Find research datasets worth reusing

In-situ monitoring of the Seine River based on the MeSeine network

In Europe, the management of freshwater ecosystem is governed by the Water Framework Directive (2000/60/CE) and its transposing legislation in France (2006-1772 of December 2006). The good ecological status of water are evaluated using a combination of several indicators such as biological and physico-chemical parameters. The Seine River crosses several important urbanized areas of France, including the Parisian conurbation (9 millions inhabitants). To ensure the good ecological status of the Seine River, the Greater Paris Conurbation Sanitation Authority (SIAAP), has constructed and operated the MeSeine network since 1990. MeSeine constitutes a tool for evaluating the quality of the Seine river and its tributaries (Marne, Oise) in terms of physico-chemistry, bacteriology, micro-contamination and faunal diversity. The MeSeine network extends along 125 km of the Seine River (from Choisy to Méricourt) and over 13 km along the Marne River (frome Champigny to Alfortville). It is structured around tree pillars: <ul> <li>Real time monitoring of the physico-chemical composition of the Seine river using in situ sensor, in particular dissolved oxygen and temperature sensors</li> <li>Sampling and laboratory analysis campaigns to monitor watercourses quality and comply with the quality standards as defined by the Water Framework Directive (good ecological and chemical parameters)</li> <li>Biota monitoring to appreciate the diversity of fish populations, macro-invertebrates and diatoms.</li> </ul> The aim of this work is to provide dissolved oxygen and temperature data of the Seine, generated by the in-situ sensors of the MeSeine network, via the open platform Zenodo.

Monitoring of the physico-chemical composition of the Seine River based on the MeSeine network

In Europe, the management of freshwater ecosystem is governed by the Water Framework Directive (2000/60/CE) and its transposing legislation in France (2006-1772 of December 2006). The good ecological status of water are evaluated using a combination of several indicators such as biological and physico-chemical parameters. The Seine River crosses several important urbanized areas of France, including the Parisian conurbation (9 millions inhabitants). To ensure the good ecological status of the Seine River, the Greater Paris Conurbation Sanitation Authority (SIAAP), has constructed and operated the MeSeine network since 1990. MeSeine constitutes a tool for evaluating the quality of the Seine river and its tributaries (Marne, Oise) in terms of physico-chemistry, bacteriology, micro-contamination and faunal diversity. The MeSeine network extends along 125 km of the Seine River (from Choisy to Méricourt) and over 13 km along the Marne River (frome Champigny to Alfortville). It is structured around tree pillars: <ul> <li>Real time monitoring of the physico-chemical composition of the Seine river using in situ sensor, in particular dissolved oxygen and temperature sensors</li> <li>Sampling and laboratory analysis campaigns to monitor watercourses quality and comply with the quality standards as defined by the Water Framework Directive (good ecological and chemical parameters)</li> <li>Biota monitoring to appreciate the diversity of fish populations, macro-invertebrates and diatoms.</li> </ul> The aim of this work is to provide data on the physico-chemical quality of the Seine generated by the MeSeine observatory via the open platform Zenodo.

Brassinosteroid gene regulatory networks at cellular resolution in the Arabidopsis root

Supplemental datasets for "Brassinosteroid gene regulatory networks at cellular resolution in the Arabidopsis root".

Supplementary Material to "Analysis of nationwide groundwater monitoring networks using lumped-parameter models and groundwater"

This repository contains the Supplementary Material to "Analysis of nationwide groundwater monitoring networks using lumped-parameter models and groundwater", submitted to Journal of Hydrology for review.

opencc-by-4.0Jul 2022View details →

Data for: Reconstructing Cosmological Initial Conditions from Late-Time Structure with Convolutional Neural Networks

Trained models and evaluation data for the revised submitted paper "Reconstructing Cosmological Initial Conditions from Late-Time Structure with Convolutional Neural Networks," Christopher J. Shallue & Daniel J. Eisenstein (2022)

Artifacts for the IEEE Internet of Things Journal Publication: Specification-based Symbolic Execution for Stateful Network Protocol Implementations in the IoT

Artifacts for the evaluation of the publication Specification-based Symbolic Execution for Stateful Network Protocol Implementations in the IoT which will be published in the IEEE Internet of Things journal. More information is available in the provided README.md file.

