Find research datasets worth reusing

opencc-by-4.0May 2020View details →

Data to "Point-wise correlations between 10-2 Humphrey visual field and OCT data in open angle glaucoma"

This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in: Cirafici, P., Maiello, G., Ancona, C., Masala, A., Traverso, C.E., & Iester M. (in press) Point-wise correlations between Humphrey visual field and OCT data in open angle glaucoma. Eye

opencc-zeroDec 2013View details →

Bulk Domain Names (26-Oct-2014) -- varocarbas.com

This post refers to an old set of outdated information.  The system extracting those links was the precursor of the current web domain ranking in https://varocarbas.com/domain_ranking/   CLARIFICATION: this has nothing to do with marketing, SEO or paid anything. The sole purpose of this system now and ever has been to show my software development skills and related issues (e.g., big system running on very restricted hardware). All the displayed information is meant to show the actual reality as objectively as possible.  There is only one way to be part of the aforementioned ranking or to get a better/worse position in it: the system (crawling bots, ranking algorithms, correction subsystems, etc.) voluntarily deciding so.

opencc-by-4.0Dec 2023View details →

Analyzing marine biofilms developed on carbon nanotube-modified surfaces by 3D OCT approach

Glass, epoxy resin, and carbon nanotubes (CNT) composite were analyzed regarding wettability by water contact angle measurement, and roughness by atomic force microscopy. Cyanobacterial biofilms formed by Nodosilinea cf. nodulosa LEGE 10377 were developed on these surfaces for seven weeks and under controlled hydrodynamic conditions. Biofilm wet weight and structural parameters such as biofilm thickness, contour coefficient, biovolume, porosity, and average size of non-connected pores obtained from Optical Coherence Tomography (OCT) were assessed.

opencc-by-4.0Nov 2021View details →

Data for: COVID-19 patents/patent applications (Jan. 2020 – Oct. 2021)

This dataset contains information regarding both applications and granted patents on COVID-19 disease Derwent Innovation database was used for data mining (accessed on Nov. 20, 2021). The patent search was carried out on by means of a precise set of keywords and performed in the title/abstract/claims search field. 6,148 Inpadoc patent families were retrieved.  The XLS file contains information related to Title, Abstract - DWPI , First Claim, Priority Number, Priority Date, Application Number, Application Date, Publication Number, Publication Date, IPC – Current, CPC – Current, Assignee/Applicant, Optimized Assignee, INPADOC Family Members.  The top countries/regions are China (3,271), WO (1,057), India (487), United States (455). The top IPC codes are listed in the following table:   <table align="center"> <tbody> <tr> <td> IPC </td> <td> Definition </td> <td> No. of patents/applications </td> </tr> <tr> <td> A61P 31/14 </td> <td> Antivirals for RNA viruses </td> <td> 1565 </td> </tr> <tr> <td> G01N 33/569 </td> <td> Biological material •• Chemical analysis of biological material ••• Immunoassay; Biospecific binding assay; Materials therefor •••• for microorganisms </td> <td> 783 </td> </tr> <tr> <td> C12Q 1/70 </td> <td> Measuring or testing processes • involving virus or bacteriophage </td> <td> 642 </td> </tr> <tr> <td> A61P 11/00 </td> <td> Drugs for disorders of the respiratory system </td> <td> 582 </td> </tr> <tr> <td> A61K 39/215 </td> <td> Medicinal preparations containing antigens or antibodies • Viral antigens •• Coronaviridae, e.g., avian infectious bronchitis virus </td> <td> 377 </td> </tr> </tbody> </table> Value of the dataset: prior art searches; patent landscape analysis  Steps to reproduce data:  CTB=("covid-19" OR "covid 19" OR "covid19" ADJ "SARS-CoV-2" OR "SARS-CoV2" OR "sarscov2" ADJ "2019 ncov" OR "2019-nCoV" OR "2019nCoV" ADJ "covid-2019" OR "covid 2019" OR "COVID2019" OR "severe acute respiratory syndrome coronavirus 2" OR "2019 novel coronavirus" OR "coronavirus disease 2019" OR "novel corona virus" OR "novel coronavirus" OR "new corona virus" OR "new coronavirus" OR "Wuhan coronavirus") CTB=title/abstract/claims

opencc-by-4.0Apr 2024View details →

European Investment Bank Projects in ACP, OCT, Africa, Asia, and Latin America (1957-2024)

