Find research datasets worth reusing

opencc-by-4.0Jan 2024View details →

The Second DISPLACE 2024 Challenge

Inspired by the previous session of DISPLACE 2023 challenge, we have  launched the DISPLACE 2024 challenge  (https://displace2024.github.io/). Compared to the first DISPLACE challenge, the current challenge  includes an additional track on automatic speech recognition (ASR) in code-switched multi-accent  conversational scenarios along with speaker and language diarization tracks. We plan to release  both supervised and unsupervised domain matched data for participants to use in model adaptation. To the best of our knowledge, no publicly available dataset matches the diverse characteristics observed in the DISPLACE dataset, including code-mixing/switching, natural overlaps, reverberation, and noise. For this challenge, a natural multi-lingual, multi-speaker conversational dataset will be distributed for development and evaluation purposes. There will be no training data given and the participants will be free to use any resource for training the models. The challenge reflects the theme of Interspeech 2024 - "Speech and Beyond" in its true sense. The dataset can be obtained by orgainisers by sending the request form and duly signed the terms and condtions Link for registration for obtaining the data : <a href="https://forms.gle/meni6SNRHtsz5XQi6" target="_blank" rel="noopener">Registration Link</a>

opencc-by-4.0Mar 2024View details →

IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain Shift Evaluation Dataset

The Chinese Acoustic Scene (CAS) 2023 dataset is a large-scale dataset that serves as a foundation for research related to environmental acoustic scenes. The dataset includes 10 common acoustic scenes, with a total duration of over 130 hours. Each audio clip is 10 seconds long with metadata about the recording location and timestamp. The dataset was collected by members of the Joint Laboratory of Environmental Sound Sensing at the School of Marine Science and Technology, Northwestern Polytechnical University. The data collection period spanned from April 2023 to September 2023, covering 22 different cities across China. The CAS 2023 dataset was collected using the XS-SN-2BE1 manufactured by Xi'an Lianfeng Acoustic Technologies Co., Ltd (https://www.lfxstek.com/).   The ICME 2024 Semi-supervised Acoustic Scene Classification under Domain Shift challenge (https://2024.ieeeicme.org/grand-challenge-proposals/, https://ascchallenge.xshengyun.com/) dataset consists of development (https://zenodo.org/records/10616533) and evaluation datasets, all derived from the CAS 2023 dataset. The evaluation dataset includes 1,100 recordings, where data are selected from 12 cities, with 5 unseen cities specifically chosen to provide a more comprehensive evaluation of submissions under domain shift. Baseline: https://github.com/JishengBai/ICME2024ASC Acoustic scenes (10): Bus, Airport, Metro, Restaurant, Shopping mall, Public square, Urban park, Traffic street, Construction site, Bar

opencc-by-4.0Dec 2014View details →

Fig. 2 in The Challenges of Incorporating Realistic Simulations of Marine Protists in Biogeochemically Based Mathematical Models

Fig. 2. The mechanistic phytoplankton model of Flynn (2001) that represents multi nutrient uptake and utilisation of N – nitrate; A – ammonium; F – bioavailable iron; P – phosphate; S – silicate; and the interaction with light (PFD). Major flows in and out of state variables (boxes) are depicted by solid arrows, with the major feedback processes depicted by dashed arrows. C – carbon biomass; Cell – cell density; NC – N C-quota; ChlC – chlorophyll C-quota, FC – iron C-quota; IPC – inorganic P C-quota, OPC – organic P C-quota; Scell – silicon cell-quota (reproduced with permission).

opencc-by-4.0Mar 2024View details →

Image repository for "Towards advancing Translators' Guidance for Organisations Tackling Innovation Challenges in Manufacturing within an Industry 5.0 context"

