Find research datasets worth reusing

Shared mobility opporTunities And challenges foR European citieS (STARS) - Work Package 2 - Tasks 2.1 - 2.3

These datasets contain data from the Work Package 2 (WP2) in the project STARS. The aims of this WP were:  1) To map existing car sharing services and quantitatively assess their relevance in their respective mobility contexts. 2) To define a classification scheme for such services to ease subsequent analyses. 3) To highlight which are the changes in social practices and media usages that are related to the diffusion of car sharing practices. 4) To assess which are the near and medium term development of such services following the trends and the planned implementation actions that are planned by different stakeholders. 5) To point out the main national and European policy barriers and opportunities for the growth of car-sharing. For more information on the project: <a href="http://stars-h2020.eu/">http://stars-h2020.eu/</a>

opencc-by-nc-nd-4.0May 2019View details →

BACH Dataset : Grand Challenge on Breast Cancer Histology images

i3S Annotated Datasets on Digital Pathology   WELCOME In an effort to contribute and push forward the field of Digital Pathology, <a href="https://www.ipatimup.pt/">Ipatimup</a> and <a href="http://www.ineb.up.pt/">INEB</a>, two major research institutions in Portugal, have joined forces in the construction of histology datasets to support grand Challenges on automatic classification of tissue malignancy. The researchers/pathologists responsible for the datasets are: <a href="mailto:apolonia@ipatimup.pt">António Polónia</a> (MD), Ipatimup/i3S <a href="mailto:celoy@ipatimup.pt">Catarina Eloy</a> (MD, PhD), Ipatimup/i3S <a href="mailto:pauloaguiar@ineb.up.pt">Paulo Aguiar</a> (PhD), INEB/i3S   This specific page refers to the <a href="https://iciar2018-challenge.grand-challenge.org/home/">Grand Challenge on Breast Cancer Histology images</a>, or BACH Challenge   THE BACH CHALLENGE DATASET <a href="https://iciar2018-challenge.grand-challenge.org/home/">ICIAR 2018 - Grand Challenge on Breast Cancer Histology images</a> [Challenge organized by Teresa Araújo, Guilherme Aresta, António Polónia, Catarina Eloy and Paulo Aguiar] For detailed information visit: <a href="https://iciar2018-challenge.grand-challenge.org/home/">https://iciar2018-challenge.grand-challenge.org/home/</a>   THIS DATASET IS PUBLICALLY AVAILABLE UNDER A CREATIVE COMMONS CC BY-NC-ND LICENSE (<a href="https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode">ATTRIBUTION-NONCOMMERCIAL-NODERIVS</a>) ESSENCIALLY, YOU ARE GRANTED ACCESS TO THE DATASET FOR USE IN YOUR RESEARCH AS LONG AS YOU CREDIT OUR WORK/PUBLICATIONS(*), BUT YOU CANNOT CHANGE THEM IN ANY WAY OR USE THEM COMMERCIALLY <ul> <li>(*) Aresta, Guilherme, et al. "BACH: Grand challenge on breast cancer histology images." Medical image analysis (2019).</li> <li>(*) Araújo, Teresa, et al. "Classification of breast cancer histology images using convolutional neural networks." PloS one 12.6 (2017): e0177544.</li> <li>(*) Fondón, Irene, et al. "Automatic classification of tissue malignancy for breast carcinoma diagnosis." Computers in biology and medicine 96 (2018): 41-51.</li> </ul>

Discrimination of biological scatterers in polarimetric weather radar data: opportunities and challenges

The associated dataset represents the base training/test data used for analyses by S. Gauthreax and R. Diehl in “Discrimination of biological scatterers in polarimetric weather radar data: opportunities and challenges.”

Discussion Survey from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes

These files include summaries of pre-meeting survey of  important topics to discuss at the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes The Chapman Conference was supported by NASA Grants: 936723.02.01.09.14 and 936723.02.01.11.21 and by NSF Award AGS 1848885

opencc-by-4.0Dec 2018View details →

Shared mobility opporTunities And challenges foR European citieS (STARS) - Work Package 4

These datasets contain data from the Work Package 4 (WP4) in the project STARS. For more information about the project: <a href="http://stars-h2020.eu/">http://stars-h2020.eu/</a> The aims of the WP 4 were:  To study the influence of factors related to individuals and to social settings that can be quantitatively measured; To uncover the role played by different mobility styles and mobility cultures in the diffusion of shared mobility practices; To assess the overall importance of individual, social, political, environmental and economic variables in driving the behavioural change towards shared mobility; To identify barriers and facilitators for a faster shift in life-styles that includes shared mobility.

