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protein roles—in terms of their cellular functions from gene ontology—in various protein-protein interaction (PPI) graphs, with each graph corresponding to a different human tissue [41]. positional gene sets are used, motif gene sets and immunological signatures as features and gene ontology sets as labels (121 in total), collected from the Molecular Signatures Database [34]. The average graph contains 2373 nodes, with an average degree of 28.8.
NewsCLIPpings is a dataset for detecting mismatched images and captions. Different to previous misinformation datasets, in NewsCLIPpings both the images and captions are unmanipulated, but some of them are mismatched.
The LandCover.ai (Land Cover from Aerial Imagery) dataset is a dataset for automatic mapping of buildings, woodlands, water and roads from aerial images. ### Dataset features * land cover from Poland, Central Europe * three spectral bands - RGB * 33 orthophotos with 25 cm per pixel resolution (~9000x9500 px) * 8 orthophotos with 50 cm per pixel resolution (~4200x4700 px) * total area of 216.27 sq. km ### Dataset format * rasters are three-channel GeoTiffs with EPSG:2180 spatial reference system * masks are single-channel GeoTiffs with EPSG:2180 spatial reference system
The Flick Cropping Dataset consists of high quality cropping and pairwise ranking annotations used to evaluate the performance of automatic image cropping approaches. Source: https://arxiv.org/abs/1701.01480
Multi-Sensor All Weather Mapping (MSAW) is a dataset and challenge, which features two collection modalities (both SAR and optical). The dataset and challenge focus on mapping and building footprint extraction using a combination of these data sources. MSAW covers 120 km^2 over multiple overlapping collects and is annotated with over 48,000 unique building footprints labels, enabling the creation and evaluation of mapping algorithms for multi-modal data. Source: SpaceNet 6: Multi-Sensor All Weather Mapping Dataset
For change detection tasks, current open-source datasets mainly focus on building extraction (e.g., WHU building dataset and LEVIR-CD dataset) (Chen and Shi, 2020; Ji et al., 2018) and urban development monitoring (e.g., SECOND dataset, Google dataset and CDD dataset) (Yang et al., 2022; Peng et al., 2021; Lebedev et al., 2018), whereas datasets for natural disaster monitoring have been seldom investigated. Therefore, we sought to present the GVLM dataset, the first large-scale and open-source VHR landslide mapping dataset. It includes $17$ bitemporal very-high-resolution imagery pairs with a spatial resolution of $0.59$ m acquired via Google Earth service. Each sub-dataset contains a pair of bitemporal images and the corresponding ground-truth map. The total coverage of the dataset is $163.77 km2$. The landslide sites in different geographical locations have various sizes, shapes, occurrence times, spatial distributions, phenology states, and land cover types, resulting in considerable spectral heterogeneity and intensity variations in the remote sensing imagery. The GVLM dataset can be used to develop and evaluate machine/deep learning models for change detection, semantic segmentation and landslide extraction.
CUHK Image Cropping is a dataset for image cropping. The photos are of varying aesthetic quality and span a variety of image categories, including animal, architecture, human, landscape, night, plant and man-made objects. Each image is manually cropped by three expert photographers (graduate students in art whose primary medium is photography) to form three training sets. There are 1,000 photos in the dataset.
MERL Shopping is a dataset for training and testing action detection algorithms. The MERL Shopping Dataset consists of 106 videos, each of which is a sequence about 2 minutes long. The videos are from a fixed overhead camera looking down at people shopping in a grocery store setting. Each video contains several instances of the following 5 actions: "Reach To Shelf" (reach hand into shelf), "Retract From Shelf " (retract hand from shelf), "Hand In Shelf" (extended period with hand in the shelf), "Inspect Product" (inspect product while holding it in hand), and "Inspect Shelf" (look at shelf while not touching or reaching for the shelf). Source: Action Detection in Videos Image Source: Action Detection in Videos
DPPIN is a collection of dynamic networks, which consists of twelve generated dynamic protein-protein interaction networks of yeast cells, stored in twelve folders.
