Find research datasets worth reusing

Linked collectors and determiners for: A second update to the checklist of Finnish long-legged flies (Diptera: Dolichopodidae), with a re-evaluation of the status of Hydrophorus callosoma Frey, 1915.

Natural history specimen data linked to collectors and determiners held within, "A second update to the checklist of Finnish long-legged flies (Diptera: Dolichopodidae), with a re-evaluation of the status of Hydrophorus callosoma Frey, 1915". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/d30a4943-1366-49eb-b781-9429528d5929">https://bionomia.net/dataset/d30a4943-1366-49eb-b781-9429528d5929</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/d30a4943-1366-49eb-b781-9429528d5929">https://gbif.org/dataset/d30a4943-1366-49eb-b781-9429528d5929</a>. Formatted as a Frictionless Data package.

opencc-zeroOct 2024View details →

Linked collectors and determiners for: Revisiting the taxonomy and molecular systematics of Sesamia stemborers (Lepidoptera: Noctuidae: Apameini: Sesamiina): updated classification and comparative evaluation of species delimitation methods.

Natural history specimen data linked to collectors and determiners held within, "Revisiting the taxonomy and molecular systematics of Sesamia stemborers (Lepidoptera: Noctuidae: Apameini: Sesamiina): updated classification and comparative evaluation of species delimitation methods". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/0dedf555-acec-471d-a197-0a2cfe1a1329">https://bionomia.net/dataset/0dedf555-acec-471d-a197-0a2cfe1a1329</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/0dedf555-acec-471d-a197-0a2cfe1a1329">https://gbif.org/dataset/0dedf555-acec-471d-a197-0a2cfe1a1329</a>. Formatted as a Frictionless Data package.

Linked collectors and determiners for: Review of the North American fauna of Drymeia Meigen (Diptera, Muscidae) and evaluation of DNA barcodes for species-level identification in the genus.

Natural history specimen data linked to collectors and determiners held within, "Review of the North American fauna of Drymeia Meigen (Diptera, Muscidae) and evaluation of DNA barcodes for species-level identification in the genus". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/16f599ee-42e4-4042-920d-8c9c4623ea0d">https://bionomia.net/dataset/16f599ee-42e4-4042-920d-8c9c4623ea0d</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/16f599ee-42e4-4042-920d-8c9c4623ea0d">https://gbif.org/dataset/16f599ee-42e4-4042-920d-8c9c4623ea0d</a>. Formatted as a Frictionless Data package.

Linked collectors and determiners for: Revision of the Metallactus generosus species-group with a preliminary evaluation of the effectiveness of the endophallus morphology in distinguishing critical sibling species (Coleoptera: Chrysomelidae: Cryptocephalinae).

Natural history specimen data linked to collectors and determiners held within, "Revision of the Metallactus generosus species-group with a preliminary evaluation of the effectiveness of the endophallus morphology in distinguishing critical sibling species (Coleoptera: Chrysomelidae: Cryptocephalinae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/43278c49-82a8-40a3-88c8-6e28bfb08a7e">https://bionomia.net/dataset/43278c49-82a8-40a3-88c8-6e28bfb08a7e</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/43278c49-82a8-40a3-88c8-6e28bfb08a7e">https://gbif.org/dataset/43278c49-82a8-40a3-88c8-6e28bfb08a7e</a>. Formatted as a Frictionless Data package.

Linked collectors and determiners for: Re-evaluation of the genus Biapertura Smirnov, 1971 (Cladocera: Anomopoda Chydoridae).

Natural history specimen data linked to collectors and determiners held within, "Re-evaluation of the genus Biapertura Smirnov, 1971 (Cladocera: Anomopoda Chydoridae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/de7c4264-09b0-4361-a7cb-04f80e6ddbe2">https://bionomia.net/dataset/de7c4264-09b0-4361-a7cb-04f80e6ddbe2</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/de7c4264-09b0-4361-a7cb-04f80e6ddbe2">https://gbif.org/dataset/de7c4264-09b0-4361-a7cb-04f80e6ddbe2</a>. Formatted as a Frictionless Data package.

opencc-by-4.0Oct 2024View details →

Wisdom of Crowds for Supporting the Safety Evaluation of Nanomaterials - Data and results