Dataset for: Indirect nitrous oxide emission factors of fluvial networks can be predicted by dissolved organic carbon and nitrate from local to global scales

Streams and rivers are important sources of nitrous oxide (N2O), a powerful greenhouse gas. Estimating global riverine N2O emissions is critical for the assessment of anthropogenic N2O emission inventories. The indirect N2O emission factor (EF5r) model, one of the bottom-up approaches, adopts a fixed EF5r value to estimate riverine N2O emissions based on IPCC methodology. However, the estimates have considerable uncertainty due to the large spatiotemporal variations in EF5r values. Factors regulating EF5r are poorly understood at the global scale. Here, we combine 4-year in situ observations across rivers of different land use types in China, with a global meta-analysis over six continents, to explore the spatiotemporal variations and controls on EF5r values. Our results show that the EF5r values in China and other regions with high N loads are lower than those for regions with lower N loads. Although the global mean EF5r value is comparable to the IPCC default value, the global EF5r values are highly skewed with large variations, indicating that adopting region-specific EF5r values rather than revising the fixed default value is more appropriate for the estimation of regional and global riverine N2O emissions. The ratio of dissolved organic carbon to nitrate (DOC/NO3-) and NO3- concentration are identified as the dominant predictors of region-specific EF5r values at both regional and global scales because stoichiometry and nutrients strictly regulate denitrification and N2O production efficiency in rivers. A multiple linear regression model using DOC/NO3- and NO3- is proposed to predict region-specific EF5r values. The good fit of the model associated with easily obtained water quality variables allows its widespread application. This study fills a key knowledge gap in predicting region-specific EF5r values at the global scale and provides a pathway to estimate global riverine N2O emissions more accurately based on IPCC methodology. This dataset is a global integrated N2O dataset including data from 4-year (2017-2020) in situ measurements of six large rivers in China, 3-year (2018-2020) in situ measurements of urban river networks in Beijing of China, and 825 measurements from 70 published papers over six continents. The data includes dissolved N2O concentration, biogeochemical (DOC, NO3-, NH4+, temperature, and DO), climatological (climate zones), and geographic (region, location, and land cover) information.

Successional and phenological effects on plant-floral visitor interaction networks of a tropical dry forest

1. Plant-pollinator interactions are fundamental to ecosystem functioning; however, the role that succession and phenology have on these interactions is poorly understood, particularly in endangered tropical ecosystems. In highly diverse ecosystems such as tropical dry forests (TDF), variation in water and food availability determines the life cycles of animal pollinators. Therefore, understanding patterns of flowering phenology and plant-pollinator interactions across seasons in successional environments is key to maintaining and restoring TDF. 2. We analysed the functional dynamics of plant-floral visitor interactions at the community level across a successional gradient in a Mexican TDF. We evaluated changes in the diversity of blooming plant species and floral visitors, phenological patterns, interaction network metrics, and beta diversity among early, intermediate, and late successional stages, between dry and rainy seasons. 3. We found a higher diversity of blooming plant species and a higher richness of animal species in the intermediate and late successional stages. Peak abundance of floral visitors overlapped with flowering peaks in the late successional stages, but this was not consistently the case in the early and intermediate stages. Plant-floral visitors networks differed in structure according to successional stage and season, but specialisation metrics were higher in late successional stages. Interaction networks were more dissimilar between dry and rainy seasons within successional stages than within seasons between successional stages, suggesting connectivity across successional sites during each season. In addition, closely related plant species do not share the same pollination systems in any successional stage. 4. Synthesis. Our results showed that plant-floral visitor interactions are dynamic and vary with flowering phenology and with successional changes in plant and animal diversity. Plant-floral visitor interactions were more diverse and specialised in the late successional stages. In the rainy season, differences in network structure among successional stages are due to interaction rewiring, while in the dry season, it is caused by species turnover. Our results demonstrate that seasonality plays a key role in community diversity and network structure and highlight the importance of conserving mature forests to ensure the maintenance of critical pollination interactions across all successional stages.