Usually, the information related to the crop types available in a given territory is annual information, that is, we only know the type of main crop grown over a year and we do not know any crops that have followed one another during the year and also we do not know when a particular crop is sown and when it is harvested. The main objective of this dataset is to create the basis for experimenting with suitable solutions to give a reliable answer to the above questions, or to propose models capable of producing dynamic segmentation maps that show when a crop begins to grow and when it is collected. Consequently, being able to understand if more than one crop has been grown in a territory within a year. In this dataset, we have 20 coverage classes as ground-truth values provided by Regine Lombardia. The mapping of the class labels used (see file lombardia-classes/classes25pc.txt) brings together some classes and provides the time intervals within which that category grows. The last two columns of the following table are respectively the date (month-day) of the start and end of the interval in which the class is visible during the construction of our dataset.
MaNGA is a component of the Fourth-Generation Sloan Digital Sky Survey whose goal is to map the detailed composition and kinematic structure of nearby galaxies. MaNGA uses integral field unit (IFU) spectroscopy to measure spectra for hundreds of points within each galaxy. MaNGA’s goal is to understand the “life history” of present-day galaxies from imprinted clues of their birth and assembly, through their ongoing growth via star formation and merging, to their death from quenching at late times. The primary MaNGA data products are composed of 3-D calibrated data cubes produced by the DRP[89] and 2-D maps of derived quantities, such as emission line fluxes, gas and stellar kinematics, and stellar population properties, produced by the DAP[149] from those cubes. The 3-D data cubes are constructed from a few tens to a few thousands of individual spectra that have been combined onto a regular grid. The 2-D maps of derived quantities are constructed by analyzing individual or binned groups of spaxels and constructing maps of the quantities at the relevant on-sky location.
Agricultural dataset collected on-board out weed removing robot. The dataset is composed by six different sequences in a soybean field and it contains stereo images, IMU measurements, wheel odometry and GPS-RTK (positional ground-truth)
face dataset
Mapping of detailed discipline tags to one of three broader disciplines (Arts, Science, Business)
The dataset contains synthetic training, validation and test data for occupancy grid mapping from lidar point clouds. Additionally, real-world lidar point clouds from a test vehicle with the same lidar setup as the simulated lidar sensor is provided. Point clouds are stored as PCD files and occupancy grid maps are stored as PNG images whereas one image channel describes evidence for a free and another one describes evidence for occupied cell state.
Mapping of free text gender entries to one of three genders: Male, Female, Non-Binary.
HAM is a dataset for molecular graph partitioning. This dataset contains coarse-grained (CG) mappings of 1206 organic molecules with less than 25 heavy atoms. Each molecule was downloaded from the PubChem database as SMILES. One molecule was assigned to two annotators to compare the human agreement between CG mappings. Downloaded SMILES were hand-mapped. The completed annotations were reviewed by a third person, to identify and remove unreasonable mappings (eg: one bead mappings) which did not agree with the given guidelines. Hence, there are 1.68 annotations per molecule in the current database (16% removed). Source: https://github.com/rochesterxugroup/HAM_dataset
This dataset was developed within an analysis of research data generated and managed within the University of Bologna, with respect to the differences and commonalities between disciplines and potential challenges for institutional data support services and infrastructures. We are primarily mapping the type (e.g., image), content (e.g., scan of a manuscript) and format (e.g., .tiff) of managed data, thus sustaining the value of FAIR data as granular resources. The analysis is based on data management plans (DMPs) produced by grantees of Horizon Europe and Horizon 2020 funding who are affiliated to the University of Bologna and are either project coordinators or partners in charge of the DMP. We are including in the study only the DMPs shared with us between May 2022 (when the data stewards team was created) and October 2023. In short, we have selected variables of interest to be headers of a table that is progressively filled with information garnered through a close reading of the DMPs. Computational analysis (R version 4.2.2) on the collected data produce graphs showing composition, relationship (bar graphs, pie charts and alluvial/sankey charts) and incidences (waterfall graph) of the different variables. Code for computational analysis on this data is "Mapping Reseach Data at the University of Bologna: Code" and it is also deposited on Zenodo (see Related Works).
This dataset accompanies the ICWSM 2022 paper "Mapping Topics in 100,000 Real-Life Moral Dilemmas".