The files on this trusted repository  are provided by the authors of the manuscript with the title “Towards advancing Translators’ Guidance for Organisations Tackling Innovation Challenges in Manufacturing within an Industry 5.0 context” that was received by the MDPI journal Sustainability (ISSN 2071-1050) on 29 January 2024, got the manuscript ID sustainability-2872279, and is intended to become part of the special issue “Sustainable Materials, Manufacturing and Design” accessible under the link <a href="https://www.mdpi.com/journal/sustainability/special_issues/Sus_materials_manufacturing_design">https://www.mdpi.com/journal/sustainability/special_issues/Sus_materials_manufacturing_design</a>. The authors Paul-Ludwig Michael Noeske, Alexandra Simperler, Welchy Leite Cavalcanti, Vinicius Carrillo Beber, Brendon Weager, Tasmin Alliott, Peter Schiffels, and Gerhard Goldbeck aim at facilitating common access to the files representing high-resolution microscopy images (corresponding to the light microscopy (LM) and scanning electron microscopy (SEM) images shown in Figure 9 and Figure 12 in the manuscript or complementing them) given in .jpg and .tif format, respectively. Moreover, this repository comprises a .csv file containing the data points underlying the values presented in Table A1 of this manuscript and their description. The authors indicate here that following the sixth step of the translation process in materials modelling the translator may provide these data in this presentation that is adapted to the process-centric perspective required by representatives of an enterprise manufacturing prepregs and to their background knowledge disclosed to the translator beforehand.“

opencc-zeroMay 2023View details →

3D printed biomedical devices and their applications: A review on state-of-the-art technologies, existing challenges, and future perspectives

This repository consists of the data that have been utilized to imagine and subsequently construct Fig. 1, Fig. 3 , Fig. 5 , Fig. 6 and Fig. 7 of the article " H. B. Mamo, M. Adamiak and A. Kunwar. 3D printed biomedical devices and their applications: A review on state-of-the-art technologies, existing challenges, and future perspectives, Journal of the Mechanical Behavior of Biomedical Materials, 143 (2023) 105930. doi: 10.1016/j.jmbbm.2023.105930 ".  Brief introduction of the files contained in this repository <ol> <li> acronyms.csv: This file consists of  the list of acronyms associated with materials and techniques used in 3D printing of biomedical devices.</li> <li>fig1a-data.csv: The csv file provides a relative ranking of different types of 3D printing techniques for biomedical applications based upon  merits and limitations (wherever applicable). The technique listed as Rank 1 is considered as the most commonly used 3D printing procedure.</li> <li> fig1b-data.csv: The csv file enlists the benfits of the 3D printing techniques in biomedical applications as compared to subtractive manufacturing methods.</li> <li> fig3-data.csv: The file contains a comparison between conventional and customized tablet printing  methods. The illustration is made through the manufacturing or printing of pharmaceutical tablets or pills.  This illustation also applies to the production of pharmaceutical capsules.  It thus illustrates that customized medicine is enabled using 3D printing technology.</li> <li> fig5-data.csv: This file enumerates the major challenges associated with 3D printing of biomedical devices.</li> <li> fig6-data.csv: The file enlists the roles of wearable smarts, cloud-based platforms and physicians in context of the hospitals implementing IoMT.</li> <li> fig7-data.csv: The aspects of IoMT Sensors,IoMT Platforms,3D Printers,Design and Prototypes within the integrated 3D printing-IoMT ecosystem are listed in the file.</li> </ol>

AI4Life-MDC24 Challenge data: SUPPORT method dataset

This is a subset of the Supporting data for Eom, M., Han, S., Park, P. et al. Statistically unbiased prediction enables accurate denoising of voltage imaging data. Nat Methods 20, 1581–1592 (2023). https://doi.org/10.1038/s41592-023-02005-8 For more details, please see the research publication "<a href="https://www.nature.com/articles/s41592-023-02005-8">Statistically unbiased prediction enables accurate denoising of voltage imaging data</a>". The selected subset contains low-SNR images from the Penicillium dataset in the form of a single tiff file. The original dataset is available at <a href="../record/8176722">https://zenodo.org/record/8176722</a> AI4Life has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101057970. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

