Find research datasets worth reusing

opencc-by-4.0Apr 2022View details →

Graph Theoretical Measures of Fast Ripple Networks Support the Epileptic Network Hypothesis

MongoDB JSON files of the (high-frequency oscillation) HFO and electrode databases used for this study and others.

opencc-by-4.0May 2022View details →

Supporting Data for Figures in "Evolving Interior Mixing Regimes in a Tidal River Plume"

Supporting data for figures in "Evolving Interior Mixing Regimes in a Tidal River Plume" by Preston S. Spicer, Kimberly D. Huguenard, Kelly L. Cole, Daniel G. MacDonald, and Michael M. Whitney. The scientific journal article is published in Geophysical Research Letters (2022). The article elucidates the evolution of stratified shear mixing in a tidal river plume using observational data taken over an ebb pulse throughout the interior of the Merrimack River plume. The file GRL_figs.m is a MATLAB file which produces Figures 1 thru 4 in the article taking the accompanying .mat and .txt files as input. Variables names and units correspond to graphed data of each figure in the journal article.

opencc-by-4.0Jun 2022View details →

Model output supporting MimiBRICK v1.1.0

Model calibration output and model simulation output supporting the model calibration and analysis codes included in MimiBRICK v1.1.0. v1.1.0 edit: file naming simplified

opencc-by-4.0Dec 2021View details →

Supporting data for "Estimating animal density for a community of species using information obtained only from camera-traps"

Data underlying a paper published in Methods in Ecology and Evolution (<a href="https://doi.org/10.1111/2041-210X.13930">https://doi.org/10.1111/2041-210X.13930</a>). These data are suitable for estimating animal density using the Random Encounter Model and include: i) detection counts for 35 species across 510 camera-trap locations; ii) movement speeds (estimated by tracking animal movements in camera-trap image sequences), iii) activity times (filtered so that records of the same species at the same location are > 60 minutes apart), and iv) measurements of the angular and radial distance from camera-traps for animals that were detected.

opencc-by-4.0Dec 2021View details →

Datasets and Supporting Materials for the IPIN 2021 Competition Track 3 (Smartphone-based, off-site)

This package contains the datasets and supplementary materials used in the IPIN 2021 Competition. Contents: <ul> <li>IPIN2021_Track03_TechnicalAnnex_V1-02.pdf: Technical annex describing the competition</li> <li>01-Logfiles: This folder contains a subfolder with the 105 training logfiles,  80 of them single floor indoors, 10 in outdoor areas, 10 of them in the indoor auditorium with floor-trasitio and 5 of them  in floor-transition zones, a subfolder with the 20 validation logfiles, and a subfolder with the 3 blind evaluation logfile as provided to competitors.</li> <li>02-Supplementary_Materials: This folder contains the matlab/octave parser, the raster maps, the files for the matlab tools and the trajectory visualization.</li> <li>03-Evaluation: This folder contains the scripts used to calculate the competition  metric, the 75th percentile on the 82 evaluation points. It requires the  Matlab Mapping Toolbox.  The ground truth is also provided as 3 csv files.  Since the results must be provided with a 2Hz freq. starting from  apptimestamp 0, the GT files include the closest timestamp matching the timing provided by competitors for the 3 evaluation logfiles. It contains samples of reported estimations and the corresponding results.</li> </ul> Please, cite the following works when using the datasets included in this package: <ul> <li>Torres-Sospedra, J.; et al.  Datasets and Supporting Materials for the IPIN 2021 Competition Track 3 (Smartphone-based, off-site). http://dx.doi.org/10.5281/zenodo.5948678</li> </ul>

opencc-by-4.0Jun 2022View details →

NEMO: A Tool to Support Feature Models with Numerical Features and Arithmetic Constraints

