Find research datasets worth reusing

opencc-by-4.0Apr 2022View details →

Fig. 2. – A and B in Diversity of intestinal protozoa and clinical signs associated in wild-caught Phoneutria nigriventer kept in captivity for the anti-arachnid serum production

Fig. 2. – A and B, Diarrheal stools, without differentiation of solid and liquid portion. C, Normal stools of Phoneutria nigriventer (red arrow). The white arrow indicates the urine portion, white in color due to urate. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

dryad40/100

Data from: Corals that survive repeated thermal stress show signs of selection and acclimatization

Climate change is transforming coral reefs by increasing the frequency and intensity of marine heatwaves, often leading to coral bleaching and mortality. Coral communities have demonstrated modest increases in thermal tolerance following repeated exposure to moderate heat stress, but it is unclear whether these shifts represent acclimatization of individual colonies or mortality of thermally susceptible individuals. For corals that survive repeated bleaching events, it is important to understand how past bleaching responses impact future growth potential. Here, we track the bleaching responses of 1,832 corals in leeward Maui through multiple marine heatwaves and document patterns of coral growth and survivorship over a seven-year period. While we find limited evidence of acclimatization at population scales, we document reduced bleaching over time in specific individuals, primarily in the stress-tolerant taxa Porites lobata, indicative of acclimatization. For corals that survived both bleaching events, we find no relationship between bleaching response and coral growth in three of four taxa studied. This decoupling between bleaching and growth suggests that coral survivorship is a better indicator of future growth than is a coral's bleaching history. Based on these results, we recommend restoration practitioners in Hawaiʻi obtain outplants from Porites and Montipora colonies with a proven track-record of growth and survivorship, rather than devote resources toward identifying and cultivating bleaching-resistant phenotypes. Survivorship followed a latitudinal thermal stress gradient, but because this gradient was small, it is likely that local environmental factors also drove differences in coral performance between sites. Efforts to reduce human impacts at low performing sites would likely improve coral survivorship in the future.

opencc-zeroApr 2024View details →

opencc-by-4.0Jul 2024View details →

Real-World Signed Graphs Annotated for Whole Graph Classification

Description: this corpus was designed as an experimental benchmark for a task of signed graph classification. It is composed of three datasets derived from external sources and adapted to our needs: <ul> <li>SpaceOrigin Conversations [1]: set of conversational graphs, each one associated to a situation of verbal abuse vs. normal situation. These conversations model interactions happening in chatrooms hosted by an MMORPG/ The graphs were originally unsigned: we attributed signed to the edges based on the polarity of the exchanged messages.  </li> <li>Correlation Clustering Instances [2]: set of graph generated randomly as instances of the Correlation Clustering problem, which consists in partitioning signed graphs. These graphs are not associated in any class in the original paper. We proposed a class based on certain features of the space of optimal solutions explored in [2].</li> <li>European Parliament Roll-Calls [3]: vote networks extracted from the activity of French Members of the European Parliament. The original data does not have any class associated to the networks: we proposed one based on the number of political factions identified in each network in [3]. </li> </ul> These data were used in [4] in order to train and assess various representation learning methods. The authors proposed Signed Graph2vec, a signed variant of Graph2vec; WSGCN, a whole-graph variant of Signed Graph Convolutional Networks (SGCN), and use an aggregated version of Signed Network Embeddings (SiNE) as a baseline. The article provides more information regarding the properties of the datasets, and how they were constituted. Software: the software used to train the representation learning methods and classifiers is publicly available online: <a href="https://github.com/CompNet/SWGE">SWGE</a>. References: <ol> <li>Papegnies, É.; Labatut, V.; Dufour, R. & Linarès, G. Conversational Networks for Automatic Online Moderation. IEEE Transactions on Computational Social Systems, 2019, 6:38-55. DOI: <a href="http://doi.org/10.1109/TCSS.2018.2887240">10.1109/TCSS.2018.2887240</a> ⟨<a href="https://hal.science/hal-01999546">hal-01999546</a>⟩</li> <li>Arınık, N.; Figueiredo, R. & Labatut, V. Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs. Journal of Complex Networks, 2020, 8(6):cnaa025. DOI: <a href="http://doi.org/10.1093/comnet/cnaa025">10.1093/comnet/cnaa025</a> ⟨<a href="https://hal.science/hal-02994011">hal-02994011</a>⟩</li> <li>Arınık, N.; Figueiredo, R. & Labatut, V. Multiple partitioning of multiplex signed networks: Application to European parliament votes. Social Networks, 2020, 60:83-102. 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.science/hal-02082574">hal-02082574</a>⟩</li> <li>Cécillon, N.; Labatut, V.; Dufour, R. & Arınık, N. Whole-Graph Representation Learning For the Classification of Signed Networks. IEEE Access, 2024, 12:151303-151316. DOI: <a href="https://dx.doi.org/10.1109/ACCESS.2024.3472474">10.1109/ACCESS.2024.3472474</a> <a href="https://hal.archives-ouvertes.fr/hal-04712854" rel="nofollow">⟨hal-04712854⟩</a></li> </ol> Funding: part of this work was funded by a grant from the Provence-Alpes-Côte-d'Azur region (PACA, France) and the Nectar de Code company. Citation: If you use this data or the associated source code, please cite article [4]: <code>@Article{Cecillon2024,</code> <code>  author    = {Cécillon, Noé and Labatut, Vincent and Dufour, Richard and Arınık, Nejat},</code> <code>  title     = {Whole-Graph Representation Learning For the Classification of Signed Networks},</code> <code>  journal   = {IEEE Access},</code> <code>  year      = {2024},</code> <code>  volume    = {12},</code> <code>  pages     = {151303-151316},</code> <code>  doi       = {10.1109/ACCESS.2024.3472474},</code> <code>}</code>

