Find research datasets worth reusing

opencc-by-4.0Apr 2022View details →

SROADEX: Dataset for binary recognition and semantic segmentation of road surface areas from high resolution Aerial Orthoimages Covering Approximately 8,650 km2 of the Spanish Territory Tagged with Road Information

The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography representing the axes of the different types of roads (urban, interurban and rural). This cartography has been obtained from different Spanish official sources (National Geographic Institute and autonomic cartographic agencies) that we have revised and edited in a meticulous and systematic way to verify that the roads are represented on the cartography according to the orthoimages, available on January 1, 2021 in the download center of the National Center of Geographic Information (CNIG), on 16 rectangular areas (28,5 km * 18,5 km) of the Spanish territory (insular and peninsular). The dataset consists of  777599 images in png format of 256x256 pixels, organized in folders for the different trainings, separating those corresponding to training, testing and validation. The structure of the data is as follows: 1-Road-Ortho and 1-Road-Mask contain the images and ground true for training the semantic segmentation networks. 1-Road-Ortho and 2-NoRoad-Ortho contain aerial images containing or not containing vials, for the training of binary tessellation networks identifying tessellations with vials. Moreover, in each folder the structure is the same: train, test, validation containing 90%, 5% and 5% of the total images and masks of each type. 1-Road-Ortho     |----Train     |----Test     -----Validation 1-Road-Mask     |----Train     |----Test     -----Validation 2-NoRoad-Ortho     |----Train     |----Test     -----Validation

opencc-zeroMay 2022View details →

Social information use about novel aposematic prey depends on the intensity of the observed cue

Animals gather social information by observing the behavior of others, but how the intensity of observed cues influences decision-making is rarely investigated. This is crucial for understanding how social information influences ecological and evolutionary dynamics. For example, observing a predator's distaste of unpalatable prey can reduce predation by naïve birds, and help explain the evolution and maintenance of aposematic warning signals. However, previous studies have only used demonstrators that responded vigorously, showing intense beak-wiping after tasting prey. Therefore, here we conducted an experiment with blue tits (Cyanistes caeruleus) informed by variation in predator responses. First, we found that the response to unpalatable food varies greatly, with only few individuals performing intensive beak-wiping. We then tested how the intensity of beak-wiping influences observers' foraging choices using video-playback of a conspecific tasting a novel conspicuous prey item. Observers were provided social information from: (1) no distaste response, (2) a weak distaste response, or (3) a strong distaste response, and were then allowed to forage on evolutionarily novel (artificial) prey. Consistent with previous studies, we found that birds consumed fewer aposematic prey after seeing a strong distaste response, however a weak response did not influence foraging choices. Our results suggest that while beak-wiping is a salient cue, its information content may vary with cue intensity. Furthermore, the number of potential demonstrators in the predator population might be lower than previously thought, although determining how this influences social transmission of avoidance in the wild will require uncovering the effects of intermediate cue salience.

opencc-by-4.0Mar 2022View details →

Supporting information for "An Atlas of Convection in Main-Sequence Stars"

The contents of 'code.zip' are the plotting and analysis scripts used in generating the plots in this work. The contents of 'atlas_Z_MW_time_2022_05_06_13_40_06_sha_94d5.zip' are the history files for the MESA runs which the plotting scripts analyze. Each file is in its own directory, labelled by the mass of the star in solar units.

opencc-zeroMay 2022View details →

MorphoGraphX2: Datasets that demonstrate how to create positional information with local coordinate systems

Confocal image data sets and segmented meshes from various plant organs including the Arabidopsis root, flower, gynoecium, meristem, embryo and ovule. The data sets are used to demonstrate features available in MorphoGraphX software (<a href="http://www.MorphoGraphX.org">www.MorphoGraphX.org</a>), and how to use positional information to add spatial context to quantitative cellular data. Also included are longform video tutorials, and source code for the MorphoGraphX software.

opencc-zeroMay 2022View details →

Dataset for paper titled: Conceptual preferences can be transmitted via selective social information use between competing wild bird species

