Find research datasets worth reusing

Data from: Fundamental study of multi-track friction surfacing deposits for dissimilar aluminum alloys with application to additive manufacturing

This dataset contains the data for the publication "Fundamental study of multi-track friction surfacing deposits for dissimilar aluminum alloys with application to additive manufacturing".

Technical Reports: Methods - The application of temperature and light intensity as intermittency sensors in a temporary pond

Dataset for Technical Reports: Methods - The application of temperature and light intensity as intermittent sensors in a temporary pond.

TreeSatAI Benchmark Archive for Deep Learning in Forest Applications

Context and Aim Deep learning in Earth Observation requires large image archives with highly reliable labels for model training and testing. However, a preferable quality standard for forest applications in Europe has not yet been determined. The TreeSatAI consortium investigated numerous sources for annotated datasets as an alternative to manually labeled training datasets. We found the federal forest inventory of Lower Saxony, Germany represents an unseen treasure of annotated samples for training data generation. The respective 20-cm Color-infrared (CIR) imagery, which is used for forestry management through visual interpretation, constitutes an excellent baseline for deep learning tasks such as image segmentation and classification.   Description The data archive is highly suitable for benchmarking as it represents the real-world data situation of many German forest management services. One the one hand, it has a high number of samples which are supported by the high-resolution aerial imagery. On the other hand, this data archive presents challenges, including class label imbalances between the different forest stand types. The TreeSatAI Benchmark Archive contains: <ul> <li> 50,381 image triplets (aerial, Sentinel-1, Sentinel-2) </li> <li> synchronized time steps and locations </li> <li> all original spectral bands/polarizations from the sensors </li> <li> 20 species classes (single labels) </li> <li> 12 age classes (single labels) </li> <li> 15 genus classes (multi labels) </li> <li> 60 m and 200 m patches </li> <li> fixed split for train (90%) and test (10%) data </li> <li> additional single labels such as English species name, genus, forest stand type, foliage type, land cover </li> </ul> The geoTIFF and GeoJSON files are readable in any GIS software, such as QGIS.  For further information, we refer to the PDF document in the archive and publications in the reference section.   Version history v1.0.2 - Minor bug fix multi label JSON file v1.0.1 - Minor bug fixes in multi label JSON file and description file v1.0.0 - First release   Citation Ahlswede, S., Schulz, C., Gava, C., Helber, P., Bischke, B., Förster, M., Arias, F., Hees, J., Demir, B., and Kleinschmit, B.: TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing, Earth Syst. Sci. Data, 15, 681–695, <a href="https://doi.org/10.5194/essd-15-681-2023">https://doi.org/10.5194/essd-15-681-2023</a>, 2023.   GitHub Full code examples and pre-trained models from the dataset article (Ahlswede et al. 2022) using the TreeSatAI Benchmark Archive are published on the GitLab and GitHub repositories of the Remote Sensing Image Analysis (RSiM) Group  (<a href="https://git.tu-berlin.de/rsim/treesat_benchmark">https://git.tu-berlin.de/rsim/treesat_benchmark</a>) and the Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) (<a href="https://github.com/DFKI/treesatai_benchmark">https://github.com/DFKI/treesatai_benchmark</a>). Code examples for the sampling strategy can be made available by Christian Schulz via email request.   Folder structure We refer to the proposed folder structure in the PDF file. <ul> <li> Folder “aerial” contains the aerial imagery patches derived from summertime orthophotos of the years 2011 to 2020. Patches are available in 60 x 60 m (304 x 304 pixels). Band order is near-infrared, red, green, and blue. Spatial resolution is 20 cm. </li> <li> Folder “s1” contains the Sentinel-1 imagery patches derived from summertime mosaics of the years 2015 to 2020. Patches are available in 60 x 60 m (6 x 6 pixels) and 200 x 200 m (20 x 20 pixels). Band order is VV, VH, and VV/VH ratio. Spatial resolution is 10 m. </li> <li> Folder “s2” contains the Sentinel-2 imagery patches derived from summertime mosaics of the years 2015 to 2020. Patches are available in 60 x 60 m (6 x 6 pixels) and 200 x 200 m (20 x 20 pixels). Band order is B02, B03, B04, B08, B05, B06, B07, B8A, B11, B12, B01, and B09. Spatial resolution is 10 m. </li> <li> The folder “labels” contains a JSON string which was used for multi-labeling of the training