This repository contains the input data and the generated results utilized as part of the research article titled "Wisdom of Crowds for Supporting the Safety Evaluation of Nanomaterials"  by Saarimäki & Fratello et al. <h1>Data</h1> This dataset includes anonymized responses of a panel of experts to a questionnaire focused on nanomaterials safety. <h2>Additional Data Sources</h2> This entry further includes additional data from the following sources: <ul> <li>Saarimäki et al. (2021): Preprocessed data and primary physicochemical characteristics are available on <a href="https://doi.org/10.5281/zenodo.6425445">Zenodo</a>.</li> <li>Gallud et al. (2020): Data is available from the NCBI Gene Expression Omnibus (GEO) under accession number <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE148705">GSE148705</a>.</li> <li>del Giudice et al. (2023): The advanced descriptors are accessible through the associated <a href="https://doi.org/10.5281/zenodo.7674574">Zenodo repository</a>.</li> <li>Labouta et al. (2019): The harmonized dataset of ENMs cell viability assays is available as <a href="https://pubs.acs.org/doi/suppl/10.1021/acsnano.8b07562/suppl_file/nn8b07562_si_001.xlsx">supporting information of the reasearch article</a>.</li> </ul> <h1>Outputs</h1> <ul> <li>Statistical modeling: The inferred parameters of the statistical model developed to analyze the expert responses and identify the consensus among the experts.</li> <li>Machine Learning classifiers: The performances of several machine learning classifiers trained on the consensus responses to learn models that can predict safety concerns of new nanomaterials based on transcriptomics and physicochemical descriptors data.</li> <li>Feature Importance: The relevant features extracted from the models analyzed to understand which aspects of the molecular responses to ENMs and which physicochemical properties are most important in driving the predictions.</li> </ul> <h1>Contents</h1> <code>data/Combined_cleaned_responses_anon.xlsx</code>: Contains the anonymized responses from the expert panel. <code>data/enms_grouping.txt</code>: Contains a categorization of the ENMs based on their core material. <code>data/expert_bibliographies_anon.pickle</code>: Contains the anonymized bibliography of the experts to assess the multidisciplinarity of the panel assembled. <code>data/gex.csc.gz</code>: Contains the gene expression after exposure to the ENMs. <code>data/phenodata.txt</code>: Contains the meta-data of the experiments <code>data/physicochemical_descriptors.txt</code>: Contains the physicochemical descriptors of the ENMs. <code>data/external/nn8b07562_si_001.xlsx</code>: Contains a panel of harmonized ENMs cytotoxicity assays. <code>outputs/concern_scores.csv</code>: Contains the consensus scores inferred by the statistical model. <code>outputs/cross_validation/</code>: Contains the logs and performances of all the machine learning classification runs . <code>outputs/important_{genes,physchem}_weighted.xlsx</code>: Feature relevance scores of both views. <code>outputs/model_inference_anon.nc</code>: Inferred parameters of the statistical model.

opencc-by-4.0Nov 2024View details →

Evaluating an instrument of the research software related to software use and disclosure - Dimension 1 - Dataset of Focus Groups

Artifacts used for data collection and analysis of the focus groups sessions during the evaluation of an instrument for research software related to software use and disclosure - dimension 1.

Evaluation of different deployment strategies for larviciding to control malaria – a simulation study

Simulation datasets for: "Evaluation of different deployment strategies for larviciding to control malaria – a simulation study". The folders include a description folder with relevant input files for simulations in OpenMalaria and the processedExpResults includes the Rdataframes, generated from the raw simulation outputs, used for analysis.

Evaluation of the Economic Situation in the Arab World (Aggregated by County)

The Arab Barometer Wave V 2018-2019 is based on a nationally representative probability sample of the population aged 18 and above. In most countries, the sample includes 2,400 citizens. The data were conducted in face-to-face public opinion surveys (CAPI and PAPI). See technical reports by country for country-specific information. You can find the data, codebooks and all relevant information on the Arab Barometer website. Our dataset contains country weighted counts of different answer options and the re-weighted values of the answers given to the Arab Barometer Wave 5 question: Q101 : How would you evaluate the current economic situation in your country? Very good, Good, Bad, Very bad our Don’t know, Refused to answer. See PDF documentation for details

Synthesis and biological evaluation of a radiolabeled PET probe for visualization of in vivo -fucosidase expression - esi

Supplementary data for paper titled: Synthesis and biological evaluation of a radiolabeled PET probe for visualization of in vivo a-fucosidase expression

Evaluation notebook and files for FAIR Workbench user evaluation

This archive contains the Jupyter notebook and associated (image) files used in the June 2021 evaluation of the FAIR Workbench.