<a name="_Hlk66629591"></a>Concept learning is considered a high-level adaptive ability. Thus far, it has been studied in laboratory via asocial trial and error learning. Yet, social information use is common among animals but it remains unknown whether concept learning by observing others occurs. We tested whether pied flycatchers (Ficedula hypoleuca) form conceptual relationships from the apparent choices of nest-site characteristics (geometric symbol attached to the nest box) of great tits (Parus major). Each wild flycatcher female (n = 124) observed one tit pair that exhibited an apparent preference for either a large or a small symbol and was then allowed to choose between two nest boxes with a large and a small symbol, but the symbol shape was different to that on the tit nest. Older flycatcher females were more likely to copy the symbol size preference of tits than yearling flycatcher females when there was a high number of visible eggs or a few partially visible eggs in the tit nest. However, this depended on the phenotype; copying switched to rejection as a function of increasing body size. Possibly the quality of and overlap in resource use with the tits affected flycatchers' decisions. Hence, our results suggest that conceptual preferences can be horizontally transmitted across co-existing animals, which may increase the performance of individuals that utilize concept learning abilities in their decision-making.

opencc-by-4.0May 2022View details →

Bangla Information Retrieval Test Collection

There are several IR test collections available in English (e.g. http://ir.dcs.gla.ac.uk/resources/test_collections/). Unfortunately, there is no Gold standard dataset available to test the effectiveness of Bangla IR. So, we have created a document collection containing 182 short stories, novels, and essays written by Rabindranath Tagore11 and 1000 newspaper articles published in 2013 crawled from the Bangla newspaper Prothom Alo12. The collection contains 100 newspaper articles each from one of the ten categories: বাংলাদেশ/ Bānlādēśa(EN: `Bangladesh'), খেলা/ khēlā(EN: `sports'), বিজ্ঞান ও প্রযুক্তি/ bijñāna ō prayukti(EN: `technology'), বিনোদন/ binōdana(EN: `entertainment'), আন্তর্জাতিক/ āntarjātika(EN: `international'), অর্থনীতি/ arthanīti(EN: `economy'), জীবনযাপন/ jībanayāpana(EN: `life-style'), মতামত/  matāmata(EN: `opinion'), শিক্ষা/ śikṣā(EN: `education') and আমরা/ āmarā(EN:`we-are'). There are 94 queries in the dataset, 26 queries belonging to complexity levels 1 and 2, 19 queries in complexity level 3 and 23 queries in complexity level 4. The definition of the complexity level of a query is described below: Complexity Level 1: The query contains exact words, phrases or sentence from the document. Complexity Level 2: The query is not present as it is in the document. There is a slight deviation. Complexity Level 3: The query is a generalised phrase capturing the overall story or the document’s theme. Complexity Level 4: It is a general query not related to any specific document.

Wikidata subset with revision history information [RDF]

