Find research datasets worth reusing

opencc-by-4.0Jul 2023View details →

Phables v 1.1.0 benchmarking data and results

This record contains all the benchmarking datasets and results for the Phables manuscript. The datasets used are, <ol> <li>A simulated dataset containing four phages Enterobacteria phage P22, Enterobacteria phage T7, Staphylococcus phage SAP13 TA-2022 and Staphylococcus phage SAP2 TA-2022, referred to as simPhage</li> <li>Water samples from Nansi Lake and Dongping Lake in n Shandong Province, China (NCBI BioProject number PRJNA756429), referred to as Lake Water</li> <li>Soil samples from flooded paddy fields from Hunan Province, China (NCBI BioProject number PRJNA866269), referred to as Paddy Soil</li> <li>Wastewater virome (NCBI BioProject number PRJNA434744), referred to as Wastewater</li> <li>Stool samples from patients with IBD and their healthy household controls (NCBI BioProject number PRJEB7772), referred to as IBD</li> </ol> The simulated dataset was assembled using metaSPAdes and the real datasets were preprocessed using Hecatomb. The Lake Water dataset was also assembled using MEGAHIT (referred to as Lake Water - MEGAHIT) and metaSPAdes (referred to as Lake Water - metaSPAdes). The simulated dataset was run using Phables and evaluated using metaQUAST. All the real datasets were run using PHAMB and Phables and the genomes were evaluated using CheckV.

opencc-by-4.0Jul 2023View details →

Maps and Validation result for LULC map of Pakyong sub-division, East Sikkim

The repository contains the maps and images pertaining to the study area and the validation results.

opencc-by-4.0Jul 2023View details →

Experimental Results for "A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models"

This package contains the raw data / logs (fetched from WandB) for the experiments of the following publication: O. Arenz, P. Dahlinger, Z. Ye, M. Volpp, and G. Neumann. A unified perspective on natural gradient variational inference with gaussian mixture models. Transactions on Machine Learning Research, 2023. URL: <a href="https://openreview.net/forum?id=tLBjsX4tjs">https://openreview.net/forum?id=tLBjsX4tjs</a>.

Results of a Survey on Obstacle Resolving Model Data

This publication includes the results of the online survey "Obstacle resolving model data survey"  which was available via https://www.uhh.de/orm-survey until June 2022. The data was collected between October 2019 and June 2022. The survey was created with the survey tool limesurvey (https://www.limesurvey.org/). The aim of the survey was to collect information on obstacle-resolving atmospheric models and their data, targeting the modeler community. As a part of the project AtMoDat (www.atmodat.de (https://www.atmodat.de/)) we invited the obstacle-resolving modeling community to contribute with their experience with obstacle-resolving models (ORM) and its model data, what models were used and how the user handles the data. We want to develop a data standard for obstacle-resolving model data, which shall be similar to existing standards. Therefore we need some information about the use and processing of model data. This survey asks about the usage of different obstacle-resolving models and/or outputs.  The survey is divided into two parts, the first part contains questions about model properties and the second part contains questions about how the participants handle model data.

Annotated corpus samples of resultative nomination constructions in 4 languages (Dutch, English, French, Spanish)

This dataset includes an annotated corpus sample retrieved from Sketch Engine of 3200 (i.e. 800x4) resultative constructions in 4 languages (Dutch, English, French, Spanish) with 16 (i.e. 4x4) different nomination verbs (Dutch: kronen 'crown', verkiezen 'elect', promoveren 'promote', uitroepen 'proclaim; English: crown, elect, promote, proclaim; French: couronner 'crown', élire 'elect', promouvoir 'promote', proclamer 'proclaim'; Spanish: coronar 'crown', elegir 'elect', ascender 'promote', proclamar 'proclaim').

QComp LRA results

It contains the benchmarks and the results of comparison between STORM, PET and MultiGain for the QComp category of Long-Run Average rewards.