opencc-by-4.0Jun 2024View details →

Synthetic data (Part 2) for HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields

opencc-by-4.0Jun 2024View details →

Synthetic data (Part 1) for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"

opencc-by-4.0Jun 2024View details →

Processed data and trained models for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"

############# HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024 ############# Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis. Affiliation: EPFL Date: June, 2024 Link to the CVPR article: <a href="https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf">https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</a> Link to the Arxiv article: <a href="https://arxiv.org/abs/2402.17062">https://arxiv.org/abs/2402.17062</a> -------------------------------- <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed data of the interacting objects and SDF samples. Meanwhile, we also include the trained model weights here.</div> <div>The overall structure of the data is:</div> <div>├── <a href="../api/records/11668766/draft/files/ckpts.zip/content" target="_blank" rel="noopener noreferrer">ckpts.zip</a>                                - Contains the trained weights model on different datasets (DexYCB and HO3Dv2)</div> <div>├── <a href="../api/records/11668766/draft/files/annotations.zip/content" target="_blank" rel="noopener noreferrer">annotations.zip</a>                     - Contains the preprocessed annotations of DexYCB and HO3Dv2 for efficient data loading.</div> <div>├── <a href="../api/records/11668766/draft/files/simple_ycb_models.zip/content" target="_blank" rel="noopener noreferrer">simple_ycb_models.zip</a>         - Contains the preprocessed YCB objects for batched evaluation.</div> <div>├── <a href="../api/records/11668766/draft/files/test.zip/content" target="_blank" rel="noopener noreferrer">test.zip</a>                                  - Contains the processed SDF files for DexYCB test set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_release.zip</a>                   - Contains the HO3Dv2 submission trained with HO3D training set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_render_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_render_release.zip</a>       - Contains the HO3Dv2 submission trained with HO3D training set and rendering set.</div> <div> </div> <div>The code to reproduce the results is available at: <a href="https://github.com/amathislab/HOISDF">https://github.com/amathislab/HOISDF</a></div> <div> </div> <div>--------------------------------</div> </div> If you find our code, weights, predictions or ideas useful, please cite: @inproceedings{qi2024hoisdf,   title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},   author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},   booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},   pages={10392--10402},   year={2024} }

FIG. 12 in Sacrifice du cochon ou mise en relation? Du sang, des signes et des sons dans trois contextes rituels chez les Alangan, les Blaan et les Ibaloy des Philippines

FIG. 12. — Offrandes faites aux kedaring (esprits des morts). Crédit photo: Frédéric Laugrand, 2019.

FIG. 1 in Sacrifice du cochon ou mise en relation? Du sang, des signes et des sons dans trois contextes rituels chez les Alangan, les Blaan et les Ibaloy des Philippines

FIG. 1. — June (un aîné) maintient la hâche sur le corps du cochon. À gauche, les Alangan ont placé des plantes cultivées et la tige de fer. La bougie n'est pas visible sur l'image, mais elle est au premier plan, près de la main de June. Crédit photo: Frédéric Laugrand, 2012.

FIG. 15 in Sacrifice du cochon ou mise en relation? Du sang, des signes et des sons dans trois contextes rituels chez les Alangan, les Blaan et les Ibaloy des Philippines

FIG. 15. — Un jeune homme place le owik (pieu en goyavier ayant servi à la mise à mort), maculé de sang et sur lequel est suspendu la vésicule biliaire du cochon, sous le toit de la cuisine. Crédit photo: Frédéric Laugrand, 2019.

