. These sex differences suggest that males may be defending or lekking at feeding sites in the agricultural zone for mating. This temporal and sex-specific habitat use of the avian vampire fly is relevant for developing targeted control methods and provides insight into the behavioural ecology of this introduced parasite on the Galápagos Archipelago.

opencc-by-4.0Dec 2021View details →

Self-Limiting Earthquake Dynamics and Spatio-Temporal Clustering of Seismicity Enabled by Off-Fault Plasticity

Earthquakes are among nature’s deadliest and costliest hazards. Physics-based simulations are essential for overcoming the lack of data and elucidating the complex patterns of earthquakes. Enabled by a novel numerical scheme, this work discovers a new mechanism for regulating earthquake dynamics that emerges due to the co-evolution of fault slip and fault zone plasticity. It enables transition from periodic events to fully irregular sequences of earthquakes. The impact of plasticity on earthquake source characteristics goes beyond its limited contribution to the overall energy budget, emphasized in earlier studies, to underscore its crucial role on the redistribution of stresses that self-limits earthquake growth and leads to clustering of seismicity. This work highlights the need for characterizing the fault zone mechanical response beyond their elastic properties to better inform seismic hazard models.

opencc-zeroDec 2021View details →

Ripples reflect a spectrum of synchronous spiking activity in human anterior temporal lobe

Direct brain recordings have provided important insights into how high-frequency activity captured through intracranial EEG (iEEG) supports human memory retrieval. The extent to which such activity is comprised of transient fluctuations that reflect the dynamic coordination of underlying neurons, however, remains unclear. Here, we simultaneously record iEEG, local field potential (LFP), and single unit activity in the human temporal cortex. We demonstrate that fast oscillations within the previously identified 80–120 Hz ripple band contribute to broadband high-frequency activity in the human cortex. These ripple oscillations exhibit a spectrum of amplitudes and durations related to the amount of underlying neuronal spiking. Ripples in the macro-scale iEEG are related to the number and synchrony of ripples in the micro-scale LFP, which in turn are related to the synchrony of neuronal spiking. Our data suggest that neural activity in the human temporal lobe is organized into transient bouts of ripple oscillations that reflect underlying bursts of spiking activity.

opencc-zeroJan 2022View details →

Nectar values from: Turnover in floral composition explains species diversity and temporal stability in the nectar supply of urban residential gardens

Residential gardens are a valuable habitat for insect pollinators worldwide, but differences in individual gardening practices substantially affect their floral composition. It is important to understand how the floral resource supply of gardens varies in both space and time so we can develop evidence-based management recommendations to support pollinator conservation in towns and cities. We surveyed 59 residential gardens in the city of Bristol, UK, at monthly intervals from March to October. For each of 472 garden surveys, we combined floral abundances with nectar sugar data to quantify the nectar production of each garden, investigating the magnitude, temporal stability, and diversity and composition of garden nectar supplies. We found that individual gardens differ markedly in the quantity of nectar sugar they supply (from 2 g to 1662 g), and nectar production is higher in more affluent neighbourhoods, but not in larger gardens. Nectar supply peaks in July (mid-summer), when more plant taxa are in flower, but temporal patterns vary among individual gardens. At larger spatial scales, temporal variability averages out through the portfolio effect, meaning insect pollinators foraging across many gardens in urban landscapes have access to a relatively stable and continuous supply of nectar through the year. Turnover in species composition among gardens leads to an extremely high overall plant richness, with 636 taxa recorded flowering. The nectar supply is dominated by non-natives, which provide 91% of all nectar sugar, while shrubs are the main plant life form contributing to nectar production (58%). Two thirds of nectar sugar is only available to relatively specialised pollinators, leaving just one third that is accessible to all. Synthesis and applications. By measuring nectar supply in residential gardens, our study demonstrates that pollinator-friendly management, affecting garden quality, is more important than the size of a garden, giving every gardener an opportunity to contribute to pollinator conservation in urban areas. For gardeners interested in increasing the value of their land to foraging pollinators we recommend planting nectar-rich shrubs with complementary flowering periods and prioritising flowers with an open structure in late summer and autumn.

opencc-zeroJan 2022View details →

Data from: Turnover in floral composition explains species diversity and temporal stability in the nectar supply of urban residential gardens

opencc-zeroJan 2022View details →

Temporal consistency in interactions among birds, ants, and plants in a neotropical savanna

Interactions among animals and plants are key to understanding seed dispersal, plant regeneration and plant community patterns. These interactions can be dynamic, with changes of species and functional roles across space and time. Despite fluctuations in species abundances and resources over time being the rule in natural communities, most studies approach plant-frugivore interactions as temporally static. We documented changes in bird and ant species in interactions with plants producing carbohydrate- (Miconia rubiginosa) or lipid-rich diaspores (Xylopia aromatica) comparing two fruiting seasons 15 years apart in a savanna in southeastern Brazil. We tested if abundance and body size of dispersal vectors (birds and ants) affected the frequency of interactions and the quantitative effectiveness of seed dispersal (QTC). Rich bird (26 species) and ant (18 genera) assemblages interacted with the diaspores. A core of bird and ant taxa was temporally consistent, and responsible for most seed removal across the two years for both plants. Temporal transience was more common for sporadic partners of interactions, and for species with similar functional roles. Abundance and body size of birds affected the interspecific contributions to QTC. Large-bodied birds dispersed large quantities of seeds of our relatively small-seeded plants, even if their visits were sporadic. For ants, variations in temperature and rainfall across time seems more important in driving variations in the contributions to QTC. The combined effect of birds and ants in the same system help to stabilize the temporal fluctuations in the amount of seeds dispersed. However, ants alone are unlikely to replace the functional roles of vertebrate dispersers. Even though species in the assemblage change either their quantitative contribution to seed dispersal or their presence or absence in the interaction network, species persisting in interaction over time are the most important quantitative partners for plants.

opencc-by-4.0Jan 2022View details →

Large-scale temporal graph datasets

Large scale datasets used for evaluation in the article: `Optimizing the Interval-centric Distributed Computing Model for Temporal Graph Algorithms` to appear in `EuroSys 2022`.