This dataset offers a comprehensive analysis of European Investment Bank (EIB) projects in Africa, the Caribbean, and the Pacific (ACP) regions, Overseas Countries and Territories (OCT), Asia, and Latin America, spanning from 1975 to 2023. The dataset includes information on 2,558 projects; each entry in the dataset includes key project details such as the project’s sector, date of signature, and financial commitments. All numbers are in 2015 euros.

opencc-by-4.0Jul 2024View details →

SD4EO: AI-based synthetic satellite multispectral agricultural textures in Spain (Oct 2017 - Sep 2018)

This dataset has been created as part of the deliverables for ESA’s <a title="https://eo4society.esa.int/projects/sd4eo/" href="https://eo4society.esa.int/projects/sd4eo/" target="_blank" rel="noopener">SD4EO project.</a> It consists of textures generated using a multispectral variant of a still unpublished high-order statistical constraint synthesis method for each of the following crop types: <ul> <li> Barley.</li> <li> Wheat.</li> <li> Other grain leguminous.</li> <li> Peas.</li> <li> Fallow & Bare soil.</li> <li> Vetch.</li> <li> Alfalfa.</li> <li> Sunflower.</li> <li> Oats.</li> </ul> The initial data was sampled from satellite images, specifically from Copernicus’ Sentinel-1 and Sentinel-2 satellites. The images were acquired over a period from October 2017 to September 2018 on the central-east region of northern Spain (Castile and León and Catalonia). From these images, the corresponding crops were extracted and used as samples for assembling  large puzzles that have been applied as input reference images to generate the synthetic images that make up this dataset. The datasets of assembled crop field "puzzles" used as reference images combine the largest crop areas to create a square multispectral texture of the largest possible size that is a power of 2 (or nearly a power of 2). Each base image combines data from all available Sentinel-2 satellite passes for the same month and a previous monthly composition from Sentinel-1. Due to cloud masks influence, the shape and number of crops vary for each time sample, preventing the reuse of element disposition in the “puzzles” across different months. Therefore, we have a base image (puzzle) for each month and crop type, with a size dependent on the number and area of crops not covered by clouds. These base image sizes range between 256, 384, 512, 768, 1024, 1536, and 2048 pixels per side, influenced by weather conditions and crop type each year season. In this dataset, the synthetic texture sizes match the corresponding base image sizes to facilitate debugging the method implementation and enable subsequent comparisons. For crops with a base image size of 1536 pixels or larger, the generated synthetic images have been reduced to half their size to reduce computational costs and RAM requirements, thereby completing the synthesis faster. Consequently, there remains some diversity in file sizes, generally smaller for crop types with less cultivated area. Additionally, to increase the amount of available data, six variants have been synthesized from each base multispectral image. This number can be arbitrarily increased, as initialization with noise (random numbers) ensures the distinction among the generated data. File names are structured as follows: <ul> <li>Prefix "HO" indicating the synthesis method</li> <li>The crop type name: <ul> <li>Barley</li> <li>Wheat</li> <li>OtherGrainLeguminous</li> <li>Peas</li> <li>FallowAndBareSoil</li> <li>Vetch</li> <li>Alfalfa</li> <li>Sunflower</li> <li>Oats</li> </ul> </li> <li>Year/Month/01 (representing the start of the month period)</li> <li>Side length of the multispectral texture in pixels (based on the highest precision instrument of Sentinel-2: 10m x 10m)</li> <li>Number of the synthesis variant</li> </ul> The generation parameters for all images include: <ul> <li>Normalized and weighted bands (VH band influence increased by a factor of 3 compared to others)</li> <li>4 levels of depth in the Steerable pyramid</li> <li>6 orientations in the Steerable pyramid</li> <li>14 joint statistics of the wavelet coefficients corresponding to basis functions at adjacent spatial locations, orientations, and scales. This parameter is crucial for capturing local dependencies between wavelet coefficients, essential for the visual perception of texture.