Model run with METROMS to evaluate open boundary conditions in CICE [idealized wind]

Support for time-varying open boundary conditions (OBC) have been developed for sea ice in the Los Alamos Sea Ice Model (CICE) by Pedro Duarte (NPI, Norway). This dataset is a result of using the coupled ocean (ROMS) and sea ice (CICE) modelling framework METROMS (<a href="https://github.com/metno/metroms">https://github.com/metno/metroms</a>) in order to test the effects of the above mentioned boundary conditions. The specific application of METROMS that was used was MET Norway's main forecasting system for the Barents Sea; the Barents-2.5km model; details about the model can be found at <a href="https://ocean.met.no/models">https://ocean.met.no/models</a>. The model was initialized from the TOPAZ4 model (Sakov et al., 2012) and was run for the period 2019.09.01 - 2019.09.20, one time without OBC and one time with OBC enabled using input data from TOPAZ4 at the boundaries. In both runs the model was set up realistically, but with the exception of idealized wind forcing. More specifically, the wind was blowing 10 m/s in the positive xi-direction until 2019-09.07 and then 10/m/s in the negative xi-direction for the rest of the simulation. The reasoning for this was to clearly demonstrate the effects of the OBC. Initially, the wind is blowing the ice away from the boundary and without using OBC for ice, it leaves open water in its path due to no information coming in through the boundary. With OBC enabled however, the simulation appear a lot more sensible with sea ice from TOPAZ4 coming in through the boundaries. When the wind switches to the opposite direction after 2019.09.07, on the case without OBC results in ice piling up at the boundary after some time. However, with OBC enabled, the ice exists the model domain.

Model run with METROMS to evaluate open boundary conditions in CICE

Collaborative Program Comprehension based on Augmented Reality (Evaluation Results of Master's Thesis)

The dataset contains feedback generated through a survey for an augmented reality approach in the ExplorViz project. The dataset includes the results for a pilot study with two probands and the results for a case study with 20 probands.

A large-ensemble simulation of yields and meteorological drivers to evaluate spatial compounding crop failures in Europe

The dataset consists of a subset from Vogel et al. (2021), comprising large-ensemble simulations of winter wheat yields aggregated at the country level for 20 European countries. The winter wheat yields were simulated by the APSIM-Wheat model (version 7.10) (Zhang et al. 2014) driven by meteorological data from the EC-Earth global climate model (Hazeleger et al., 2010; Van der Wiel et al., 2019). To investigate meteorological drivers of crop failure, the dataset also includes monthly means of daily precipitation, vapour pressure deficit, and maximum temperature fields, of two leading European producers, i.e.  France and Germany. For more details, see the description in Vogel et al. (2021). By using this data, you also agree to cite the reference below: Vogel, J., Rivoire, P., Deidda, C., Rahimi, L., Sauter, C. A., Tschumi, E., van der Wiel, K., Zhang, T., Zscheischler, J. (2021). Identifying meteorological drivers of extreme impacts: an application to simulated crop yields. Earth System Dynamics,12(1),151-172.

ADEPT: A Dataset for Evaluating Prosody Transfer

The ADEPT dataset consists of prosodically-varied natural speech samples for evaluating prosody transfer in english text-to-speech models. The samples include global variations reflecting emotion and interpersonal attitude, and local variations reflecting topical emphasis, propositional attitude, syntactic phrasing and marked tonicity. Txt and wav files are organised according to the folder structure {speech_class}/{subcategory_or_interpretation}/{filename}, where filename follows the naming convention {speaker}_{utterance_id}. Speakers comprise 'ad00' (female voice) and 'ad01' (male voice). For classes with multiple interpretations, we provide the interpretations used in the disambiguation tasks in 'adept_prompts.json'. The corpus only includes prosodic variations that listeners are able to distinguish with reasonable accuracy, and we report these figures as a benchmark against which text-to-speech prosody transfer can be compared. More details can be found in our pre-print about the dataset (https://arxiv.org/abs/2106.08321).