Your can watch this video in my Youtube channel: <a href="https://youtu.be/V-ONW8PftwM">https://youtu.be/V-ONW8PftwM</a> This is a Live Conference Presentation, please access and cite the published version of the respective publication: <a href="https://youtu.be/V-ONW8PftwM">https://doi.org/10.1007/978-3-031-08129-3_4</a> Real-world Software Product Lines (SPLs) need Numerical Feature Models (NFMs) whose features not only have boolean values satisfying boolean constraints, but also have numeric attributes satisfying arithmetic constraints. A key operation on NFMs finds near-optimal performing products, which requires counting the number of SPL products. Typical constraint satisfaction solvers perform poorly on counting. Nemo (Numbers, features, models) supports NFMs by bit-blasting, the technique that encodes arithmetic as boolean clauses. Nemo translates NFMs to propositional formulas whose products can be counted efficiently by #SAT solvers, enabling near-optimal products to be found. We evaluate Nemo with a diverse set of real-world NFMs, complex arithmetic constraints, and counting experiments in this paper.

Supporting data for: Low-cost anti-mycobacterial drug discovery using engineered E. coli

Supporting data for: Low-cost anti-mycobacterial drug discovery using engineered E. col This dataset pertains to our work developing TESEC Mtb ALR, a genetically engineered strain of E. coli expressing the enzyme ALR derived from Mtb. We used the TESEC Mtb ALR strain in a high-throughput drug screen and identified benazepril as targeted inhibitor of the ALR enzyme. We then performed additional experiments to characterize the activity of benazepril against E. coli, Mtb and purified enzymes. Finally, we tested the extensibility of the platform by constructing and screening against similar strains for additional targets: Asd, CysH, DapB, and TrpD. These files include growth measurements, biochemical assays and other forms of biological data. They are packaged together with scripts used to analyze the data and present them in figures. Our goal in creating this archive was to present our complete analysis pipeline in the spirit of open science. It is not intended to be a stand-alone resource. Consult the associated manuscript for protocols, units of measurement and other essential technical context. Our scripts were written for Python 3.8. The raw data is presented as human-readable .csv files intended to be imported as Pandas DataFrames. Some data is also packaged as Python dictionaries saved with the Pickle package.

Microsatellite genotypes for «Genetic diversity and spatial genetic structure support the specialist‑generalist variation hypothesis in two sympatric woodpecker species»

Species are often arranged along a continuum from “specialists” to “generalists”. Specialists typically use fewer resources, occur in more patchily distributed habitats and have overall smaller population sizes than generalists. Accordingly, the specialist-generalist variation hypothesis (SGVH) proposes that populations of habitat specialists have lower genetic diversity and are genetically more differentiated due to reduced gene flow compared to populations of generalists. Here, expectations of the SGVH were tested by examining genetic diversity, spatial genetic structure and contemporary gene flow in two sympatric woodpecker species differing in habitat specialization. Compared to the generalist great spotted woodpecker (Dendrocopos major), lower genetic diversity was found in the specialist middle spotted woodpecker (Dendrocoptes medius). Evidence for recent bottlenecks was revealed in some populations of the middle spotted woodpecker, but in none of the great spotted woodpecker. Substantial spatial genetic structure and a significant correlation between genetic and geographic distances were found in the middle spotted woodpecker, but only weak spatial genetic structure and no significant correlation between genetic and geographic distances in the great spotted woodpecker. Finally, estimated levels of contemporary gene flow did not differ between the two species. Results are consistent with all but one expectations of the SGVH. This study adds to the relatively few investigations addressing the SGVH in terrestrial vertebrates.

Supporting Information for Disclosing Spin-Polarized Bonds on Isolable Molecules

The file corresponds to the Bachelor Thesis of Ms. Elena Paulus. It contains the xyz coordinates of all optimized structures and their corresponding electronic energy in Hartree.