Questions for Presenters at the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes

This file lists the questions asked of  Chapman Conference presenters by the Chapman Conference attendees. -Questions, along with presenter names and presentation titles are included -The document contains        1) an explanation sheet          2) a sheet with all questions sorted by day and presenter         3) a sheet with questions that were asked in a general setting. The materials have been lightly edited to spell out acronyms, correct spelling and clarify non specific references when possible. The Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes was supported by NASA Grants 936723.02.01.09.14 and 936723.02.01.11.21 and by  NSF Award AGS 1848885

Dataset related to: "Interdisciplinary challenges for elucidating human olfactory attractiveness"

Datasets (2 excel files) 1. OdorQualitativeDescriptors.xlsx This file contains a “Raw data sheet” with participants raw responses for each odour sample (choice of the descriptive terms best describing the odour quality). Two more sheets “Processed data” are provided for axilla and head odours separately. They contain processed data used to conduct statistical tests and create Figures 1a, Figure 2 and Table S1. 2. OdorRatings.xlsx This file contains a “Raw data sheet” with participants raw responses for each odour sample (attractiveness and intensity ratings from 0 to 100). A sheet with "Processed data" is also provided, with the data used to conduct statistical tests and to create Figure 1b and Figure 1c. NB: In each excel file, a sheet entitled “Readme” provides detailed explanations about the content of the other sheets.

opennotspecifiedJan 2020View details →

FIG. 1 in A new subgenus for "Acalathus" advena (LeConte, 1846) and the challenge of defining Calathina based on morphological characters (Coleoptera, Carabidae Sphodrini)

FIG. 1. Calathus (Certocalathus) advena (LeConte, 1848), habitus, dorsal view; male specimen from Alaska: Afognak Is.