A case study: evaluation of a single column model with advection to simulate fog/stratus during C-FOG experiment

Those datasets are observed by  C-FOG (Toward Improving Coastal Fog Prediction) campaign, which was organized as a field experiment combined with modelling and theoretical initiatives. The objective of C-FOG was to advance our understanding and ability to observe, simulate, and predict fog, with a particular focus on warm fog formation, development and dissipation over coastal environments. The uploaded observation data contains liquid water content, droplet number concentration, temperature, SST, wind, visibility, backscatter collected by ceilometer, and atmospheric profile. The details can be found in the dataset. Thanks for the intense observation by the C-FOG project, which collected valuable data for detailed fog research.

opencc-by-4.0Nov 2020View details →

Compendium of examples: good practice in evaluating measurement uncertainty

This document illustrates good practice in the evaluation of measurement uncertainty. It contains examples from a variety of areas in calibration and testing, and illustrates the use of the methods from the “Guide to the expression of Uncertainty in Measurement” and its supplements, as well as Bayesian approaches.  Compendium_M36.pdf: document

Research project on field data collection for honey bee colony model evaluation - datasets

Description of the datasets The file 00_MUSTB_field_data_model.docx contains the data model according to which the data collected in the context of the MUSTB field data collection were reported to EFSA. The current data model description includes some modifications with respect to the specifications published before the beginning of the project (EFSA, 2017, https://doi.org/10.2903/sp.efsa.2017.EN-1234). All the tables included in the data model are published here in csv format. The underlying schemas are also published in xsd format. Sites: General information about the sites where the data collection took place; Polygons: General information about the polygons where the botanical survey took place. Table I: Pesticide application, reporting data on experimental spraying events; Table II: Resource providing unit and landscape fitness, reporting data on abundance of flowering plants in polygons mostly within 1.5 km, but in some cases up to 3 km of the experimental colony; Table III: Master list of all hives included in the study; Table IV: Colony management, reporting the log of the beekeeper regarding input (if material was added to the hive: e.g. empty frames, chemicals for varroa treatment, sugar), output (if material was removed from the hive, e.g. honey combs, supers), queen loss, swarming, or clinical signs observed in the experimental hives; Table V: Hive inspection, reporting data on in-hive measurements in the experimental colonies. This table contained several types of data, including: <ul> <li>Data on brood development and food provision (“cell utilization”) obtained from image analysis of combs;</li> <li>Data on forager activity obtained from automatic video recordings and image analysis by a bee counter;</li> <li>Data on hive weight obtained from automatic logging by a hive scale;</li> <li>Data on adult bee strength, obtained by weight assessment of combs with and without adult bees (“bees per comb data”);</li> </ul> Table VI: SSD2, reporting data on results of laboratory analyses of pollen, pesticide residues and parasites/pathogens. These four types of laboratory analyses involved different methods, and were reported according to different standards. Therefore, a number of the fields in the technical specifications for the SSD2 table (EFSA, 2017) were not applicable for records reporting results of some analyses, in particular palynological, parasite and pathogen analyses. These fields were left empty; Table VII: Colony observation, reporting observations of honey bee waggle dances from observation hives. Orientation denotes the angle of the waggling phase relative to the vertical axis on the comb. Direction denotes the actual direction in the landscape, as calculated from the orientation of the waggle dance. In all the csv files, columns with the suffix "_desc" have been included, where relevant, to include the name corresponding to the EFSA controlled terminology used in the previous column (e.g. resUnit contains EFSA term codes while resUnit_desc contains the term names). Data storage All data collected during the project was stored in a relational database. The database was developed in .NET Entity Framework Core, ran on a PostgreSQL, and was hosted by Amazon Web Service during the whole duration of the project development. Data could be imported or entered manually in the database through a web form. Administrators could create new users and administrators, new sites, and new colonies, i.e., administrators were allowed to enter or change data of all tables. Users were allowed to enter data, and could view, retrieve, and modify their own data of all tables, except for Table III (description of experimental colonies). Administrators could view and retrieve all data. Data was retrieved in CSV and XML formats, and were structured to secure a smooth transmission of data to the Data Collection Framework of EFSA. Furthermore, data flow from the field data collection to the development of ApisRAM was secured by direct communication between the field and modelling teams.   Version 2 contains the UTM coordinates in tables Sites, Polygons and Resource providing unit.