Supporting data: HiFi chromosome-scale diploid assemblies of the grape rootstocks 110R, Kober 5BB, and 101-14 Mgt

Repository for supporting data to the paper: HiFi chromosome-scale diploid assemblies of the grape rootstocks 110R, Kober 5BB, and 101-14 Mgt

opencc-by-4.0Dec 2021View details →

Supporting data of: Hydrography and food distribution during a tidal cycle above a cold-water coral mound

This file contains the raw data and data analyses scripts to: Hydrography and food distribution during a tidal cycle above a cold-water coral mound Evert de Froe, Sandra R. Maier, Henriette G. Horn, George A. Wolff, Sabena Blackbird, Christian Mohn, Mads Schultz, Anna-Selma van der Kaaden, Chiu H. Cheng, Evi Wubben, Britt van Haastregt, Eva Friis Moller, Marc Lavaleye, Karline Soetaert, Gert-Jan Reichart, Dick van Oevelen. Deep Sea Research Part I: Oceanographic Research Papers, 2022, ISSN 0967-0637, https://doi.org/10.1016/j.dsr.2022.103854. Abstract: Cold-water corals (CWCs) are important ecosystem engineers in the deep sea that provide habitat for numerous species and can form large coral mounds. These mounds influence surrounding currents and induce distinct hydrodynamic features, such as internal waves and episodic downwelling events that accelerate transport of organic matter towards the mounds, supplying the corals with food. To date, research on organic matter distribution at coral mounds has focussed either on seasonal timescales or has provided single point snapshots. Data on food distribution at the timescale of a diurnal tidal cycle is currently limited. Here, we integrate physical, biogeochemical, and biological data throughout the water column and along a transect on the south-eastern slope of Rockall Bank, Northeast Atlantic Ocean. This transect consisted of 24-hour sampling stations at four locations: Bank, Upper slope, Lower slope, and the Oreo coral mound. We investigated how the organic matter distribution in the water column along the transect is affected by tidal activity. Repeated CTD casts indicated that the water column above Oreo mound was more dynamic than above other stations in multiple ways. First, the bottom water showed high variability in physical parameters and nutrient concentrations, possibly due to the interaction of the tide with the mound topography. Second, in the surface water a diurnal tidal wave replenished nutrients in the photic zone, supporting new primary production.  Third, above the coral mound an internal wave (200 m amplitude) was recorded at 400 m depth after the turning of the barotropic tide. After this wave passed, high quality organic matter was recorded in bottom waters on the mound coinciding with shallow water physical characteristics such as high oxygen concentration and high temperature. Trophic markers in the benthic community suggest feeding on a variety of food sources, including phytodetritus and zooplankton. We suggest that there are three transport mechanisms that supply food to the CWC ecosystem. First, small phytodetritus particles are transported downwards to the seafloor by advection from internal waves, supplying high quality organic matter to the CWC reef community. Second, the shoaling of deeper nutrient-rich water into the surface water layer above the coral mound could stimulate diatom growth, which form fast-sinking aggregates. Third, evidence from lipid analysis indicates that zooplankton faecal pellets also enhance supply of organic matter to the reef communities. This study is the first to report organic matter quality and composition over a tidal cycle at a coral mound and provides evidence that fresh high-quality organic matter is transported towards a coral reef during a tidal cycle.

Electronic Supporting Information for Catalytic Ammonia Oxidation to Dinitrogen by a Nickel Complex

The dataset provides electronic supporting information in the format of XYZ molecular files, formatted Gaussian checkpoint files, and cube files for atomic spin density distributions for selected complexes obtained while investigating the catalytic mechanism of ammonia oxidation to dinitrogen using a N-heterocyclic carbene containing nickelocene complex. The level of theory used for all calculations is omega-B97xD with def2TZVP basis set. All calculations were performed using the Gaussian16 suite of programmes. Model Set 1 contains the metal free compounds and were used to calculate the overall thermodynamics of the ammonia oxidation reaction. Model Set 2 corresponds to the most truncated, in vacuo optimized structures. Model Set 3 comprises from non-truncated, realistic structures embedded in polarizable continuum model of benzene.