opencc-by-nc-sa-4.0Feb 2020View details →

DCASE 2020 Challenge Task 2 Development Dataset

Description This dataset is the "development dataset" for the DCASE 2020 Challenge Task 2 "Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring" <a href="http://dcase.community/challenge2020/task-unsupervised-detection-of-anomalous-sounds">[task description]</a>.  The data comprises parts of ToyADMOS and the MIMII Dataset consisting of the normal/anomalous operating sounds of six types of toy/real machines. Each recording is a single-channel (proximately) 10-sec length audio that includes both a target machine's operating sound and environmental noise. The following six types of toy/real machines are used in this task: <ul> <li>Toy-car (ToyADMOS)</li> <li>Toy-conveyor (ToyADMOS)</li> <li>Valve (MIMII Dataset)</li> <li>Pump (MIMII Dataset)</li> <li>Fan (MIMII Dataset)</li> <li>Slide rail (MIMII Dataset)</li> </ul>   Recording procedure The ToyADMOS consists of normal/anomalous operating sounds of miniature machines (toys) collected with four microphones, and the MIMII dataset consists of those of real-machines collected with eight microphones. Anomalous sounds in these datasets were collected by deliberately damaging target machines. For simplifying the task, we used only the first channel of multi-channel recordings; all recordings are regarded as single-channel recordings of a fixed microphone. The sampling rate of all signals has been downsampled to 16 kHz. From ToyADMOS, we used only IND-type data that contain the operating sounds of the entire operation (i.e., from start to stop) in a recording. We mixed a target machine sound with environmental noise, and only noisy recordings are provided as training/test data. For the details of the recording procedure, please refer to the papers of <a href="https://ieeexplore.ieee.org/document/8937164">ToyADMOS</a> and <a href="http://dcase.community/documents/workshop2019/proceedings/DCASE2019Workshop_Purohit_21.pdf">MIMII Dataset</a>.   Data We first define two important terms in this task: Machine Type and Machine ID. Machine Type means the kind of machine, which in this task can be one of six: toy-car, toy-conveyor, valve, pump, fan, and slide rail. Machine ID is the identifier of each individual of the same type of machine, which in the training dataset can be of three or four. Each machine ID's dataset consists of (i) around 1,000 samples of normal sounds for training and (ii) 100-200 samples each of normal and anomalous sounds for the test. The given labels for each training/test sample are Machine Type, Machine ID, and condition (normal/anomaly). Machine Type information is given by directory name, and Machine ID and condition information are given by their respective file names.    Directory structure When you unzip the downloaded files from  Zenodo, you can see the following directory structure. As described in the previous section, Machine Type information is given by directory name, and Machine ID and condition information are given by file name, as: <ul> </ul> /dev_data <ul> <li>/ToyCar <ul> <li>/train (Only normal data for all Machine IDs are included.) <ul> <li>/normal_id_01_00000000.wav</li> <li>...</li> <li>/normal_id_01_00000999.wav</li> <li>/normal_id_02_00000000.wav</li> <li>...</li> <li>/normal_id_04_00000999.wav</li> </ul> </li> <li>/test (Normal and anomaly data for all Machine IDs are included.) <ul> <li>/normal_id_01_00000000.wav</li> <li>...</li> <li>/normal_id_01_00000349.wav</li> <li>/anomaly_id_01_00000000.wav</li> <li>...</li> <li>/anomaly_id_01_00000263.wav</li> <li>/normal_id_02_00000000.wav</li> <li>...</li> <li>/anomaly_id_04_00000264.wav</li> </ul> </li> </ul> </li> <li>/ToyConveyor (The other Machine Types have the same directory structure as ToyCar.)</li> <li>/fan</li> <li>/pump</li> <li>/slider</li> <li>/valve</li> </ul>   The paths of audio files are: <ul> <li>"/dev_data/<Machine_Type>/train/normal_id_<Machine_ID>_[0-9]+.wav"</li> <li>"/dev_data/<Machine_Type>/test/normal_id_<Machine_ID>_[0-9]+.wav"</li> <li>"/dev_data/<Machine_Type>/test/anomaly_id_<Machine_ID>_[0-9]+.wav"</li> </ul> For example, the Machine Type and Machine ID of "/ToyCar/train/normal_id_01_00000000.wav" are "ToyCar" and "01", respectively, and its condition is normal. The Machine Type and Machine ID of "/fan/test/anomaly_id_00_00000000.wav" are "fan" and "00", respectively, and its condition is anomalous.   Baseline system A simple baseline system is available on the Github repository <a href="https://github.com/y-kawagu/dcase2020_task2_baseline">[URL]</a>. The baseline system provides a simple entry-level approach that gives a reasonable performance in the dataset of Task 2. It is a good starting point, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task.   Conditions of use This dataset was created jointly by NTT Corporation and Hitachi, Ltd. and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.   Publication If you use this dataset, please cite all the following three papers: Yuma Koizumi, Shoichiro Saito, Noboru Harada, Hisashi Uematsu, and Keisuke Imoto, "ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection," in Proc of Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2019. <a href="https://ieeexplore.ieee.org/document/8937164">[pdf]</a> Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” in Proc. 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2019. <a href="http://dcase.community/documents/workshop2019/proceedings/DCASE2019Workshop_Purohit_21.pdf">[pdf]</a> Yuma Koizumi, Yohei Kawaguchi, Keisuke Imoto, Toshiki Nakamura, Yuki Nikaido, Ryo Tanabe, Harsh Purohit, Kaori Suefusa, Takashi Endo, Masahiro Yasuda, and Noboru Harada, "Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring," in Proc. 5th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2020. <a href="https://dcase.community/documents/workshop2020/proceedings/DCASE2020Workshop_Koizumi_3.pdf">[pdf]</a> Feedback If there is any problem, please contact us: <ul> <li>Yuma Koizumi, <a href="mailto:koizumi.yuma@ieee.org">koizumi.yuma@ieee.org</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> <li>Keisuke Imoto, <a href="mailto:keisuke.imoto@ieee.org">keisuke.imoto@ieee.org</a></li> </ul>

Priorities Survey from the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes

Mid-meeting survey results on Priorities from the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes 2 Files are provided:  An Excel Spreadsheet and a summary pdf of highest priorities. Statistics compiled by Tomoko Matsuo The Chapman Conference was supported by NSF Award AGS 1848885 and NASA grants  936723.02.01.09.14 and  936723.02.01.11.21

Discussion Notes from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes

Discussion notes from the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes Notes from Days 1, 3 and 4 are provided.  Conference Day 2 was a 'Poster Day' These notes were aggregated from meeting scribes and conveners The Chapman Conference was supported by NSF Award AGS 1848885 and NASA grants  936723.02.01.09.14 and  936723.02.01.11.21