Results of the differential gene expression analysis in SIV infection in Chlorocebus sabaeus and Macaca mulatta

Results of differential gene expression analysis in SIV infection in Chlorocebus sabaeus and Macaca mulatta. From the transcriptome data repository MACE (http://mace.ihes.fr)

Results of the poll in the study "Information Scientists' Motivations for Research Data Sharing and Reuse"

This is a dataset with results of the poll conducted in the study “Information Scientists’ Motivations for Research Data Sharing and Reuse”. In terms of the Uses and Gratifications Theory (Questions 1 and 2), the most popular uses relate to the categories of research support and information. Researchers share, or would share, their research data in general for any reusability purposes and especially for combination of different datasets to produce new evidence. Also, the vast majority of study participants associate research data sharing with possibilities to accelerate scientific progress and to increase research efficiency. In case of research data reuse, all the researchers indicated that they use, or would use, others’ data first of all for inspiration. Interestingly, study participants put relatively high the category of recognition in case of sharing, but at the same time they do not associate increased recognition among colleagues and other researchers with research data reuse. The remaining categories belonging to the categories of self-esteem and social interaction, i.e. increased citation level and visibility of the research as well as enhanced scientific reputation, possible cooperations and co-authorship, were selected only by few respondents. Also remarkably, data reuse is more frequently linked to entertainment then data sharing.  In terms of the Self-Determination Theory (Questions 3 and 4), all but one of the interviewees indicated that they have shared or would share their research data because it can accelerate scientific progress which they consider important and would like to contribute to it (i.e., identified regulation). The second most popular motivation turned out to be the obligation by employer, project funder and/or journals (i.e., external regulation). The third most popular option was social influence, i.e. because many other researchers participate in data sharing and they feel obligated to do the same (i.e., external regulation).This way, the participants demonstrate a mixture of identified motivation and external regulation, both material and social. In the case of data reuse, the participants demonstrate more homogeneous results with identification and intrinsic motivation having most of the votes. The role of external regulation seems to be much less important as in the case with data sharing. So, researchers reuse, or would reuse, research data because it can accelerate scientific progress which is important for them. Additionally, researchers enjoy exploring and using third party research data. Thus, interviewees participate or would participate in data sharing because they consider it important, but also feel or are obliged to do so. At the same time, study participants do not feel pressure from outside when deciding whether to reuse data or not. For more information about the study and its results, please read the article “Information Scientists’ Motivations for Research Data Sharing and Reuse” by Shutsko and Stock (2023).

Results and log of LLM-KG-Bench runs described in article "Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering", Meyer et al. 2023

Results and logs of <a href="https://github.com/AKSW/LLM-KG-Bench">LLM-KG-Bench</a> runs described in article "Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering", Meyer et al., to appear in <a href="https://2023-eu.semantics.cc/page/accepted_posters">SEMANTICS 2023 poster track</a> proceedings.

LPJ-GUESS model results for parametrizing the CLASH model

Results from the LPJ-GUESS model to parametize the CLASH model, as used in the manuscript 'CLASH - Climate-responsive Land Allocation model with carbon Storage and Harvests' The dataset contains netCDF files for four sets of runs (cropland, pastures, forests and LUH2 land-use). In each of these the LPJ-GUESS model is driven with four different climate scenarios modelled wiht three different Earth-system models (EC-Earth3, CanESM and MPI). This data was used to parametrize the CLASH model (https://doi.org/10.5281/zenodo.8271846).

InTheMED WP6 Data Archive – Tympaki, Greece: Simulation Results

This data archive includes the files “InTheMED_WP6_DS_ SimulatedGWLevelTimeSeriesGreece.mat” and “InTheMED_WP6_GDS_ MapsGreece.gdb” which contain the datasets that represent the results of the simulation runs of the Tympaki numerical groundwater model. More specifically, the timeseries of the groundwater level at the observation wells and the shapefiles that are necessary for the mapping of groundwater level and pumping rates at the optimization time-step.

SRBench Competition 2023 - Datasets, Scripts, and Results

Source-codes for: generating artificial datasets, evaluating the results and repositories for the SRBench Competition 2023: https://cavalab.org/srbench/competition-2023/

TAM Results dataset

Data from the TAM questionnaire presented to participants in all three pilots.