Dataset of CONTAINMENT and SUPPORT in the Uralic languages of the Volga-Kama area

This open access dataset contains examples of the expressions of CONTAINMENT and SUPPORT in the Uralic languages of Volga-Kama area. The exact languages and the sources of data are given in Table 1. The dataset contains data on the relational nouns (RN) and plain spatial cases expressing prototypical CONTAINMENT and SUPPORT in the languages. The RN included in the dataset are listed in Table 2, and case forms in Table 3.   <table> <tbody> <tr> <td> language </td> <td> corpora </td> </tr> <tr> <td> Erzya (MdE) </td> <td> Syatko-subcorpus of the MokshEr corpus (MokshEr 2010) </td> </tr> <tr> <td> Moksha (MdM) </td> <td> Subcorpora in Moksha of the MokshEr corpus (MokshEr 2010) </td> </tr> <tr> <td> Meadow Mari (MaM) </td> <td> Marko East (Marko [no year]), Oncyko (Oncyko 2000), Meadow Mari corpus (Arkhangelskiy 2019b), Wanca (Meadow Mari) (Helsingin yliopisto et al. 2019) </td> </tr> <tr> <td> Hill Mari (MaH) </td> <td> Marko West (Marko [no year]), Wanca (Hill Mari) (Helsingin yliopisto et al. 2019) </td> </tr> <tr> <td> Udmurt (Udm) </td> <td> Pilot version of Udmurt corpus (relational nouns; presently included into [Arkhangelskiy 2018]) Udmurt corpus (content nouns) (Arkhangelskiy 2018) </td> </tr> <tr> <td> Komi Zyrian (KoZ) </td> <td> Komi Zyrian Web Corpus (Arkhangelskiy 2019a), Коми корпус (Fu-Lab team 2021) </td> </tr> <tr> <td> Komi Permyak (KoP) </td> <td> Komi Permyak text collection from the University of Turku (Permyak 2009) </td> </tr> </tbody> </table> Table 1. Languages included into the dataset and the sources of the data for each language.   <table> <tbody> <tr> <td>   </td> <td> MdE </td> <td> MdM </td> <td> MaM </td> <td> MaH </td> <td> Udm </td> <td> KoZ </td> <td> KoP </td> </tr> <tr> <td> containment </td> <td> pot(mo)- </td> <td> potmə- </td> <td> kørgø, kørgə- </td> <td> kørgə̈- </td> <td> puʃk- </td> <td> pɨt͡ʃk- </td> <td> pɨt͡ʃk- </td> </tr> <tr> <td> support </td> <td> lang- </td> <td> lang- </td> <td> ymba- </td> <td> βə̈(l)- </td> <td> vɨl- </td> <td> vɨl-/vɨv- </td> <td> vɨl-/vɨv- </td> </tr> </tbody> </table> Table 2. RN included in the dataset.   <table> <tbody> <tr> <td>   </td> <td> MdE </td> <td> MdM </td> <td> MaM </td> <td> MaH </td> <td> Udm </td> <td> KoZ </td> <td> KoP </td> </tr> <tr> <td> location </td> <td> -so/-se (inessive) </td> <td> -sa (inessive) </td> <td> -ʃte/-ʃto/-ʃtø (inessive) </td> <td> -ʃtə/-ʃtə̈ (inessive) </td> <td> -ɨn (inessive) </td> <td> -ɨn (inessive) </td> <td> -ɨn (inessive) </td> </tr> <tr> <td> source </td> <td> -sto/-ste (elative) </td> <td> -sta (elative) </td> <td> gət͡ɕ (source postposition) </td> <td> gə̈t͡s (source postposition) </td> <td> -ɨɕ (elative) </td> <td> -ɨɕ (elative) </td> <td> -iɕ (elative) </td> </tr> <tr> <td> goal </td> <td> -s (illative) </td> <td> -s/-t͜s (illative) </td> <td> -ʃke/-ʃko/-ʃkø/-ʃ, (illative) </td> <td> -ʃkə/-ʃkə̈/-ʃ, (illative) </td> <td> -e/-ɨ (illative) </td> <td> -ɘ (illative) </td> <td> -ɘ (illative) </td> </tr> <tr> <td> path </td> <td> -ka/-ga/-va (prolative) </td> <td> -ka/-ga/-va/-gæ (prolative) </td> <td> - </td> <td> - </td> <td> -ti/-eti/-jeti/-ɨti (prolative) </td> <td> -ɘd (prolative); -ti (transitive) </td> <td> -ɘt (prolative); -ti (transitive) </td> </tr> </tbody> </table> Table 3. Cases that have been included into the dataset. All cases do not necessary show in every set, as for some combinations of RN and case there is no data.   The main purpose of the dataset is to enable the study of variation between a plain case and RN inflected in case when expressing CONTAINMENT or SUPPORT. To facilitate this each expression of relation has been given a prototypicality score 4 = most prototypical, 1 = non-prototypical, which tells if the relation between landmark and trajector expressed in the sentence is typical for the entities participating in it. The prototypicality scores are based on the pre-linguistic concepts of containment and support, which are robustly attested and therefore should be independent of any single language. The scoring is based on the authors understanding of the language external relations, and no native consultants are used to verify the results. Therefore, some caution is in order when using the dataset.   