Table of Contents for Meeting Artifacts from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes

Table of Contents of Meeting Artifacts and Output from the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes, 11-15 February 2019, Pasadena, CA, USA Each entry in the table of contents provides a short description and/or artifact title, along with the number of associated files and a weblink showing the associated DOI or permanent URL. The Chapman Conference was supported by NSF Award AGS 1848885 and NASA grants  936723.02.01.09.14 and  936723.02.01.11.21

Full list of signatories to: Pe'er et al. "Action needed for the EU Common Agricultural Policy to address sustainability challenges" (Preprint version)

This is the list of signatories to the Scientists' Statement on the EU's Common Agricultural Policy: "Action needed for the EU Common Agricultural Policy to address sustainability challenges" (Pe'er et al. 2019). The preprint version (DOI: 10.5281/zenodo.3666258) has been made available online between 4.11.2019 and 19.2.2020. Signatories were requested to read the full statement prior to adding their name in its support; and to provide a proof that they are scientists. A final version of the paper has been published in March 2020 in the journal People and Nature.

Shared mobility opporTunities And challenges foR European citieS (STARS) - Work Package 5, Task 5.1

These datasets contain information about car sharing members and non-members collected through a survey carried out as part of the Work Package 5 (WP5) activities in the STARS project. The data were gathered in three European countries (Italy, Germany and Belgium) and contain information about travel habits, car ownership level and sociodemographic characteristics of the respondents. The data collected were used to: 1) Define the maximum portion of travel demand that can be served by car sharing and how this will impact the demand for other travel means. 2) Define a “rupture scenario”, where the benefits of car sharing are maximised. 3) Quantify the gap between the business as usual scenario and the rupture scenario, and the related impacts. For more information about the project, please refer to <a href="http://stars-h2020.eu/">http://stars-h2020.eu/</a>

opencc-zeroJun 2020View details →

Supplementary material 3 from: Petersen M, Pramann B, Toepfer R, Neumann J, Enke H, Hoffmann J, Mauer R (2020) Research Data Management - Current status and future challenges for German non-university research institutions. Research Ideas and Outcomes 6: e55141. https://doi.org/10.3897/rio.6.e55141

Praxisbericht: Entwicklung eines Maßnahmenkatalogs zur Verbesserung des Forschungsdatenmanagements am Herder-Institut für historische Ostmitteleuropaforschung

opencc-by-4.0Oct 2019View details →

CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition

As one of the research directions at <a href="https://ghassanalregib.info/">OLIVES Lab @ Georgia Tech</a>, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (>2M images) traffic sign recognition dataset (<a href="https://github.com/olivesgatech/CURE-TSR">CURE-TSR</a>) which is among the most comprehensive datasets with controlled synthetic challenging conditions. Traffic sign images in the <a href="https://github.com/olivesgatech/CURE-TSR">CURE-TSR</a> dataset were cropped from the <a href="https://github.com/olivesgatech/CURE-TSD">CURE-TSD</a> dataset, which includes around 1.7 million real-world and simulator images with more than 2 million traffic sign instances. Real-world images were obtained from the BelgiumTS video sequences and simulated images were generated with the Unreal Engine 4 game development tool.  Sign types include speed limit, goods vehicles, no overtaking, no stopping, no parking, stop, bicycle, hump, no left, no right, priority to, no entry, yield, and parking. Unreal and real sequences were processed with state-of-the-art visual effect software Adobe(c) After Effects to simulate challenging conditions, which include rain, snow, haze, shadow, darkness, brightness, blurriness, dirtiness, colorlessness, sensor and codec errors. Please refer to our <a href="https://github.com/olivesgatech/CURE-TSR">GitHub page</a> for code, papers, and more information. Instructions:  The name format of the provided images are as follows: "sequenceType_signType_challengeType_challengeLevel_Index.bmp" <ul> <li> sequenceType: 01 - Real data 02 - Unreal data </li> <li> signType: 01 - speed_limit 02 - goods_vehicles 03 - no_overtaking 04 - no_stopping 05 - no_parking 06 - stop 07 - bicycle 08 - hump 09 - no_left 10 - no_right 11 - priority_to 12 - no_entry 13 - yield 14 - parking </li> <li> challengeType: 00 - No challenge 01 - Decolorization 02 - Lens blur 03 - Codec error 04 - Darkening 05 - Dirty lens 06 - Exposure 07 - Gaussian blur 08 - Noise 09 - Rain 10 - Shadow 11 - Snow 12 - Haze </li> <li> challengeLevel: A number in between [01-05] where 01 is the least severe and 05 is the most severe challenge. </li> <li> Index: A number shows different instances of traffic signs in the same conditions. </li> </ul>