PolyU-BPCoMa: A Dataset and Benchmark Towards Mobile Colorized Mapping Using a Backpack Multisensorial System
The availability of well-curated datasets has driven the success of Machine Learning (ML) models. Despite greater access to earth observation data in agriculture, there is a scarcity of curated and labelled datasets, which limits the potential of its use in training ML models for remote sensing (RS) in agriculture. To this end, we introduce a first-of-its-kind dataset called SICKLE, which constitutes a time-series of multi-resolution imagery from 3 distinct satellites: Landsat-8, Sentinel-1 and Sentinel-2. Our dataset constitutes multi-spectral, thermal and microwave sensors during January 2018 - March 2021 period. We construct each temporal sequence by considering the cropping practices followed by farmers primarily engaged in paddy cultivation in the Cauvery Delta region of Tamil Nadu, India; and annotate the corresponding imagery with key cropping parameters at multiple resolutions (i.e. 3m, 10m and 30m). Our dataset comprises 2, 370 season-wise samples from 388 unique plots, having an average size of 0.38 acres, for classifying 21 crop types across 4 districts in the Delta, which amounts to approximately 209, 000 satellite images. Out of the 2, 370 samples, 351 paddy samples from 145 plots are annotated with multiple crop parameters; such as the variety of paddy, its growing season and productivity in terms of per-acre yields. Ours is also one among the first studies that consider the growing season activities pertinent to crop phenology (spans sowing, transplanting and harvesting dates) as parameters of interest. We benchmark SICKLE on three tasks: crop type, crop phenology (sowing, transplanting, harvesting), and yield prediction.
The repository hosts the STURM-Flood dataset, an open-access resource designed for flood extent mapping using Sentinel-1 and Sentinel-2 satellite imagery. The dataset comprises 21,602 Sentinel-1 tiles and 2,675 Sentinel-2 tiles, each of size 128 × 128 pixels at a resolution of 10 meters, along with corresponding water masks covering 60 flood events globally. This curated dataset is optimized for deep learning applications and provides ground-truth data from the Copernicus Emergency Management Service to facilitate robust model development. We invite researchers and developers to utilize this resource for advancing flood mapping techniques in disaster management. For further details on the methodology, results, and implementation, please refer to our study published in Big Earth Data (2096-4471): https://doi.org/10.1080/20964471.2025.2458714 .
The data is about 1.5 million English tweets annotated for part-of-speech using Ritter's extension of the PTB tagset. The tweets are from 2012 and 2013, tokenized using the GATE tokenizer and tagged jointly using the CMU ARK tagger and Ritter's T-POS tagger. Only when both these taggers' outputs are completely compatible over a whole tweet, is that tweet added to the dataset.
This 2d indoor dataset collection consists of 9 individual datasets. Each dataset contains odometry and (range and bearing) measurement data from 5 robots, as well as accurate groundtruth data for all robot poses and (15) landmark positions. The dataset is intended for studying the problems of cooperative localization (with only a team robots), cooperative localization with a known map, and cooperative simultaneous localization and mapping (SLAM).
This dataset was developed within an analysis of research data generated and managed within the University of Bologna, with respect to the differences and commonalities between disciplines and potential challenges for institutional data support services and infrastructures. We are primarily mapping the type (e.g., image), content (e.g., scan of a manuscript) and format (e.g., .tiff) of managed data, thus sustaining the value of FAIR data as granular resources. The analysis is based on data management plans (DMPs) produced by grantees of Horizon Europe and Horizon 2020 funding who are affiliated to the University of Bologna and are either project coordinators or partners in charge of the DMP. We are including in the study only the DMPs shared with us between May 2022 (when the data stewards team was created) and October 2023. In short, we have selected variables of interest to be headers of a table that is progressively filled with information garnered through a close reading of the DMPs. Computational analysis (R version 4.2.2) on the collected data produce graphs showing composition, relationship (bar graphs, pie charts and alluvial/sankey charts) and incidences (waterfall graph) of the different variables. Code for computational analysis on this data is "Mapping Reseach Data at the University of Bologna: Code" and it is also deposited on Zenodo (see Related Works).