AI4Life-MDC24 Challenge data: W2S Dataset

This is a subset of the W2S dataset: Zhou, R., El Helou, M., Sage, D., Laroche, T., Seitz, A., Süsstrunk, S. (2020). W2S: Microscopy Data with Joint Denoising and Super-Resolution for Widefield to SIM Mapping. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12535. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-66415-2_31">https://doi.org/10.1007/978-3-030-66415-2_31</a> The selected subset contains 120 images with three channels, acquired using a conventional fluorescence widefield, in the form of a single multi-channel tiff file. Code, data, and a copy of the original paper is available at  <a href="https://github.com/IVRL/w2s" target="_blank" rel="noopener noreferrer">https://github.com/IVRL/w2s</a> AI4Life has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101057970. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

AI4Life-MDC24 Challenge data: JUMP Cell Painting Datasets

This is a subset of the cpg0016-jump dataset (Chandrasekaran et al., 2022), available from the Cell Painting Gallery on the Registry of Open Data on AWS (<a href="https://registry.opendata.aws/cellpainting-gallery/" rel="nofollow">https://registry.opendata.aws/cellpainting-gallery/</a>).  The selected subset contains 517 images with four channels in the form of a single multi-channel tiff file. Gaussian noise is applied to each image to simulate the detector noise. The GitHub repository with the details about the original dataset is available at <a href="https://github.com/jump-cellpainting/datasets">https://github.com/jump-cellpainting/datasets</a>. The preprint describing the original dataset is <a href="https://www.biorxiv.org/content/10.1101/2023.03.23.534023v1">available on BioRxiv</a> AI4Life has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101057970. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

AI4Life-MDC24 Challenge data: Fluorescence Microscopy Datasets for Training Deep Neural Networks

This is a subset of the Supporting data for Guy M Hagen, Justin Bendesky, Rosa Machado, Tram-Anh Nguyen, Tanmay Kumar, Jonathan Ventura, Fluorescence microscopy datasets for training deep neural networks, GigaScience, Volume 10, Issue 5, May 2021, giab032, <a href="https://doi.org/10.1093/gigascience/giab032">https://doi.org/10.1093/gigascience/giab032</a> The selected subset contains 79 images from Data Set 4 in the form of a single tiff file.  The paper describing the original dataset is available here: <a href="https://academic.oup.com/gigascience/article/10/5/giab032/6269106">https://academic.oup.com/gigascience/article/10/5/giab032/6269106</a> The original dataset is available here: <a href="http://gigadb.org/dataset/100888">http://gigadb.org/dataset/100888</a> AI4Life has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101057970. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

Night Photography Rendering Challenge 2024 Dataset

The procedure of capturing and processing images taken by cameras involves employing onboard processing to transform raw sensor images into the final, polished photographs, subsequently encoding them in a standard color space like sRGB. However, capturing images at night presents distinct challenges not typically encountered in daylight shots. Unlike daytime images, where assuming a single global illumination is often adequate, night images frequently feature multiple illuminants, many of which are visible in the scene. This complexity makes it difficult to determine the optimal illumination correction for rendering night images. Additionally, commonly used tone curves and photo-finishing techniques for daytime images may not be suitable for night photography. Furthermore, widely employed image metrics like SSIM and LPIPS may not effectively evaluate night images. The absence of established best practices and limited research in the realm of night photography pose significant challenges. The main objective of addressing this challenge is to stimulate research and advance the field of image processing specifically tailored for night photography. The participants in this challenge will be granted access to raw-RGB images of night scenes, which have been captured using the Huawei Mate 40 Pro sensor type and are encoded in 16-bit PNG files. Accompanying these images will be additional meta-data, provided in JSON files.  As an added resource, the organizers have also provided code for a baseline algorithm, as well as a demonstration, on <a href="https://github.com/createcolor/nightimaging24">GitHub</a>. Dataset includes data postprocessed by baseline algorithm and resulting images processed by competitors` algorithms. Mean opinion scores are obtained through visual comparison, carried out using Yandex Tasks (similar to Mechanical Turk). Yandex Tasks users ranked the solutions in a forced-choice manner. It is important to note that Yandex Tasks primarily relies on observers from Eastern Europe and and Central Asia to perform the image ranking, and as a result, there may be a cultural bias in terms of the preferred image aesthetics by the observers. Yandex Tasks users were not aware of the identity of the participants. An example of this evaluation process can be found at a <a href="https://nightimaging.org/challenges/2022/ranking/index.html">specified link</a>.