FIG. 10 in Sacrifice du cochon ou mise en relation? Du sang, des signes et des sons dans trois contextes rituels chez les Alangan, les Blaan et les Ibaloy des Philippines

FIG. 10. — Victoire! Lors du datah sdè, le cochon est touché par la lance et la maladie est transférée dans son corps. Bolo-Bolo. Crédit photo: Antoine Laugrand, 2016.

FIG. 11 in Sacrifice du cochon ou mise en relation? Du sang, des signes et des sons dans trois contextes rituels chez les Alangan, les Blaan et les Ibaloy des Philippines

FIG. 11. — Dessin du datah sdè par Lory Macatunao, Blaan de Mindanao. Little Baguio. Crédit photo: Frédéric Laugrand, 2015.

FIG. 13 in Sacrifice du cochon ou mise en relation? Du sang, des signes et des sons dans trois contextes rituels chez les Alangan, les Blaan et les Ibaloy des Philippines

FIG. 13. — Lolo Salcedo, un aîné ibaloy, mettant à mort un cochon en utilisant la technique du owik (pieu fabriqué à partir d'une branche de goyavier). Crédit photo: Antoine Laugrand, 2019.

FIG. 2 in Sacrifice du cochon ou mise en relation? Du sang, des signes et des sons dans trois contextes rituels chez les Alangan, les Blaan et les Ibaloy des Philippines

FIG. 2. – Artus (un aîné) arborant son gulok (une machette) à la ceinture, assène des petits coups de pied au cochon pour « le faire crier ». Crédit photo: Antoine Laugrand, 2012.

Greek News Sign Language Dataset - Part A

Part A entails 1.000 signed phrases of crime-related news stories broadcasted in Greece.

Greek News Sign Language Dataset - Part B

Part B entails 989 signed phrases of crime-related news stories broadcasted in Greece.

Costarican Sign Language (LESCO) emergency-based signs dataset

This dataset was part of Juan Zamora-Mora's doctoral dissertation on the recognition of Costarican Sign Language (LESCO) in emergency situations from Aspen University. The dataset is composed of 39 signs. There are three videos for each sign on each folder. Videos have been cropped and are on average 1 second long.  This dataset contains a total of  mp4 117 videos.

opencc-by-4.0Oct 2019View details →

Space of Optimal Solutions of the Correlation Clustering Problem for Complete Signed Graphs