patches. Code example of an image sample with respective proportions of 94% for Abies and 6% for Larix is: "Abies_alba_3_834_WEFL_NLF.tif": [["Abies", 0.93771], ["Larix", 0.06229]] </li> <li> The two files “test_filesnames.lst” and “train_filenames.lst” define the filenames used for train (90%) and test (10%) split. We refer to this fixed split for better reproducibility and comparability. </li> <li> The folder “geojson” contains geoJSON files with all the samples chosen for the derivation of training patch generation (point, 60 m bounding box, 200 m bounding box). </li> </ul> CAUTION: As we could not upload the aerial patches as a single zip file on Zenodo, you need to download the 20 single species files (aerial_60m_…zip) separately. Then, unzip them into a folder named “aerial” with a subfolder named “60m”. This structure is recommended for better reproducibility and comparability to the experimental results of Ahlswede et al. (2022),    Join the archive Model training, benchmarking, algorithm development… many applications are possible! Feel free to add samples from other regions in Europe or even worldwide. Additional remote sensing data from Lidar, UAVs or aerial imagery from different time steps are very welcome. This helps the research community in development of better deep learning and machine learning models for forest applications. You might have questions or want to share code/results/publications using that archive? Feel free to contact the authors.   Project description This work was part of the project TreeSatAI (Artificial Intelligence with Satellite data and Multi-Source Geodata for Monitoring of Trees at Infrastructures, Nature Conservation Sites and Forests). Its overall aim is the development of AI methods for the monitoring of forests and woody features on a local, regional and global scale. Based on freely available geodata from different sources (e.g., remote sensing, administration maps, and social media), prototypes will be developed for the deep learning-based extraction and classification of tree- and tree stand features. These prototypes deal with real cases from the monitoring of managed forests, nature conservation and infrastructures. The development of the resulting services by three enterprises (liveEO, Vision Impulse and LUP Potsdam) will be supported by three research institutes (German Research Center for Artificial Intelligence, TUB Remote Sensing Image Analysis Group, TUB Geoinformation in Environmental Planning Lab).   Project publications Ahlswede, S., Schulz, C., Gava, C., Helber, P., Bischke, B., Förster, M., Arias, F., Hees, J., Demir, B., and Kleinschmit, B.: TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing, Earth System Science Data, 15, 681–695, <a href="https://doi.org/10.5194/essd-15-681-2023">https://doi.org/10.5194/essd-15-681-2023</a>, 2023. Schulz, C., Förster, M., Vulova, S. V., Rocha, A. D., and Kleinschmit, B.: Spectral-temporal traits in Sentinel-1 C-band SAR and Sentinel-2 multispectral remote sensing time series for 61 tree species in Central Europe. Remote Sensing of Environment, 307, 114162, <a href="https://doi.org/10.1016/j.rse.2024.114162">https://doi.org/10.1016/j.rse.2024.114162</a>, 2024.   Conference contributions Ahlswede, S. Madam, N.T., Schulz, C., Kleinschmit, B., and Demіr, B.: Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree Species Classification Based on Explanation Methods, IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, <a href="https://doi.org/10.48550/arXiv.2201.07495">https://doi.org/10.48550/arXiv.2201.07495</a>, 2022. Schulz, C., Förster, M., Vulova, S., Gränzig, T., and Kleinschmit, B.: Exploring the temporal fingerprints of mid-European forest types from Sentinel-1 RVI and Sentinel-2 NDVI time series, IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, <a href="https://doi.org/10.1109/IGARSS46834.2022.9884173">https://doi.org/10.1109/IGARSS46834.2022.9884173</a>, 2022. Schulz, C., Förster, M., Vulova, S., and Kleinschmit, B.: The temporal fingerprints of common European forest types from SAR and optical remote sensing data, AGU Fall Meeting, New Orleans, USA, 2021. Kleinschmit, B., Förster, M., Schulz, C., Arias, F., Demir, B., Ahlswede, S., Aksoy, A.K., Ha Minh, T., Hees, J., Gava, C., Helber, P., Bischke, B., Habelitz, P., Frick, A., Klinke, R., Gey, S., Seidel, D., Przywarra, S., Zondag, R., and Odermatt B.: Artificial Intelligence with Satellite data and Multi-Source Geodata for Monitoring of Trees and Forests, Living Planet Symposium, Bonn, Germany, 2022. Schulz, C., Förster, M., Vulova, S., Gränzig, T., and Kleinschmit, B.: Exploring the temporal fingerprints of sixteen mid-European forest types from Sentinel-1 and Sentinel-2 time series, ForestSAT, Berlin, Germany, 2022.