SNN-NLN August 2023 HERA Experiment Results

Trial result files, presented plots and summary result tables for HERA simulation RFI detection experiments. These result files are published for complete result transparency and not necessarily re-use.

opencc-by-4.0Dec 2022View details →

Images of daphnids (control and exposed to NMs) over multiple generations, scored by experts as toxic or non-toxic and the resulting deepDaph predictions

Background This study showcases a pioneering application of deep learning methodologies in ecotoxicology, aimed at facilitating hazard assessment and safer design of engineered nanomaterials (ENMs). The research hinges on a high-quality dataset comprising microscopic images of Daphnia magna exposed to various ENMs, collected systematically under controlled conditions. The Dataset: A Cornerstone of Nanoinformatics Our dataset, which will be openly accessible on Zenodo, serves as a foundational resource for the ecotoxicology community. It contains high-resolution images tagged with intricate details like malformations, tail lengths, lipid concentrations, and lipid deposit shapes. Researchers can use this exhaustive dataset to train a variety of predictive models for diverse applications. Methodology We employ two different deep learning architectures to process the dataset. These architectures automatically detect malformations and assess the impact of ENMs on D. magna by classifying various biological structures based on lipid densities. Results and Validation The developed models demonstrate high statistical validation, confirming their prediction accuracy on external D. magna images. Our dataset and the associated models not only accelerate manual procedures but also pave the way for automated, high-throughput analyses in ecotoxicology. Future Prospects The dataset holds the potential to extend investigations into predicting the impacts on future generations from parental exposures, thus reducing the time and cost of multi-generational toxicity assays.

Search-based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functions - Dataset and Results

This dataset contains the replication package for the paper: Federico Formica, Tony Fan, and Claudio Menghi. 2023. "Search-based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functions". The dataset contains: <ul> <li>The models used in the evaluation section.</li> <li>All the results obtained by running Athena-S on the models.</li> <li>The scripts that automatically analyze the results file and produce the tables and figures used in the paper.</li> </ul>

First results of online compression of HEP data using Baler

Within the framework of the HiDA Trilateral Data Science Exchange Program, this internship project unveils preliminary findings on online compression using Baler. It involved the examination of various datasets of different sizes from the High Energy Physics (HEP) domain to evaluate compression performance. All datasets used are subsets of the jet data recorded by the CMS experiment at the LHC in 2012, released as open data under the Creative Commons CC0 waiver (see references). The data is modified (flattened, truncated, formatted, etc.) and packaged in a way that makes it easy for others to reproduce the results. The provided files in this page include comprehensive instructions for replicating the project's results, as well as datasets and outcomes. Below, you'll find a brief overview of the project's folder structure, categorized by the dataset utilized (small dataset/example CMS data/larger CMS data), the online/offline compression method, and resource utilization, particularly regarding GPU usage. Presentations summarizing this project's results can be found here: https://zenodo.org/record/8326707. Project's folders: <ol> <li> Reproduction Instructions: This folder houses all files that offer detailed guidelines for replicating the project's presented results. These files serve as a reference for accessing relevant materials. </li> <li> GPU with Example CMS Data.zip: This directory contains all files related to offline compression of the approximately 100MB example CMS dataset provided by Baler. GPU resources were employed in the model training process. </li> <li> GPU with Larger CMS Data (1).zip: In this section, you'll find files associated with the compression of a larger CMS dataset, approximately 1.4GB in size. It includes results of offline compression and a split of the dataset into a 50/50 ratio for training and testing, with results provided for various epochs. </li> <li> GPU with Larger CMS Data (2): This folder holds the larger dataset, divided into two halves, with the first half's array values in one file and the second half's in another. </li> <li> Offline/Online on Small Dataset: Here, you'll find files related to both offline and online compression of a small dataset, roughly 100KB in size, extracted from the example CMS dataset provided by Baler. </li> <li> Modifications of Small Dataset: This section comprises variations of the small dataset, including both normalized and un-normalized datasets. </li> <li> Materials: This folder includes fundamental papers and summaries to enhance your understanding of the project. </li> <li> HiDA: Within this directory, you'll find a printed webpage from the HiDA program. </li> </ol>

DESI Complete Calibration of the Color-Redshift Relation (DC3R2): Results from early DESI data

The data and python code used to reproduce the figures in the paper of the same name, J. McCullough et al.

Norway energy policy optimization results

These files contain the raw results from master thesis on Norwegian energy policy directions. The each folder has a html file (with basic plots), a netcdf file and a series of csv files. The four scenarios uploaded are the unconstrained (model is free to install capacity as it wants) and base case (current norwegian policy direction), for both a beyond 2030 and beyond 2050 timeline. These results are part of a Master thesis work. Additional results related to transmission and onshore wind policy available upon request to h.guddingsmo@outlook.com.

H2020 Platone Greek Demo State Estimation Results

The dataset contains the results of the simulations of the State Estimation Tool for the Greek Demo of the Platone Project, for two separate instances during a day