This dataset is composed of 300 instances from the 100 most important classes in Wikidata, for a total of around 30000 entities and 390000 triples. The dataset is geared towards knowledge graph refinement models that leverage edit history information from the graph. There are two versions of the dataset: <ul> <li>The static version (files postfixed with '_static') contains the simple statements of each entity fetched from Wikidata.</li> <li>The dynamic version (files postfixed with '_dynamic') contains information about the operations and revisions made to these entities, and the triples that were added or removed.</li> </ul> Each version is split into three subsets: train, validation (val), and test. Each split contains every entity from the dataset. The train split contains the first 70% of revisions made to each entity, the validation split contains the 70% to 85% revisions, and the test set contains the last 15% revisions. This is a sample from the static datasets: <pre><code>wd:Q217432 a uo:entity ; wdt:P1082 1.005904e+06 ; wdt:P1296 "0052280" ; wdt:P1791 wd:Q18704103 ; wdt:P18 "Pitakwa.jpg" ; wdt:P244 "n80066826" ; wdt:P571 "+1912-00-00T00:00:00Z" ; wdt:P6766 "421180027" .</code></pre> Each entity has the type uo:entity, and contains the statements added during that time period following Wikidata's data model. In the following code snippet we show an example from the dynamic dataset: <pre><code>uo:rev703872813 a uo:revision ; uo:timestamp "2018-06-28T22:31:32Z" . uo:op703872813_0 a uo:operation ; uo:fromRevision uo:rev703872813 ; uo:newObject wd:Q82955 ; uo:opType uo:add ; uo:revProp wdt:P106 ; uo:revSubject wd:Q6097419 . uo:op703878666_0 a uo:operation ; uo:fromRevision uo:rev703878666 ; uo:opType uo:remove ; uo:prevObject wd:Q1108445 ; uo:revProp wdt:P460 ; uo:revSubject wd:Q1147883 .</code></pre> This dataset is composed of revisions, which have a timestamp. Each revision is composed of 1 to n operations, in which there is a change to a statement from the entity. There are two types of operations: uo:add and uo:remove. In both cases, the property and the subject being modified are shown with the uo:revProp and uo:revSubject properties. In the case of additions, uo:newObject and uo:prevObject properties are added to show the previous and new objects after the addition. In the case of removals, there is a uo:prevObject property to record the object that was removed.

opencc-zeroJun 2022View details →

Performance of akaike information criterion and bayesian information criterion in selecting partition models and mixture models

In molecular phylogenetics, partition models and mixture models provide different approaches to accommodating heterogeneity in genomic sequencing data. Both types of models generally give a superior fit to data than models that assume the process of sequence evolution is homogeneous across sites and lineages. The Akaike Information Criterion (AIC), an estimator of Kullback-Leibler divergence, and the Bayesian Information Criterion (BIC) are popular tools to select models in phylogenetics. Recent work suggests AIC should not be used for comparing mixture and partition models. In this work, we clarify that this difficulty is not fully explained by AIC misestimating the Kullback-Leibler divergence. We also investigate the performance of the AIC and BIC by comparing amongst mixture models and amongst partition models. We find that under non-standard conditions (i.e. when some edges have a small expected number of changes), AIC underestimates the expected Kullback-Leibler divergence. Under such conditions, AIC preferred the complex mixture models and BIC preferred the simpler mixture models. The mixture models selected by AIC had a better performance in estimating the edge length, while the simpler models selected by BIC performed better in estimating the base frequencies and substitution rate parameters. In contrast, AIC and BIC both prefer simpler partition models over more complex partition models under non-standard conditions, despite the fact that the more complex partition model was the generating model. We also investigated how mispartitioning (i.e. grouping sites that have not evolved under the same process) affects both the performance of partition models compared to mixture models and the model selection process. We found that as the level of mispartitioning increases, the bias of AIC in estimating the expected Kullback-Leibler divergence remains the same, and the branch lengths and evolutionary parameters estimated by partition models become less accurate. We recommend that researchers be cautious when using AIC and BIC to select among partition and mixture models; other alternatives, such as cross-validation and bootstrapping should be explored, but may suffer similar limitations.

How to Develop Resilience False Information?

In this video, you will   <ul> <li> Learn about the problems related to false information;  </li> <li> Understand two types of false information: misinformation and disinformation;  </li> <li> Develop an awareness of information literacy;  </li> <li> Discover some strategies to overcome susceptibility to fake news. </li> </ul>

opencc-by-4.0May 2022View details →

Logs and QC information for the northern Borneo Orogeny Seismic Survey seismic network

Instrument log files (mass positions + GPS offset/syncs and system information) for the northern Borneo Orogeny Seismic Survey (nBOSS) seismic network, which operated 2018–2020. FDSN network code: YC (<a href="https://doi.org/10.7914/SN/YC_2018">https://doi.org/10.7914/SN/YC_2018</a>).

opencc-by-4.0Jan 2022View details →

Supplementary information for 'Distinct gene expression dynamics in developing and regenerating crustacean limbs', by Sinigaglia et al.