AIWare 2024 Challenge Datasets

This dataset includes the program schedules of MSR 2022 and MSR 2023. Each schedule is stored in a json file including the title and abstract of each presented paper, their presentation location, time, and session name. This dataset is used in the AIWare 2024 Challenge https://2024.aiwareconf.org/track/aiware-2024-challenge-track

Fig. 7 in Certesella larai (Amoebozoa: Arcellinida: Hyalospheniformes) a new soil testate amoeba species from the Dominican Republic and Chile challenges the definition of genera Certesella and Porosia

Fig. 7. Maximum range of shell length and width of the three known Certesella and Porosia species showing a lack of overlap between C. larai n.sp. and all other species from the two genera in this two-dimensional space. Each species is illustrated (images not exactly to scale).

Fig. 6 in Certesella larai (Amoebozoa: Arcellinida: Hyalospheniformes) a new soil testate amoeba species from the Dominican Republic and Chile challenges the definition of genera Certesella and Porosia

Fig. 6. Comparative overview of the morphology of all known species of genera Certesella and Porosia species. A: Certesella larai n. sp., B: C. certesi, C: C. australis, D: C. murrayi, E: C. martiali, F: Porosia paracarinata, G: P. bigibbbosa. Images are not to scale as some early images lacked a scale.

Fig. 5 in Certesella larai (Amoebozoa: Arcellinida: Hyalospheniformes) a new soil testate amoeba species from the Dominican Republic and Chile challenges the definition of genera Certesella and Porosia

Fig. 5. Biplot of length vs. width of two populations of Certesella larai n.sp. from Dominican Republic and Chile.

Fig. 3 in Certesella larai (Amoebozoa: Arcellinida: Hyalospheniformes) a new soil testate amoeba species from the Dominican Republic and Chile challenges the definition of genera Certesella and Porosia

Fig. 3. Light microscopy images of Certesella larai n.sp. from Parque Nacional Alerce Costero, Los Ríos Region, Chile, showing the detail of the pseudostome. The right image shows internal teeth fully visible on the flank of the neck (solid arrow and visible only in transparency (empty arrow). Such structures are only visible in ca. 10% of the specimens. Scale bar 10 µm.

Fig. 1 in Certesella larai (Amoebozoa: Arcellinida: Hyalospheniformes) a new soil testate amoeba species from the Dominican Republic and Chile challenges the definition of genera Certesella and Porosia

Fig. 1. General shape of Certesella larai n.sp. and indication of the morphometrical measurements. 1 – shell length, 2 – shell breadth, 3 – shell length / breadth ratio (not illustrated), 4 – aperture (long axis), 5 – distance from fundus to the pores, 6 – distance from fundus to base of neck, 7 – distance between the pores, 8 – width of the neck at narrowest point, 9 – pore length, 10 – pore width.

Fig. 2 in Certesella larai (Amoebozoa: Arcellinida: Hyalospheniformes) a new soil testate amoeba species from the Dominican Republic and Chile challenges the definition of genera Certesella and Porosia

Fig. 2. Light microscopy images of Certesella larai n.sp. from Parque Nacional Alerce Costero, Los Ríos Region, Chile. Left: DIC image of the type specimen deposited at the Natural History Museum of Neuchâtel (slide 95–2). Centre and right: brightfield images of specimens from the same sample. Scale bar 20 µm.

opencc-by-4.0Nov 2024View details →

SemTab 2024: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching Data Sets - WikidataTables2024R1 and WikidataTables2024R2