Dataset for "Intra-intestinal analysis of the functional activity of microbiomes and its application to the common marmoset intestine"

Dataset: data1_ExpressionProfile.xlsx: gene expression profile data2_AnnotationProfile.xlsx: gene annotation profile

Intra-intestinal analysis of the functional activity of microbiomes and its application to the common marmoset intestine

Supplementary Table Captions: Table S7. Assignment of genes to unknown gene clusters Table S8. AUC used to determine parameters for covariation analysis by benchmarking Table S9. Linked unknown gene clusters Table S10. Information on common marmosets Table S11. Dual index sequences Table S12. Sequencing statistics Table S13. RIN score of total RNA samples Table S14. Top 20 bacterial species in taxonomic profiling Table S15. Number of genes covered by the nonchimaeric genome in each parameter at the assembly step Table S16. Number of genes covered by the nonchimaeric genome in each parameter at the merge step Table S17. Distance between intestinal sites

PID Registration Service Demo Application

The demonstration will cover the four functions: verification, registration, identified user taskId and taskId status.  <ul> <li> The API Reference Overview demonstrates four functions of the pid registration service for variables: the verification (validation) of the variables and the metadata to be registered. It returns a validation report. </li> <li> The variable registration prototype (not implemented yet) returns a taskId to follow up on the registration status.  </li> <li> The Variable status tasks check the status of the tasks according to the identified user </li> <li> The variable/status/task/{taskId} that returns either the verification status or register process identified by the taskId. </li> </ul> The demonstration also cover the status pages. The first status page organizes the tasks by Username, amount of tasks, and their respective status (Finished, Pending/running or failed). The user task overview demonstrates the JSON file validation. Some tasks can register a bunch of variables, taking longer, so it is important to follow up on the running tasks from the user's perspective. The status page breakdowns the follow-up for each variable registration requested at a given taskId.

ARIA - Accessible Rich Internet Applications Landmarks classified dataset

ARIA Landmarks classified dataset. ARIA Landmarks are regions of web applications that can be accessed by specific keyboard shortcuts and are specially useful for blind users. They comprise the banner, complementary, contentinfo, form, main, navigation, region and search classes. In the dataset, samples represent DOM elements of web applications with different attributes extracted when these elements are rendered in the browser.

opencc-by-4.0Dec 2020View details →

Multiple Partitioning of Multiplex Signed Networks: Application to European Parliament Votes