Supplementary data and code for the manuscript 'Distinct gene expression dynamics in developing and regenerating crustacean limbs', by Sinigaglia et al.

opencc-by-sa-4.0Jun 2022View details →

Topological Network of the Dutch Fairway Information System

Topological fairway network derived from the <a href="https://www.vaarweginformatie.nl/">Dutch Fairway Information System</a>. The data is processed to be topological connected and usable for transport network analysis.   Files <code>network_digital_twin_v0.3.json</code> This is the json (for web) file for the Rhine corridor extending from Rotterdam (NLD) to Basel (Switzerland). <code>network_digital_twin_v0.3.pickle</code> This is the pickled (for performance) file for the Rhine corridor extending from Rotterdam (NLD) to Basel (Switzerland). <code>network_digital_twin_v0.3.zip</code> This is the shapefile (for gis) file for the Rhine corridor extending from Rotterdam (NLD) to Basel (Switzerland). Methodological information For details about the creation of the network see <a href="https://github.com/Deltares/digitaltwin-waterway/blob/master/notebooks/Build_FIS_network.ipynb">Build_FIS_network.ipynb</a>. Updates in version 0.3.0: - Added information on discharge dependent <a href="https://github.com/Deltares/digitaltwin-waterway/blob/feature/sailing/notebooks/velocities/read-velocities.ipynb">velocities</a> and <a href="https://github.com/Deltares/digitaltwin-waterway/blob/feature/sailing/notebooks/waterlevels/read_waterlevels.ipynb">waterlevels</a> in <code>river_waterlevel.geojson</code> and <code>river_velocity.geojson</code> - Moved information on <a href="https://github.com/Deltares/digitaltwin-waterway/blob/feature/sailing/notebooks/fis-network/generate_bathymetry.ipynb">bathymetry</a> to separate file<code>edges_0.3_with_bathy.geojson</code> - More structures added (fix in source dataset) - Edge and node id's are now always strings - Removed yaml file. It was not efficient enough and reading functionality is removed from networkx   Updates in version 0.2.0: - added bathymetry info: mean, standard deviation , percentiles [0 (min), 5,  10, 50 (median),  90, 95, 100 (max)] - added new output formats: added shapely compatible geometry type Source data for the network is available at <a href="https://www.vaarweginformatie.nl/">https://www.vaarweginformatie.nl/</a>. Sharing and Access information CC BY-SA 4.0 license applies: <a href="https://creativecommons.org/licenses/by-sa/4.0/">https://creativecommons.org/licenses/by-sa/4.0/</a>.