The dataset contains files with data of CONTAINMENT RN, SUPPORT RN, and plain case on all the included languages. The files are named according to the scheme element_languge (e. g. Containment_Erzya for the containment data on Erzya). In addition, files named element_frequencies show the number of examples divided by case and prototypicality score for each language, and element_summary shows the total number of prototypicality scores for each language. For plain case there are also summary files for the scores of CONTAINMENT and SUPPORT separately.   The dataset is annotated for following information: <ol> <li>The case in which the content noun or RN is inflected.</li> <li>The predicate as inflected in the data.</li> <li>The content noun as given in the data.</li> <li>Translations of both (mainly in citation form, but in predicate sometimes with some grammatical information, cf. abbreviations below).</li> <li>The prototypicality score. In the data on plain cases the prototypicality score is given only for the clauses where the relation is either CONTAINMENT or SUPPORT (i. e. the prototypicality score indicates the prototypicality of the relation as CONTAINMENT or SUPPORT according to the type of relation expressed).</li> <li>In the data on plain cases, the relation expressed by the case is marked (CONT = CONTAINMENT, SUP = SUPPORT, N/A = some other relation).</li> <li>The original sentence context.</li> <li>Free translation. Some of the translations are done following the lexical meanings and syntactic structures of the languages, so the English is unidiomatic from time to time.</li> <li>The file name with which the original sentence can be located in the corpus.</li> </ol>   The three final columns are partly lacking at the moment from the Mari and Komi languages. The translations in the data are intended only as guidelines, and anyone using the dataset should refer to the original language data in the analysis. The data in the columns is presented according to the following conventions : <ul> <li>If the predicate is in square brackets, it means that the predicate is not present in the clause with the target LM. This can be because of two reasons: 1) The predicate is given in a previous clause, and is elliptically omitted, 2) the “predicate” is copula, which is not obligatory in the present tense in the languages studied.</li> <li>The following abbreviations are used to specify the meaning of the predicate when the English translation is ambiguous (note that the use is not checked, and the abbreviations might be lacking from some predicates):</li> </ul> CAUS    causative CONT   continuative CVB      converb FRQ      frequentative INCH     inchoative INF        infinitive ITR        intransitive MOM     momentaneous NEG      negative NMLZ    nominalization PASS    passive PTCP    participle REFL    reflexive TRA      transitive The authors of this dataset are Tomi Koivunen and Riku Erkkilä and it is published under CC-BY-NC-ND licence. If used in a publication, please refer to this publication as well as mention the original source(s): This dataset has been used in following publications:   References to used corpora: Arkhangelskiy, Timofey. 2018. Udmurt corpus. http://udmurt.web-corpora.net/index.html. Arkhangelskiy, Timofey. 2019a. Komi-Zyrian corpus. http://komi-zyrian.web-corpora.net/index.html. Arkhangelskiy, Timofey. 2019b. Meadow Mari corpus. http://meadow-mari.web-corpora.net/index_en.html. Fu-Lab team. 2021. Корпус коми языка. http://komicorpora.ru/. Helsingin yliopisto, FIN-CLARIN, H. Jauhiainen, T. Jauhiainen & K. Lindén. 2019. Wanca 2016, Korp Version. Kielipankki. http://urn.fi/urn:nbn:fi:lb-2019052401. Marko. (no year). MARKO - Corpus of Mari language. University of Turku. MokshEr, V.3. 2010. Mok&scaron;an ja ersän sähköinen korpus. Turun yliopisto. Oncyko. 2000. Oncyko corpus. University of Turku. Permyak. 2009. Turku Komi-Permyak Corpus. University of Turku.