Presentation. For more than a decade, graphs have been used to model the voting behavior taking place in parliaments. However, the methods described in the literature suffer from several limitations. The two main ones are that 1) they rely on some temporal integration of the raw data, which causes some information loss; and/or 2) they identify groups of antagonistic voters, but not the context associated with their occurrence. In this article, we propose a novel method taking advantage of multiplex signed graphs to solve both these issues. It consists in first partitioning separately each layer, before grouping these partitions by similarity. We show the interest of our approach by applying it to a European Parliament dataset. Particularly, we study the voting behavior of French and Italian MEPs on "Agriculture and Rural Development" (AGRI) during the 2012-13 legislative year. These are the data used in the following paper: <ul> <li>N. Arınık, R. Figueiredo, and V. Labatut, “Multiple partitioning of multiplex signed networks: Application to European Parliament votes,” Social Networks, vol. 60, pp. 83–102, 2020. DOI: <a href="http://doi.org/10.1016/j.socnet.2019.02.001">10.1016/j.socnet.2019.02.001</a> ⟨<a href="https://hal.archives-ouvertes.fr/hal-02082574">hal-02082574</a>⟩</li> </ul> Source code. The code source is accessible on GitHub: <a href="https://github.com/CompNet/MultiNetVotes">https://github.com/CompNet/MultiNetVotes</a> Citation. If you use these data our this source code, please cite the above paper. <code>@Article{Arinik2020,</code> <code>  author    = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code> <code>  title     = {Multiple Partitioning of Multiplex Signed Networks: Application to {E}uropean {P}arliament Votes},</code> <code>  journal   = {Social Networks},</code> <code>  year      = {2020},</code> <code>  volume    = {60},</code> <code>  pages     = {83-102},</code> <code>  doi       = {10.1016/j.socnet.2019.02.001},</code> <code>}</code> ---------------------------------------------- Details. # RAW INPUT FILES The 'itsyourparliament' folder contains all raw input files for further data processing. This is the same raw data that can be found in our previous Figshare repository: https://doi.org/10.6084/m9.figshare.5785833 The folder structure is as follows: * itsyourparliament/ ** domains: There are 28 domain files. Each file corresponds to a domain (such as Agriculture, Economy, etc.) and contains corresponding vote identifiers and their "itsyourparliament.eu" links. ** meps: There are 870 Members of Parliament (MEP) files. Each file contains the MEP information (such as name, country, address, etc.) ** votes: There are 7513 vote files. Each file contains the votes expressed by MEPs # ROLLCALL NETWORKS This folder contains two separate zip files regarding rollcall networks: - rollcall-networks: This folder contains only the rollcall networks that are used in the article. - all-rollcall-networks: For those who are interested in other countries or domains, we make available all rollcall networks that we can extract from raw data. Note that these rollcall networks constitute the layers of the input signed multplex network, as illustrated in Figure 1 of the article. Note also that we consider three vote types in our network extraction process: FOR, AGAINST and ABSTAIN. # ROLLCALL PARTITIONS Note that MEPs who voted similarly are connected together by positive links, and are connected by negative links to MEPs that voted differently from them. MEPs who did not vote at all (ABSENT) are isolates (nodes without any neighbor). We identify the factions of similarly voting MEPs in the graph by solving the Correlation Clustering problem (CC). The rollcall partitions correspond to voting patterns, as illustrated in Figure 1 of the article. # ROLLCALL CLUSTERING This folder contains the results of Steps 3 and 4 of our workflow (see Figure 1 in the article). The structure of this folder is as follows: |__ votetypes=FAA/: 'FAA' means we consider three vote types in our analysis: FOR, AGAINST and ABSTAIN. |__ F.purity-k=2-sil=SILHOUETTE_SCORE |__ clu=CLUSTER_NO/ |__ network: It corresponds to the network created through the similarity network-based approach, as explained in Section 4.4 of the article. |__ partition: It corresponds to the characteristic voting pattern, as explained in Section 4.4 of the article. ---------------------------------------------- Funding: this research benefited from the support of the Agorantic FR 3621, as well as the FMJH Program PGMO and from the support to this program from EDF-THALES-ORANGE-CRITEO.