A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF) - 2021-11-26

A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF) --- Global Biodiversity Information Facility (GBIF) facilitates access to billions of biodiversity data records. These records include detailed accounts of life on earth. To help records of specific life forms, GBIF provides a taxonomic backbone [1,2]. This backbone contains a long list of names used to describe species and associated hierarchies and taxonomic publications. These lists are sourced from datasets around the world. At time of writing (18 Aug 2021), GBIF publishes a simplified version of their taxonomic backbone at [https://hosted-datasets.gbif.org/datasets/backbone/](https://hosted-datasets.gbif.org/datasets/backbone/) [1]. This repository provides script to pre-process https://hosted-datasets.gbif.org/datasets/backbone/backbone-current-simple.txt.gz to help facilitate access and improve performance of the creation of search indexes. Pre-process steps currently include: 1. reducing amount of columns 2. reverse sort by id 3. reverse sort by name Contents --- README:     this file repackage-gbif-backbone.sh:     script used to repackage GBIF Simple Backbone. backbone-current-simple.txt.gz:     original GBIF backbone archive gbif-backbone-by-name.tsv.gz:     two columns, gzipped, tab-separated text file with columns name, and id     reverse sorted by name gbif-backbone-by-name.tsv.sha256:     sha256 hash of the uncompressed gbif-backbone-by-name.tsv.gz gbif-backbone-by-id.tsv.gz:     20 columns, gzipped, tab-separated text file with first 20 columns of repackaged GBIF backbone file     reverse sorted by id gbif-backbone-by-id.tsv.sha256:     sha256 hash of the uncompressed gbif-backbone-by-id.tsv.gz References --- [1] Simplied GBIF Backbone Taxonomy. Accessed at https://hosted-datasets.gbif.org/datasets/backbone/ on 2021-08-18. [2] GBIF Secretariat (2021). GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei accessed via GBIF.org on 2021-08-18. Hash URIs --- This publication includes the following content uris: repackage-gbif-backbone.sh:     hash://sha256/073ac5490252c4ccbbd4f516d391faebe62c9fde9e4d75ae870441a86c382527 backbone-current-simple.txt.gz:     hash://sha256/15cbfc038e666356af27248935f79e408ed51fd8c0b49a668fed8dbf72591502     hash://sha256/1f78788a4a046dcbcf1e36c7658a1e333ca60e7586a372238d58b938d91fde51 (uncompressed) gbif-backbone-by-name.tsv.gz:     hash://sha256/6e11ae9961a9498b60d4bdeb489d6c1f5da9c2732310edaecdc79bd287b79ef4     hash://sha256/934ce05dbd067abb209168bd1d9389f122d051e1b7374b5d757a12e86f8da9a5 (uncompressed) gbif-backbone-by-id.tsv.gz:     hash://sha256/c434c7d3622421b17dadcd119391b32a66edee59f484d4cab924d92fd17713e2     hash://sha256/e2cf9116a21966315b0482d391052223e21c8e916ae0c097dfd37bed017b815b (uncompressed)

Supplementary Information for "G-type Halohydrin Dehalogenases Catalyze Ring Opening Reactions of Cyclic Epoxides with Diverse Anionic Nucleophiles"

This is the external Supplementary Information for our publication "G-type Halohydrin Dehalogenases Catalyze Ring Opening Reactions of Cyclic Epoxides with Diverse Anionic Nucleophiles". The .zip files contain the raw NMR data for all compounds as well as the protein structural data described in the manuscript.

Climate Change and 2030 Cooling Demand in Ahmedabad, India: Opportunities for Expansion of Renewable Energy and Cool Roofs (Supplemental Information)

Supplemental information and analysis files for article, "Climate change and 2030 cooling demand in Ahmedabad, India: opportunities for expansion of renewable energy and cool roofs" (Original article available at: https://doi.org/10.1007/s11027-022-10019-4)

opencc-by-4.0Dec 2021View details →

Supporting information for Parametrized regionalization of paper recycling life-cycle assessment

This supporting information provides the numerical results for (1) the process parameters' regionalization (S4); (2) the regionalized LCA climate change results for three different paper grades (S7); (3) the destinations of the mixed paper bales exiting Quebec's sorting centers (S8); (4) the LCA results for the scenarios for Quebec's case study (S9) and (5) the sensitivity analysis results, performed on the most uncertain parameters from Quebec's case study (S10).

opencc-by-4.0Jul 2022View details →

Dataset for the manuscript "Crowding results from optimal integration of visual targets with contextual information"

There are seven experimental datasets, two program with which data are collected, two supplemetary programs needed to run the main code and one program to analyse data. Two .txt files are included, where we describe how to use the stimulation and analysis programs.

opencc-by-4.0Jul 2022View details →

Research data for `Quantifying information scrambling via Classical Shadow Tomography on Programmable Quantum Simulators'

Research data associated with the paper `Quantifying information scrambling via Classical Shadow Tomography on Programmable Quantum Simulators'. Contains raw data obtained from simulations run on the IBM quantum device ibm_lagos.

opencc-by-4.0Jul 2022View details →

Supporting Information for Article "MarINvaders: A web toolkit of marine species for use in environmental assessments"

An Excel file with the overview of number of species per ecoregion and classes of alien species is available online. The code for querying and harmonizing the databases was published as a separate package (see Lonka et al. (2021))and available with an open source (GPL v3) license at <a href="https://gitlab.com/marinvaders/marinvaders">https://gitlab.com/marinvaders/marinvaders</a>.