</li> <li>30 iterations</li> </ul> A significant effort has been made to stabilize the algorithm, and to eliminate artifacts in the generated textures, resulting in much more robust outcomes. However, in rare cases, the initial white noise distribution can be statistically unfavorable, leading to instabilities. Files have been left as generated, without correcting these effects, to make them visible despite their low frequency. Specifically, among the 657 generated multispectral textures, this phenomenon has occurred prominently in only two and is relatively noticeable in another two, leaving the rest free of this effect (affecting less than 1% of the syntheses). Thus, the following files can be considered partially failed syntheses: <ul> <li>HO_Alfalfa_20180801_768_1.nc</li> <li>HO_FallowAndBareSoil_20180101_768_3.nc</li> <li>HO_OtherGrainLeguminous_20171201_256_4.nc</li> <li>HO_Vetch_20180301_384_3.nc</li> </ul> Files are encoded in the standardized net4CDF format [<a href="https://unidata.github.io/netcdf4-python/">link</a>], each containing a single xarray with metadata corresponding to a 3D array with the synthesized texture of the indicated crop type and satellite passes for the regions of Castilla y León and Catalonia for the corresponding monthly period. The most important data structure is the 3D array, where the first two dimensions correspond to the pixel extent indicated in the file name as square textures ('x' and 'y' labels in the xarray). The third dimension denotes the spectral band of the satellite, ordered by constellation and pixel size: <ul> <li>'B02' 10m (Sentinel-2)</li> <li>'B03' 10m (Sentinel-2)</li> <li>'B04' 10m (Sentinel-2)</li> <li>'B08' 10m (Sentinel-2)</li> <li>'B05' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B06' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B07' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B11' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B12' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B8A' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'VH' also resampled to 10m (Sentinel-1)</li> </ul> The original dynamic range is preserved in all bands, and they have been synthesized together using our multispectral algorithm variant. The new band combination may result in slightly unusual values in vegetation indices since restrictions were not considered in their transformed space, but in the latent space of the decorrelated Steerable pyramid. Additionally, the following metadata are stored as xarray attributes: <ul> <li>"long_name": corresponding to the crop type name</li> <li>"date": the period of the original data used as the base image for synthesis</li> <li>"dataset": denotes the combination of the initial Castilla y León dataset and the extended 6 Tiles from Catalonia</li> <li>"synthetic_method": corresponds to the high-order constrained method</li> <li>"max_visible_value": a reference value to maintain the same dynamic range when comparing with base images, avoiding distortions in color space and contrast</li> </ul> A total of:                   9 types of crops x 12 months x 6 variants = 648 synthetized multispectral textures occupying 34.5GB, have been organized and uploaded into 9 ZIP files (one per crop type) on the Zenodo website for distribution under Creative Commons Attribution 4.0 International license. The SD4EO Project is funded by the ESA’s FutureEO programme under contract no. 4000142334/23/I-DT and supervised by ESA Φ-lab.