Guaranteed Publisher/Subscriber Anonymity with Interest Profiles for Smart Cities Applications: Trends and Gaps

Studies carried out in the context of anonymous Publisher/Subscriber (MQTT protocol) and with a profile of interest, are topics that the literature and the IoT (Internet of Things) industry face to implement models, architecture and architectural patterns, mainly related to anonymity, and the profile of interest there is nothing in literature or industry. In this way, the creation of an architecture based on these themes can facilitate the communication of IoT devices in smart cities. Therefore, this article aims to identify in the literature/industry how to guarantee the anonymity of Publisher/Subscriber with interest profiles using the MQTT protocol, and as future works to create an architecture based on the theme so that it can be used by academia and industry. IoT. The search string returned 291 (Two hundred and Ninety-One) works, of which 4 (Four) were selected according to the criteria of the Systematic Literature Review.

opencc-by-4.0Apr 2022View details →

[DATASET] Design and characterization of an RF applicator for in vitro tests of electromagnetic hyperthermia doi.org/10.3390/s22103610

[DATASET] Design and characterization of an RF applicator for in vitro tests of electromagnetic hyperthermia <a href="https://doi.org/10.3390/s22103610">doi.org/10.3390/s22103610</a> The data used in the paper are dived in separate folder for each published figure.

A Dataset of Synthetic Images of Outdoor Scenes Taken from Sidewalks, for Temporal Semantic Segmentation Applications

This dataset has been generated using the CARLA simulator (release 0.9.11), an open-source 3D simulator for experiments in autonomous vehicle, based on the Unreal Engine game engine. It comes with pre-made city environment maps. CARLA is distributed with several integrated maps as well as parameters to increase the variety in the dataset. In the release that we have used, there are 13 semantic segmentation classes: None, Building, Fence, Other, Pedestrian, Pole, Lane-marking, Road, Sidewalk, Vegetation, Vehicle, Wall, and Traffic sign. The "None" category corresponds to textures that are not part of an object, such as lawns which are not part of "Vegetation", or sky. In the “Other” category are found objects that are not included in the other classes like plant and flower pots. For smart mobility applications, the “Sidewalks” and “Road” classes are of particular importance to find the way forward, as well as “Buildings” and “Poles” for obstacle avoidance. Sequences are made of 4 images. The dataset is composed of 46436 frames (11609 sequences) partitioned in 41024 frames (10256 sequences) for train, 2696 frames (674 sequences) for validation, and 2716 for test (679 sequences). The size of the images is 800 x 600 (resp. width x height). Additionaly, we have generated another smaller dataset with images taken from 2 different viewpoints: one located on the road and the other located on the sidewalk. The number of frames for train/validation/test is respectively 7288 (1822 sequences) partitioned in 6344 (1687 sequences) for train, 416 frames (104 sequences) for validation, and 424 for test (106 sequences). This smaller dataset is aimed at showing the importance of the viewpoint in the result of semantic segmentation. This can be done by cross-validation: learning on images taken from a viewpoint located on the road and test on images with a viewpoint located on the sidewalk, and vice versa.

Data from: Improved FIFRELIN de-excitation model for neutrino applications

New FIFRELIN cascades for the isotopes 156,158Gd are distributed to the community, to be used for various applications. The new cascades feature an improved modeling of de-excitation. The main improvements are: 1) Inclusion of primary transitions from EGAF database. 2) Treatment of gamma-directional correlations 3) Improved physics for the Internal Conversion process and X ray emission. With the use of the files provided, please cite the following publication: H. Almazán et al, <a href="http://doi.org/10.1140/epja/s10050-023-00977-x">The European Physical Journal A volume 59, Article number: 75 (2023)</a>  DOI: <a href="http://doi.org/10.1140/epja/s10050-023-00977-x">https://doi.org/10.1140/epja/s10050-023-00977-x</a>

opencc-by-4.0Aug 2022View details →

Dataset for "Application of dissipative particle dynamics to interfacial systems: Parameterization and scaling"