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Extreme mobility of the world's largest flying mammals creates key challenges for management and conservation

Background: Effective conservation management of highly mobile species depends upon detailed knowledge of movements of individuals across their range; yet, data are rarely available at appropriate spatiotemporal scales. Flying-foxes (Pteropus spp.), are large bats that forage by night on floral resources and rest by day in arboreal roosts that may contain colonies of many thousands of individuals. They are the largest mammals capable of powered flight, and they are highly mobile, which makes them key seed and pollen dispersers in forest ecosystems. However, their mobility also facilitates transmission of zoonotic diseases and brings them in conflict with humans, and so require a precarious balancing of conservation and management concerns throughout their Old World range. Here we analyse the Australia-wide movements of 201 satellite-tracked individuals, providing unprecedented detail on the inter-roost movements of three flying-fox species: Pteropus alecto, P. poliocephalus, and P. scapulatus across jurisdictions over up to five years. Results: Individuals were estimated to travel long distances among a network of 755 roosts, with P. alecto reaching more than 1,800 kilometers a year, P. poliocephalus up to 2,500 kilometers a year and P. scapulatus up to 6,000 kilometers a year, but with little uniformity among their directions of travel. This indicates that flying-fox populations are composed of extremely mobile individuals that move nomadically and at species-specific rates. Individuals of all three species exhibited very low fidelity to roosts, resulting in very high estimated colony turnover rates. This indicates that flying-fox roosts form nodes in a vast continental network of 'staging posts' through which highly mobile individuals travel far and wide across their species range. Conclusions: The extreme inter-roost mobility reported here demonstrates the extent of the ecological linkages that nomadic flying-foxes provide across Australia's contemporary fragmented landscape, with profound implications for the ecosystem services and zoonotic dynamics of flying-fox populations. In addition, the extreme mobility of the species means that impacts from local management actions can readily reverberate across jurisdictions throughout the species ranges; therefore, local management actions need to be assessed with reference to actions elsewhere and hence require national coordination. These findings underscore the need for sound understanding of animal movement dynamics to support evidence-based, transboundary conservation and management policy, tailored to the unique movement ecologies of species.

opencc-zeroDec 2019View details →

dryad32/100

Data from: Taxonomic delimitation of species complexes: a challenge for conservation, first steps with Abarema cochliacarpos complex

Taxonomic and ecological knowledge of plant species complexes informs their conservation status. Poorly delimited species boundaries can lead to incorrect assessments of biodiversity, and a rare species might have its distribution range overestimated if considering other closely related taxa as the same one. The present study investigates the level of morphometric variation within the Abarema cochliacarpos complex, throughout its entire distribution range in Brazilian Atlantic Forest and Caatinga vegetation, and considers the drivers of this variation. A morphometric study using cluster, PERMANOVA, and NMDS analyses was conducted. We analyzed patterns of trait variation among and within populations, and their relationship with environmental factors along with their geographical gradient. From the four morphotypes previous cited for the complex, the analyses delimited three morphological groups. Their morphological characteristics differed in response to environmental drivers. We identified morphological characters that differentiated the groups within the complex. The development of conservation strategies and actions to fully protect a species complex requires the understanding of the morphological variation within the complex. In the Abarema cochliacarpos complex different threats and challenges are associated with the conservation of each morphotype. We highlight the importance of conserving the various morphotypes of a species complex which has a large distribution range as evolutionary significant units, something that is frequently neglected in the published literature.

opencc-zeroAug 2020View details →

opencc-by-4.0Sep 2020View details →

PANDA challenge trained models

This dataset includes pre-trained efficientNet-B0 network as well as models manually trained using aforementioned backbone.

ISMRM Reproducible Research Study Group: Data for the paper "CG-SENSE revisited: Results from the first ISMRM reproducibility challenge"

Challange data (brain/heart) and supplementary data (cardiac/rawdata_sprial) for the paper  "CG-SENSE revisited: Results from the first ISMRM reproducibility challenge".