Data for conductance-based simulations of "Cortical oscillations support sampling-based computations in spiking neural networks"

This repository contains the full data generated by the conductance-based simulations described in: <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009753">Cortical oscillations support sampling-based computations in spiking neural networks</a>. The code is accessible via <a href="https://doi.org/10.5281/zenodo.5512526.">this repository</a>.

Data to support the publication "Impact of agricultural management on soil aggregates and associated organic carbon fractions: Analysis of long-term experiments in Europe"

Raw data: Experimental plot ids and information, mass distribution of all aggregate fractions after wet sieving, Sand content of each fraction to conduct the sand correction, mass distribution of all fractions after isolating the micro-aggregates held within the macroaggregates, yields per treatment, carbon content per fraction (raw data) All data per plot: SOC content, MAOM and POM content of each fraction presented in the fractionation scheme included in the manuscript, together with the mass of the relative fractions.

opencc-by-4.0Oct 2022View details →

Idealized wave data in support of Modulation of Bubble Mediated Gas Transfer due to Wave-Current Interactions

WaveWatchIII data output from idealized solutions reported by Romero 2019 Data are in Netcdf format and include metadata. Each file corresponds to a duration-limited solution with constant wind speed as indicated in the filename (e.g. 10mps means 10 m/s winds)

opencc-by-4.0Oct 2022View details →

Level 2 Winter data in support of Modulation of Bubble Mediated Gas Transfer due to Wave-Current Interactions

WaveWatchIII data output from solutions of a nested configuration off the coast of California. These data are a subset of the solutions reported by Romero et al. 2020. These data are Level 2 of the nested configuration with a horizontal resolution of 270 m. Also included in this repository are the Level 2 and Level 3 grid files. Data are in Netcdf format including the metadata. The two simulation periods are December 2006 and Spring 2007. List of files: L2_Dec2006.nc --         December control solution only forced by winds L2_cew_Dec2006.nc --   December solution including forcing by both winds and currents. L2_grid.nc  --   Level 2 grid L3_grid.nc -- Level 3 grid

opencc-by-4.0Oct 2022View details →

Supporting material of deliverable D2.1 (StretchBio_D.2.1_Report on relation deformation-force for the nanopillars)

Nanopillars - Analytical vs numerical bending - Spreadsheet Nanopillars - Deformation_pillars_on_SiO2substrate_figure10 at deliverable D2.1 Nanopillars - vonMises_stress_pillars_on_SiO2substrate_figure14 at deliverable D2.1

opencc-by-4.0Jan 2018View details →

Wind data (2007-2017) in florentine and chianti areas to support tree's damages reporting.

Wind data of several weather station to support tree damages investigations. Firenze Peretola Areoporto LIRQ ENAV LAT 43.809722 LON 11.203 ELEV 44 Sesto Polo Scientifico LAMMA-CNR LAT 43.8189 LON 11.2021 ELEV 40 Sesto Case Passerini Codice CFR TOS01001225 LAT 43.82 LON 11.17 ELEV 33 Scandicci San Giusto CFR TOS01001215 LAT 43.76 LON 11.19 ELEV 42 Tavarnelle CFR TOS11000021 LAT 43.57 LON 11.16 ELEV 374 Greve in Chianti CFR TOS11000073 LAT 43.61 LON 11.30 ELEV 254 Data sets gives annual and seasonal windplot roses. Wind data summaries by sectors of wind provenience ( Mean, Max,Median and Quantile95). Futher the 500th maximum records of gust are also extracted. Data are provided to support tree damages reporting.