Dataset for figures of the upcoming article "Application of dissipative particle dynamics to interfacial systems: Parameterization and scaling" submitted to "AIP Advances".

opencc-by-4.0Aug 2022View details →

A topic modelling application for violence against the women through text mining techniques

This research was focused on a social problem that affects worldwide. Violence Against Women (VAW) and girls occurs in many forms with different levels of aggressiveness. There is a wide amount of data on the internet about real cases of VAW that can be gathered for further study. Many researchers have done studies on structured data sets which facilitates their manipulation and understanding. But in this work digital news (data source large non-structured text) was used. These are the data used for the development of the investigation.This file was extracted from our mongoDB database. The data set contains real news of violence against women, which were organized with their date, the title and the body of the news.

opencc-by-4.0Oct 2021View details →

Application-Oriented Performance Benchmarks for Quantum Computing

Complete dataset and Jupyter Notebook used to produce image files for the paper at     https://arxiv.org/abs/2110.03137. To execute the notebook, copy the .ipynb file and the _data directory to the top level of the repository at:     https://github.com/SRI-International/QC-App-Oriented-Benchmarks

opencc-by-4.0Jan 2022View details →

Scientific Applications of Unmanned Vehicles in Svalbard

This database was generated in the scope of the State of Environmental Science in Svalbard (SESS) report. <a href="https://zenodo.org/record/4293283/files/SESS2020_UAV_Svalbard.pdf?download=1">Scientific Applications of Unmanned Vehicles in Svalbard</a>. SESS Report 2020, Svalbard Integrated Arctic Earth Observing System. DOI: 10.5281/zenodo.4293283

opencc-by-4.0Mar 2021View details →

Free-standing electrochemically coated MoSx based 3D-printed nanocarbon electrode for solid-state supercapacitor application

Raw data of journal article "Free-standing electrochemically coated MoSx based 3D-printed nanocarbon electrode for solid-state supercapacitor application" DOI: 10.1039/d0nr06479c

opencc-by-4.0Jul 2021View details →

Flexible Graphite−Poly(Lactic Acid) Composite Films as Large-Area Conductive Electrodes for Energy Applications

Raw data of energy storage part -author Kalyan Ghosh

dryad40/100

Data from: A novel laboratory method to simulate climatic stress with successful application to experiments with medically relevant ticks

Ticks are the most important vectors of zoonotic disease-causing pathogens in North America and Europe. Many tick species are expanding their geographic range. Although correlational evidence suggests that climate change is driving the range expansion of ticks, experimental evidence is necessary to develop a mechanistic understanding of ticks' response to a range of climatic conditions. Previous experiments used simulated microclimates, but these protocols require hazardous salts or expensive laboratory equipment to manipulate humidity. We developed a novel, safe, stable, convenient, and economical method to isolate individual ticks and manipulate their microclimates. The protocol involves placing individual ticks in plastic tubes, and placing six tubes along with a commercial two-way humidity control pack in an airtight container. We successfully used this method to investigate how humidity affects survival and host-seeking (questing) behavior of three tick species: the lone star tick (Amblyomma americanum), American dog tick (Dermacentor variabilis), and black-legged tick (Ixodes scapularis). We placed 72 adult females of each species individually into plastic tubes and separated them into three experimental relative humidity (RH) treatments representing distinct climates: 32% RH, 58% RH, and 84% RH. We assessed the survival and questing behavior of each tick for 30 days. In all three species, survivorship significantly declined in drier conditions. Questing height was negatively associated with RH in Amblyomma, positively associated with RH in Dermacentor, and not associated with RH in Ixodes. The frequency of questing behavior increased significantly with drier conditions for Dermacentor but not for Amblyomma or Ixodes. This report demonstrates an effective method for assessing the viability and host-seeking behavior of tick vectors of zoonotic diseases under different climatic conditions.

opencc-zeroSep 2022View details →