Find research datasets worth reusing

opencc-by-4.0Oct 2020View details →

Data for: On-the-fly ab initio semiclassical evaluation of third-order response functions for two-dimensional electronic spectroscopy

Data for publication: T. Begusic, J. Vanicek, On-the-fly ab initio semiclassical evaluation of third-order response functions for two-dimensional electronic spectroscopy, J. Chem. Phys., 153, 184110 (2020). Contains simulated linear and two-dimensional spectra of the S1 - S0 electronic transition of phenol, excited-state and ground-state ab initio trajectories at the PBE0/6-311G(d,p) level of theory, and other supporting data related to the abovementioned publication.

opencc-by-4.0Nov 2020View details →

Unblocking Breakthroughs: How DEIP Are Using Blockchain and Open Science to Provide Innovation Evaluation

Episode Summary: In this episode we talk to Alex Shkor about his company DEIP which is a peer review platform and blockchain protocol for the evaluation of intellectual capital, research, and innovation. We discussed why Alex created this platform, what the potential for digital research and big data is, and how Open Science can accelerate research.  Episode Links:  <a href="https://deip.world/">DEIP</a>

opencc-by-4.0Nov 2020View details →

Evaluating registrations of serial sections with distortions of the ground truths. Supplemental data

Evaluating Registrations of Serial Sections With Distortions of the Ground Truths This is the supplemental data for our paper on how to benchmark registrations of serial sections with ground truths. The files are named as follows: <ul> <li>*_challenge.7z: local distortions and global rigid transformations applied, the input for the benchmark we used. Use this to test your rigid and non-rigid methods.</li> <li>*_local-only.7z: only local distortions applied.</li> <li>*_local-DIST.7z: the distortion maps for local distortions.</li> <li>*_SURF-rigid.7z: local distortions and global rigid transformations applied, rigid transformations undone with SURF-based rigid-only method. Local distortions remain. Use this if your method does not cope well with large rigid transformations.</li> <li>_*vis.7z: visualizations of distortions.</li> <li>_rigid_ground.7z: the real rigid transformations used in the global phase.</li> <li>*_ground.7z: the ground truth. All data fit each other, no distortions. Use this to compare your registration result to it.</li> </ul> There are three main modalities and one further, as a reference: <ul> <li>CT_*: µCT data, a rabbit lung, 600 images. (In ground truth, and local distortions, and global transformations we supply more images that went into the benchmark, 50 more from both beginning and end.)</li> <li>EM_*: an EM serial block-face (SBF-SEM) data set of adult mouse lung, 1000 images. (EM ground truth is individually normalized, see paper.)</li> <li>LS_*: a lung from the light sheet microscopy from a male 24 week-old rat, 300 images. (LS ground truth is individually normalized, too.)</li> <li>REAL_*: a region from real serial sections from a rabbit lung, 2 images.</li> </ul> We also supply elastix parameter files. A preprint has been uploaded to <a href="https://arxiv.org/abs/2011.11060">arXiv</a>. The definite version is available from <a href="https://ieeexplore.ieee.org/abstract/document/9594850/media#media">IEEE</a>. The source code of the distorter is available from <a href="https://github.com/olegl/distort">GitHub</a>.

Disease and lesion maps - part of the Scientific opinion on the evaluation of public and animal health risks in case of a delayed post-mortem inspection in ungulates

EFSA was requested to assess the impact on effectiveness of post-mortem inspection in terms of any change in the sensitivity of detection of animal diseases of domestic and wild ungulates listed according to Article 5 of Regulation (EC) No 2016/429 and septicaemia, pyaemia, toxaemia or viraemia, when carried out after up to 24 hours or up to 72 hours after slaughter in comparison to when it is carried out immediately after slaughter. In order to identify the main lesions associated with the target diseases, a so called “disease map” was built, with the information about the clinical forms of the diseases  (acute, subacute, chronic, or latent forms) and clinical signs, as well as potential lesions that could be observed on the respective diseased animals. The following information is indicated for each disease/condition in the disease map: <ol> <li>The susceptible animal species</li> <li>Whether there is any surveillance programme in place in the EU</li> <li>The signs associated with the disease that could be detected at ante mortem inspection;</li> <li>The lesions associated with the disease that could be detected at post mortem inspection</li> <li>The probability of detecting the disease during the PMI as normally carried out.</li> <li>Whether carcass swabbing and/or laboratory tests are normally carried out.</li> </ol> From the disease map, the list of organs to be considered at post mortem inspection and the lesions, a “lesion map” was built by connecting animal species with organs, lesions and corresponding disease. This was done to facilitate the construction of a questionnaire where, for each organ and the five types of lesions, the respondents (meat inspectors) had to provide a numerical answer about how many carcasses out of 100 with the given lesion, will be still detected after 24- or 72-h of refrigerated storage.

Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kočevje Rog

An elevation model of Kočevje Rog, Slovenia Landform features: karstified plateau, karst Resolution: 2 meter, 4,500 x 4,500 height samples File format: Esri ASCII This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models. When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models: Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a>

Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kočevje Rog GeoTIFF

An elevation model of Kočevje Rog, Slovenia Landform features: karstified plateau, karst Resolution: 2 meter, 4,500 x 4,500 height samples File format: GeoTIFF This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models. When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models: Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a>

Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Bryce Canyon GeoTIFF

An elevation model of Bryce Canyon, USA Landform features:  narrow rock formations known as hoodoos Resolution: 1 meter, 4,000 x 3,800 height samples File format: GeoTIFF This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models. When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models: Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a>

Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Bryce Canyon ASCII

An elevation model of Bryce Canyon, USA Landform features:  narrow rock formations known as hoodoos Resolution: 1 meter, 4,000 x 3,800 height samples File format: Esri ASCII This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models. When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models: Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a>

opencc-by-4.0Jul 2020View details →

Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms

This is a set of elevation models of archetypal landforms:  <ul> <li>volcanic caldera (Crater Lake, Oregon, USA),</li> <li>active sand dunes (Great Sand Dunes, Colorado, USA),</li> <li>a braided riverbed (Jackson Hole, Wyoming, USA),</li> <li>folded ridges (Massanutten Mountain, Virginia, USA),</li> <li>stabilized sand dunes (Sandhills, Nebraska, USA),</li> <li>crater of a shield volcano (Kilauea, Hawaii, USA),</li> <li>karst plateau (Kočevje Rog, Slovenia),</li> <li>narrow rock formations, aka hoodoos (Bryce Canyon, USA)</li> </ul> All elevation models were derived from NED LiDAR sources with cell sizes ranging from 1 to 10 meters. The size of the models varies between approximately 4,000 × 4,000 and 5,500 × 5,500 height samples. The elevation models are available in georeferenced GeoTIFF and Esri ASCII file formats. Version 2 adds models of Kilauea, Hawaii, USA, Kočevje Rog, Slovenia, and Bryce Canyon, USA. When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models: Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a>

Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kilauea GeoTIFF

An elevation model of Kīlauea, Hawaii, USA Landform features: shield volcano crater Resolution: 1 meter, 7,200 x 6,800 height samples File format: GeoTIFFI This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models. When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models: Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a>

Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kilauea ASCII

An elevation model of Kīlauea, Hawaii, USA Landform features: shield volcano crater Resolution: 1 meter, 7,200 x 6,800 height samples File format: Esri ASCII This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models. When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models: Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a>

Non-human assemblies evaluated in the hifiasm paper

Non-human assemblies evaluated in <a href="https://www.nature.com/articles/s41592-020-01056-5">Cheng et al (2021)</a>. Human assemblies are available via <a href="https://zenodo.org/record/4393631">doi:10.5281/zenodo.4393631</a>.

opencc-by-4.0Jan 2021View details →

Automated prediction of visual complexity of web pages: Tools and evaluations

This dataset includes the screenshots of the web pages used for the evaluation of ViCRAM which is described in the following paper: Eleni Michailidou, Sukru Eraslan, Yeliz Yesilada, and Simon Harper. 2020. Automated Prediction of Visual Complexity of Web Pages: Tools and Evaluations. International Journal of Human-Computer Studies (SCI-E, SSCI), 145, 102523.

opencc-by-4.0Jan 2021View details →

Soil Dataset for Land evaluation in southwestern of Egyptian Delta

Soil Dataset  for Land  evaluation in southwestern of Egyptian Delta

opencc-by-4.0Jan 2021View details →

User Interaction Evaluation of 3D Handicraft Products Application

The dataset for analysis during the study for evaluation of 3D handicraft products application for smartphones usage

opencc-by-nc-nd-4.0Jun 2021View details →

Evaluation results for When a Computer Cracks a Joke: Automated Generation of Humorous Headlines

Evaluation results for the paper: Alnajjar, K., & Hämäläinen, M. (2021) When a Computer Cracks a Joke: Automated Generation of Humorous Headlines. In The Proceedings of the Twelfth International Conference on Computational Creativity, ICCC’21. The table has the aggregated evaluation results for each evaluation question. The left and right columns are the title before and after the replacement word. The replacement column show the humorous word and original column the word that existed in the headline before the replacement. The system column indicates whether the humorous headline was produced by our system or by a human.

Fig. 3 in Are Abrolhos no-take area sites of naïve fish? An evaluation using flight initiation distance of labrids

Fig. 3. Mean of flight initiation distances for the three labrid species studied in MUAs (Multiple-use areas) and NTAs (No-take areas) in Abrolhos. The upper limits of lines indicate standard deviation; *symbols indicate significant differences according to ANCOVA; **(p <0.001).

Fig. 4 in Are Abrolhos no-take area sites of naïve fish? An evaluation using flight initiation distance of labrids

Fig. 4. Relationship between flight initiation distances (FID), group size and body size (continuous covariates) inside NTAs and MUAs (categorical factor). Black dots represent samples from no-take areas (NTAs); white dots represent samples from multiple-use areas (MUAs). The continuous line represents the best fit for MUAs data and the dotted line that for NTAs data.

Fig. 1 in Are Abrolhos no-take area sites of naïve fish? An evaluation using flight initiation distance of labrids

Fig. 1. Map of study area showing sampled sites (MUAs: Multiple-use areas; NTAs: No-take areas) in the Abrolhos bank.

Fig. 5 in Are Abrolhos no-take area sites of naïve fish? An evaluation using flight initiation distance of labrids

Fig. 5. Average group size (a) and body size (b) estimated for the three labrids studied in both no-takes (NTAs) and multiple-use areas (MUAs) sampled in the Abrolhos Bank. The upper limits of lines indicate standard error.