Description. This is the data used in the experiments of the following paper: <ul> <li>N. Arınık, R. Figueiredo, and V. Labatut, “Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs,” Journal of Complex Networks 8(6):cnaa025, 2020. DOI: <a href="http://doi.org/10.1093/comnet/cnaa025">10.1093/comnet/cnaa025</a> ⟨<a href="https://hal.archives-ouvertes.fr/hal-02994011">hal-02994011</a>⟩</li> </ul> This dataset contains: <ul> <li>Plot files used in the article;</li> <li>Input signed networks;</li> <li>All optimal solutions (i.e. optimal solution space) of the corresponding networks;</li> <li>Evaluation files.</li> </ul> Source code. The code source is accessible on GitHub: <a href="https://github.com/CompNet/Sosocc">https://github.com/CompNet/Sosocc</a> Citation. If you use the data or source code, please cite the above article. <code>@Article{Arinik2020,</code> <code>  author    = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code> <code>  title     = {Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs},</code> <code>  journal   = {Journal of Complex Networks},</code> <code>  year      = {2020},</code> <code>  volume    = {8},</code> <code>  number    = {6},</code> <code>  pages     = {cnaa025},</code> <code>  doi       = {10.1093/comnet/cnaa025},</code> <code>}</code> -------------------------------------------- Details. # PLOT FILES * `Figure1.zip`: Figures showing that there might be many distinct optimal solutions of a small-sized network. * `Figure2.zip`: Figures showing that distinct optimal solutions of a given network might be partition-wise very similar or different. * `Figure4: All Results.zip`: Figure 4 in the article contains only a few plots regarding the results for space considerations. This zip file contains all plots, and it is organized by the values of `l0`. In each `l0` folder, the results are shown in three different perspectives: --- Detected Imbalance Percentage vs Graph Order (i.e. number of vertices) --- Prop mispl vs Graph order --- Graph order vs Prop mispl * `workflow.pdf`: The workflow of the methodology used in the article. * `Syrian network With All Solutions.pdf`: Syrian network (on top) with core part information through node colors, and its optimal solutions in which node colors represent partition information (on bottom).   #NETWORKS All networks are in `Input Signed Networks.tar.gz`. Networks are generated through a simple random model (available in https://github.com/CompNet/SignedBenchmark) designed to produce complete (or uncomplete) unweighted networks with built-in modular structure. There are 3 parameters used for the generation: <ol> <li>number of nodes (`n`)</li> <li>initial number of modules (`l0`)</li> <li>proportion of misplaced links, i.e. proportion of frustrated links, (`qm`)</li> </ol> Inside `Input Signed Networks.tar.gz`: NETWORKS |__n=NB-NODE_l0=INIT_NB_MODULE_dens=1.0000 ....|__propMispl=PROP_MISPL ........|__propNeg=PROP_NEG ............|__network=NETWORK_NO - The first hierarchy => the folders are named as follows: n=NB-NODE_l0=INIT-NB-MODULE_dens=1.0000 The number of nodes, the initial number of modules and the network density are given. The network density is always 1, since we treat only complete signed networks. - The second hierarchy => the folders are named as follows: propMispl=PROP_MISPL Proportion of misplaced links is given. - The third hierarchy => the folders are named as follows: propNeg=PROP_NEG Proportion of negative links (`qn`) is specified. `qn` changes depending on `n` and `l0`. Since only complete signed networks are studied, this parameter is automatically computed from the other input parameters. - The fourth hierarchy => the folders are named as follows: network=NETWORK_NO Network numbers are shown. In the end, thre are three file formats describing the same network content: GraphML (.graphml), Pajek NET (.net) or .G format. # PARTITIONS All partition results are in `Partition Results.tar.gz`. Note that all optimal partitions of a signed network are obtained through an exact partitioning method. The code source is accessible here: https://github.com/arinik9/ExCC Inside `Partition Results.tar.gz`: PARTITIONS |__n=NB-NODE_l0=INIT_NB_MODULE_dens=1.0000 ....|__propMispl=PROP_MISPL ........|__propNeg=PROP_NEG ............|__network=NETWORK_NO ................|__"ExCC-all" ....................|__"signed-unweighted" - The first hierarchy => the folders are named as follows: n=NB-NODE_l0=INIT-NB-MODULE_dens=1.0000 - The second hierarchy => the folders are named as follows: propMispl=PROP_MISPL - The third hierarchy => the folders are named as follows: propNeg=PROP_NEG - The fourth hierarchy => the folders are named as follows: network=NETWORK_NO - The fifth hierarchy => the folders are named as follows: "ExCC-all" The name of the partitioning method are shown. Since an exact partitioning method is used to obtain all distinct optimal solutions, it is named as "ExCC-all". - The sixth hierarchy => the folders are named as follows: "signed-unweighted" The type of signed networks are shown: signed and unweighted In the end, the partition results are located, and the file names are named as follows: membership.txt. Note that the first partition result number starts from zero.   # EVALUATIONS Evaluation results related to our plots are in `Evaluation Results.tar.gz. Note that the hierarchy of this folder is the same as that of 'Partitions'. Inside `Evaluation Results.tar.gz`: - `Best-k-for-kmedoids.csv`: It contains three columns. 1) the number of solution classes via kmedoids, 2) the best Silhouette score, 3) the best clustering in terms of Silhouette score, which represents solution classes. - `class-core-part-size-tresh=1.00.csv`. It indicates the proportion of core part size for each solution class. - `exec-time.csv`: It indicates the execution time in seconds. - `imbalance.csv`: It contains the information of imbalance as 1) count and 2) percentage - `nb-solution.csv`: It indicates the total number of solutions -------------------------------------------- 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.

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.

opencc-by-4.0Oct 2022View details →

Delphi survey on clinical signs and symptoms at primary health facilities

Data from a Delphi survey conducted among 30 primary health care workers in Tanzania. The Delphi survey was based on a recent Delphi study among international experts on predictors of sepsis in children under five and included questions about each clinical element based on three domains: 1. Reliability of measurement, 2. Frequency of finding an abnormal value, and 3. Level of training required. Additionally, availability of instruments to measure vital signs and other challenges in collecting each element were evaluated. The answers were classified using a 5-point Likert scale: minimal, moderate, high, not applicable, I don’t know. The answer options for the availability of vital sign instruments were yes/no/I don’t know. We also collected data on the professional background and expertise of the participants.

Text-fig. 13. Bivariate plots of teeth of Libycochoerus massai from Gebel Zelten (circles) and Moghara (+ sign). in New Suoid Fossils (Mammalia, Artiodactyla) From The Miocene Of Moghara, Egypt, And Gebel Zelten, Libya: Biochronological Implications

Text-fig. 13. Bivariate plots of teeth of Libycochoerus massai from Gebel Zelten (circles) and Moghara (+ sign).