opencc-by-4.0Aug 2023View details →

Afterslip distribution related to the Van Earthquake (Turkey) of 23 Oct. 2011

Two distributions of slip (afterslip) related to the Van earthquake - after 4 days - after 17 days as reported in Figure 9 and 10 of the paper Deformation and Related Slip Due to the 2011 Van Earthquake (Turkey) Sequence Imaged by SAR Data and Numerical Modeling Elisa Trasatti, Cristiano Tolomei, Giuseppe Pezzo, Simone Atzori and Stefano Salvi Rem. Sens. 2016, 8, 532; doi:10.3390/rs8060532   Coordinates, depth and slip in meters. The geographical coordinates are projected with UTM zone 38. The depth of the first row of patches is positive due to the elevation of the area.

opencc-by-4.0Aug 2023View details →

OPTICAL COHERENCE TOMOGRAPHY (OCT) IMAGE DATASET OF RADIATION DERMATITIS

Optical Coherence Tomography (OCT) Image dataset of radiation dermatitis  Citing the Dataset The dataset is released under a Creative Commons Attribution license, so please cite the dataset if it is used in your work in any form. Published academic papers should use the academic paper citation for our paper.  Personal works, such as projects or blog posts, should provide a URL to this Zenodo page, though a reference to our paper would also be appreciated. Academic paper citation Photiou C., Cloconi C. & Strouthos I. Feature-Based vs. Deep-Learning Fusion Methods for the In Vivo Detection of Radiation Dermatitis Using Optical Coherence Tomography, a Feasibility Study. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01241-4 Personal use citation Include a link to this Zenodo page - 10.5281/zenodo.8238140 ACKNOWLEDGMENT This research is funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 739551 (KIOS CoE) and from the Republic of Cyprus through the Directorate General for European Programs, Coordination and Development. Contact Information If you would like further information about the dataset, or if you experience any issues downloading files, please contact us at photiou.christos@ucy.ac.cy. Dataset Description This dataset consists of Optical Coherence Tomography (OCT) images from 22 head and neck cancer patients undergoing radiotherapy. Specifically, this dataset includes OCT images of five stages of Acute Radiation Dermatitis (ARD), labelled by an expert oncologist as Grade 0 (0), Grade 1 (1), Grade 2a (2), Grade 2b (3) and Grade 3 (4). Twenty-two head and neck cancer patients who were scheduled to receive radiation therapy at the German Oncology Center (GOC) in Limassol, Cyprus, participated in this proof-of-concept trial. The trial has received bioethics approval from the Cyprus National Bioethics Committee (Cyprus National Bioethics Committee 2020/61) and informed consent was collected. Patients under the age of 18 or with disabilities, expectant women, those who had recently undergone radiation therapy in the same area, and patients with autoimmune diseases were excluded from the study. After informed consent, the irradiated side of the neck of the subjects, was imaged with OCT. The imaging was performed with a swept-source OCT system (Santec IVS300), with a center wavelength of 1300 nm, an axial resolution of 12 micrometers in tissue, and an A-scan rate of 40 kHz. Six images were acquired at 1 cm intervals, covering the region from the mandibular angle to the clavicle. Imaging was repeated prior to every radiation therapy session, twice per week, until the conclusion of the therapy, resulting in a dataset of 1487 images. During each visit, the patient's ARD grade, at each of the imaging sites, was determined and recorded by a senior oncologist. Dataset The data consists of two items: (1) the excel file 'Description.xlsx' with the patient information and (2) the zip file 'Dataset.zip' containing the images, as described below. 1) Description.xlsx This excel file contains patient information such as age, habits, etc, in the sheet 'Patient_Info'. The sheet 'Image_Info' contains the information for each image, such as the patient number (1-22), week number, visit number (usually one or two visits per week), image number (six images per visit with some exceptions), and classification (0-4).     2) Dataset.zip  This zip file contains the OCT images. Each patient's folder has sub-folders corresponding to each week, within which there are sub-folders corresponding to each visit, which contain the image folders. Each image folder contains two excel files: OCT Data (demodulated and logarithmic intensity image)  and Raw Data (resampled interferometric data).

opencc-by-4.0Dec 2022View details →

Lookout Fire Time-Lapse Video Taken from the Roswell Communication Tower for the Period of Record Aug. 10 - Oct. 23, 2023.

A lightning strike fire started on Saturday, August 5, within the H.J. Andrews Experimental Forest, between the base of Lookout cliff and the ridge dividing Lookout and Mack Creek drainages.  As of today, August 7, the fire is 2.5 acres. Two helicopters are traveling between the Blue River reservoir and the fire carrying water. The plan is to keep knocking back the fire until ground crews can get a line around it and contain it.  It is burning in steep terrain, in old growth with dense understory, which is making it a challenge for experienced ground crews.  In addition to the helicopters, a hotshot crew has been assigned to the fire, most likely starting August 8.These images were taken from remote based StarDot and NetCam IP cameras positioned 13 meters up the Roswell Communication tower located in the north east side of the HJ Andrews Experimental Forest. The HJAHQCAM video was recorded from a StarDot camera positioned on top of the HJ Andrews main office building. Andrews Forest LTER collected and manged these data in real-time and compiled a final time-lapse video of each camera using a multi-threaded python program.Additional Resources: <a href="https://andrewsforest.oregonstate.edu/about/news-events/lookout-fire-updates-2023">Andrews Forest LTER</a> <a href="https://www.youtube.com/@AndrewsForest/playlists">Andrews Forest Youtube</a> <a href="https://inciweb.wildfire.gov/incident-information/orwif-lookout-fire">InciWeb</a>This material is based upon work supported by the H.J. Andrews Experimental Forest and Long Term Ecological Research (LTER) program under the NSF grant LTER8 DEB-2025755.

openmit-licenseDec 2023View details →

Probabilistic volumetric speckle suppression in OCT using deep learning: Dataset

This file contains a retinal OCT intensity volume as a demo dataset to generate volumetric speckle-suppressed training data using our non-local-means despeckling (TNode) script and four OCT intensity volumes and their corresponding TNode-processed intensity volumes of different tissue samples to train and test our deep learning framework used in "Probabilistic volumetric speckle suppression in OCT using deep learning" by Chintada et al. 2023. The TNode code for generating the training data and the source code for our deep learning framework are available at https://github.com/bhaskarachintada/DLTNode.git

opencc-by-4.0Mar 2024View details →

Human Colorectal Tissue OCT Dataset: Neoplastic and Non-Neoplastic Samples from Chorioallantoic Membrane (CAM) Assays