Data Sets from the ISWC 2024 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, Round 1, Wikidata Tables. Links to other datasets can be found on the challenge website: https://sem-tab-challenge.github.io/2024/ as well as the proceedings of the challenge published on CEUR. For details about the challenge, see: http://www.cs.ox.ac.uk/isg/challenges/sem-tab/ For 2024 edition, see: https://sem-tab-challenge.github.io/2024/ Note on License: This data includes data from the following sources. Refer to each source for license details: - Wikidata https://www.wikidata.org/ THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

opencc-by-4.0Jul 2024View details →

Dataset for Diamond-coated quartz crystal microbalance sensors: Challenges in high yield production and enhanced detection of ethanol and sars-cov-2 proteins

The data set to paper:  Name:             Diamond-coated quartz crystal microbalance challenges in mass production and enhanced detection of ethanol and sars-cov-2 proteins Authors:        Tibor Izsák1*, Marian Varga1, Michal Kočí2,3, Ondrej Szabó2, Katarína Aubrechtová Dragounová2, Gabriel Vanko2, Miroslav Gál4, Jana Korčeková5, Michaela Hornychová 4, Alexandra Poturnayová5, Alexander Kromka2* Affiliations:        1 Department of Microelectronics and Sensors, Institute of Electrical Engineering, Slovak Academy of Sciences, Dúbravská Cesta 9, Bratislava, 841 04, Slovak Republic             2 Department of Semiconductors, Institute of Physics of the Czech Academy of Sciences, Cukrovarnicka 10/112, Prague 6 162 00, Czech Republic             3 Department of Microelectronics, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, Prague 6, 166 27, Czech Republic             4 Faculty of Chemical and Food Technology, Slovak University of Technology, Bratislava, Slovak Republic             5 Center of Biosciences, Institute of Molecular Physiology and Genetics, Slovak Academy of Sciences, Bratislava, Slovak Republic             *corresponding author: tibor.izsak@savba.sk Data manager:         Kristýna Dostálová: dostalovak@fzu.cz Date of collection:    1. 5. 2023 - 31. 7. 2024 Description:        Figure 1: Photos of QCM substrates oriented horizontally or vertically on the substrate holder in the deposition chamber (left) and during the diamond CVD process with ignited plasma (right).             Figure 2: a) 3D model of the measurement setup and b) photograph of the open gas chamber with embedded QCM sample.             Figure 3: Photo of the a) measurement setup and b) disassembled flow cell with V-Dia-QCM. c) Side view photo of the assembled flow cell in the measurement setup.             Figure 4: a) SEM images revealing surface morphology and b) corresponding Raman spectra of Dia-QCM and Dia-Si substrates horizontally or vertically oriented on the substrate holder and corresponding optical photos. There is also the Raman spectrum of the bare QCM (Au-QCM) sample before the diamond deposition.             Figure 5: a) Raman spectra and b) SEM images depicting surface morphology of porous diamond film grown on Si (H-PorDia-Si) and QCM (H-PorDia-QCM) substrate. The inset in Fig. 5a represents the optical photo of diamond-coated QCM. Note: ‘H-’ in sample names means horizontally loaded samples.             Figure 6: The response delta fR of diamond-coated QCM sensors horizontally and vertically oriented, i.e., single-sided and double-sided diamond-coated QCMs, when applying periodic switching (at 3-minute intervals) of ethanol vapour (E) with various concentrations (from 10 ppm to 100 ppm) and synthetic air (Air).             Figure 7: a) First resonant frequency shift (delta fR) of individual QCM sensors and b) mean values of delta fR with corresponding error bars for each QCM sensor group dependent on ethanol concentration.             Figure 8: a) The changes of the resonant frequency, delta fR, after the addition of neutravidin (NA) dissolved in water, biotinylated 1C aptamers (1C APT) dissolved in PBS with MgCl2, and 50 pg/mL S-RBD protein in PBS. The addition of neutravidin, aptamers, proteins, and surface washings by water (H2O) or buffer (PBS) are highlighted by arrows. b) Zoom in on the highlighted area in Fig. 8a.             Figure 9: Decrease of the resonant frequency, fR, at various S-RBD protein concentrations. The comparison of the sensitivity of diamond and gold QCM surfaces on which S-RBD was determined is indicated in the graph legend.