Chemical Networks and Model Output and for "Evidence of Photochemistry in an Exoplanet Atmosphere"

The volume mixing ratio output of the key sulphur species computed by photochemical models for producing Fig. 1  Synthetic spectra in Fig. 2 The photochemical networks used in each model.

DeepGeoStat WP5 Solar Panel Trained Network

This dataset contains the weights of a convolutional neural network (CNN) trained to recognize the presence of solar panels on aerial photos. In particular, it contains the saved state of a ResNet50 CNN that has been trained on a dataset containing annotated high-resolution aerial images of two regions in the south of the Netherlands. Many photos in this dataset have been annotated multiple times, and the annotations are not always unanimous. The dataset of aerial images together with annotations can be downloaded from <a href="https://zenodo.org/record/7551799#.Y85W5pKZO3A">here</a>. The model for detecting whether solar panels are present in aerial photos has been developed under the DeepSolaris and DeepGeoStat projects. Corresponding Pytorch code can be found <a href="https://gitlab.com/CBDS/deepgeostat-wp-5">here</a>. The code also demonstrates how to load the saved state into a ResNet50 model, and use it for detecting solar panels on aerial photos. This research was conducted under: <ul> <li>ESS action 'Merging Geostatistics and Geospatial Information in Member States' (grant agreement no.: 08143.2017.001-2017.408),</li> <li> ESS topic B5674-2020-GEOS (project 101033951 2020-NL-GEOS-DEEP-GEO-STAT),</li> <li>a research program of Statistics Netherlands (https://www.cbs.nl)</li> </ul>

Strain‐dependent differences in coordination of yeast signalling networks

The yeast mitogen activated protein kinase pathways serve as a model system for understanding how network interactions affect the way in which cells coordinate the response to multiple signals. We have quantitatively compared two yeast strain backgrounds YPH499 and Σ1278b (both of which have previously been used to study these pathways) and found several important differences in how they coordinate the interaction between the high osmolarity glycerol (HOG) and mating pathways. In the Σ1278b background, in response to simultaneous stimulus, mating pathway activation is dampened and delayed in a dose dependent manner. In the YPH499 background, only dampening is dose dependent. Further, leakage from the HOG pathway into the mating pathway (crosstalk) occurs during osmostress alone in the Σ1278b background only. The mitogen activated protein kinase Hog1p suppresses crosstalk late in an induction time course in both strains but does not affect the early crosstalk seen in the Σ1278b background. Finally, the kinase Rck2p plays a greater role suppressing late crosstalk in the Σ1278b background than in the YPH499 background. Our results demonstrate that comparisons between laboratory yeast strains provide an important resource for understanding how signaling network interactions are tuned by genetic variation without significant alteration to network structure.

Dataset for 4-clique network minor embedding for quantum annealers

Dataset for the paper titled 4-clique network minor embedding for quantum annealers. Includes raw D-Wave quantum annealer samples and minor embedding graph structures.

Shared predators between primate groups and mixed species bird flocks: The potential for forest-wide eavesdropping networks

A basic tenet of animal behavior is that animal groupings (e.g., schools of fishes or flocks of birds) are widely influenced by predators. Many studies have focused on communication between individuals within the same species or different species within a defined social group; but predators typically select from a number of different co-occurring species. To evaluate whether two distantly-related species with similar predators share vocal information regarding predator threats, we conducted a field experiment in the Amazonian rainforest involving an avian prey-species, a primate prey-species, and a shared predator. In our reciprocal field experiment, we elicited alarm calls from birds (Bluish-slate antshrikes, Thamnomanes schistogynus) and primates (Saddle-backed tamarins, Saguinus fuscicollis) by exposing them to a trained raptor (Bicolored Hawk, Accipiter bicolor). We then played all types of recorded alarm calls back to birds and tamarins, and measured 1) the time to respond (for both birds and tamarins), and 2) the distance moved across the substrate (for tamarins). Our results show that both birds and tamarins were significantly more likely to flee when hearing vocal alarms compared to a control (a common bird call, the Screaming Piha, Lipaugus vociferans), regardless of the species who produced the alarm. In addition, tamarins moved significantly more upon hearing bird alarm calls when compared to the control. We suggest that signals regarding shared predators may be highly valued across prey from distinct social groups. These data support the hypothesis that overlapping potential predators can drive communication between distinct prey groups, resulting in taxonomically diverse eavesdropping networks within tropical rainforests.