A commercial OCT system (Telesto II-1325 LR spectral domain OCT (SD-OCT)) was used to imaging two colon cell lines implanted in a chick embryo chorioallantoic membrane (CAM) assay. This is a high-performance imaging system designed for in vivo and ex vivo imaging of biological tissues. The CAM is a highly vascularized extra-embryonic membrane connected to the developing embryo through an easily accessible circulatory system that allows the successful engraftment of a variety of foreign tissues, such as tumor explants or cancer cell lines. Neoplastic and non-neoplastic tumors were developed from RKO and NCM460 cell lines, respectively.   OCT B-scan images were collected from 30 CAM models, 15 with RKO-cells and 15 with NCM460-cells. The collected images are avalilable in format OCT format (Neoplastic_OCT.zip/Non-neoplastic_OCT.zip), TIFF format (Neoplastic_tiff.zip/Non-neoplastic_tiff.zip), and MAT format (Neoplastic_mat.zip/Non-neoplastic_mat.zip).   Since, the attenuation of near-infrared light in biological samples has proven to be a powerful tool for tissue characterization the attenuation coefficient of light was calculated for each image. This data is available in the Neoplastic_processed.zip and Non-neoplastic_processed.zip folders.

AMPERE GRD & IRD Data (2012-Oct)

2012-Oct AMPERE GRD and IRD data.AMPERE IRD files contain hemispheric delta-B observations in global coordinates (geographic and AACGM). Data are blocked into 10-minute 'chunks' at 2-minute resolution for use in the fitting algorithm. Measurements flagged 'bad' in the earlier processing are removed prior to entry in IRD.AMPERE GRD files contain hemispheric delta-B and Birkeland current fits at 2-minute resolution.

AMPERE GRD & IRD Data (2011-Oct)

2011-Oct AMPERE GRD and IRD data.AMPERE IRD files contain hemispheric delta-B observations in global coordinates (geographic and AACGM). Data are blocked into 10-minute 'chunks' at 2-minute resolution for use in the fitting algorithm. Measurements flagged 'bad' in the earlier processing are removed prior to entry in IRD.AMPERE GRD files contain hemispheric delta-B and Birkeland current fits at 2-minute resolution.

AMPERE GRD & IRD Data (2010-Oct)

2010-Oct AMPERE GRD and IRD data.AMPERE IRD files contain hemispheric delta-B observations in global coordinates (geographic and AACGM). Data are blocked into 10-minute 'chunks' at 2-minute resolution for use in the fitting algorithm. Measurements flagged 'bad' in the earlier processing are removed prior to entry in IRD.AMPERE GRD files contain hemispheric delta-B and Birkeland current fits at 2-minute resolution.

AMPERE GRD & IRD Data (2009-Oct)

2009-Oct AMPERE GRD and IRD data.AMPERE IRD files contain hemispheric delta-B observations in global coordinates (geographic and AACGM). Data are blocked into 10-minute 'chunks' at 2-minute resolution for use in the fitting algorithm. Measurements flagged 'bad' in the earlier processing are removed prior to entry in IRD.AMPERE GRD files contain hemispheric delta-B and Birkeland current fits at 2-minute resolution.

AMPERE GRD & IRD Data (2019-Oct)

2019-Oct AMPERE GRD and IRD data.AMPERE IRD files contain hemispheric delta-B observations in global coordinates (geographic and AACGM). Data are blocked into 10-minute 'chunks' at 2-minute resolution for use in the fitting algorithm. Measurements flagged 'bad' in the earlier processing are removed prior to entry in IRD.AMPERE GRD files contain hemispheric delta-B and Birkeland current fits at 2-minute resolution.

AMPERE GRD & IRD Data (2017-Oct)

2017-Oct AMPERE GRD and IRD data.AMPERE IRD files contain hemispheric delta-B observations in global coordinates (geographic and AACGM). Data are blocked into 10-minute 'chunks' at 2-minute resolution for use in the fitting algorithm. Measurements flagged 'bad' in the earlier processing are removed prior to entry in IRD.AMPERE GRD files contain hemispheric delta-B and Birkeland current fits at 2-minute resolution.

AMPERE GRD & IRD Data (2016-Oct)

2016-Oct AMPERE GRD and IRD data.AMPERE IRD files contain hemispheric delta-B observations in global coordinates (geographic and AACGM). Data are blocked into 10-minute 'chunks' at 2-minute resolution for use in the fitting algorithm. Measurements flagged 'bad' in the earlier processing are removed prior to entry in IRD.AMPERE GRD files contain hemispheric delta-B and Birkeland current fits at 2-minute resolution.