PAN EUROPEAN RAIL NETWORK - Rail Baltica

Rail Baltica demonstrator focus on deployment of autonomous drone flight command and controlled exclusively through cellular network. A drone as a service concept will be elaborated and demonstrated to be further incorporated in routine monitoring of Pan European railway infrastructure construction and exploitation stages.

Functional and taxonomic diversity indices of riparian plants in river networks

1. The River Continuum Concept (RCC) predicts a gradual shift of organisms' functional adaptations along the longitudinal (upstream-downstream) gradient, as well as the maximization of the biotic diversity in mid-reaches. Although this theoretical framework was originally developed for stream macroinvertebrates, we tested whether such a pattern can be also observed in riparian plant communities. 2. We studied the cover of plant species in riparian forests across two river networks. We analyzed the taxonomic and functional diversity indices, as well as community-weighted means of functional traits in relation to the plots' position in the catchments. 3. The observed patterns were largely in line with the predictions of RCC. We discovered a significant decrease in the specific leaf area and an increase in the herbaceous plants' height in communities along a river gradient. There was also a shift in the dispersal syndromes, towards a higher importance of zoochory in the lower reaches. 4. The functional richness and divergence displayed unimodal patterns of increasing values in the mid-reaches. The patterns of taxonomic diversity were similar, but some plots in the lowest reaches were more diverse than expected, forming an additional increase in diversity. 5. The study shows that plant communities in natural riparian forests show high connectivity along the longitudinal gradient, which along with the environmental gradients creates patterns that are known from theoretical predictions.

Dataset1 for "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian"

Supporting data for the paper "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian".  Contains atomic structures and Hamiltonian matrices of monolayer graphene, monolayer MoS2, bilayer graphene and bilayer bismuthene. Detailed descriptions about the format of data and instructions on how to reproduce the results in the paper can be found in README.md.

Dataset2 for "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian"

Supporting data for the paper "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian".  Contains atomic structures and Hamiltonian matrices of bilayer bismuth selenide.  Detailed descriptions about the format of data and instructions on how to reproduce the results in the paper can be found in README.md.

Dataset3 for "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian"

Supporting data for the paper "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian".  Contains atomic structures and Hamiltonian matrices of bilayer bismuth telluride.  Detailed descriptions about the format of data and instructions on how to reproduce the results in the paper can be found in README.md.

opencc-by-4.0Feb 2023View details →

Exploring Korean adolescent stress on social media: A semantic network analysis

Korean Adolescent's Stress Semantic Network Analysis Project Semantic Network Analysis for Korean Adolescent's Stress Input data file <ul> <li>data_news.csv : News data collected from Naver(<a href="https://www.naver.com">https://www.naver.com</a>)</li> <li>data_blog.csv : Blog data collected from Naver(<a href="https://www.naver.com">https://www.naver.com</a>) and Daum(<a href="https://www.daum.net">https://www.daum.net</a>)</li> </ul> Output files <ul> <li>Frequency Table of Each word in Documents (freq_news.csv, freq_blog.csv)</li> <li>TF-IDF(Term Frequency-Inverse Document Frequency) Table of Each word in Documents (tfidf_news.csv, tf_idf_blog.csv)</li> <li>Frequency Table of 30 keywords in Documents (freq_news_30.csv, freq_blog_30.csv)</li> <li>DTM(Document Term Matrix) of 30 keywords in Documents (DTM_news_30.csv, DTM_blog_30.csv)</li> <li>COM(Co-Occurrence Matrix) of 30 keywords in Documents (COM_news_30.csv, COM_blog_30.csv)</li> <li>Binary COM of 30 keywords in Documents (BinaryCOM_news_30.csv, BinaryCOM_blog_30.csv)</li> <li>Centrality Table of 30 keywords in Documents (centrality_news_30.csv, centrality_blog_30.csv)</li> </ul>