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Landsat Enhanced Thematic Mapper image data for Baltimore Ecosystem Study collected on 2011-02-27

This LTER Remote Sensing spatial raster dataset consists of Landsat Enhanced Thematic Mapper image data for Baltimore Ecosystem Study, on 2011-02-27 (15:36:24.6050250Z). Data were collected by Landsat 5, row 33, path 15. Cloud cover was 43.43 percent. These are reference data from the USGS EROS archive, not data generated by Baltimore Ecosystem Study. This product was created by the U.S. Geological Survey (USGS) and contains Landsat data files in Geographic Tagged Image-File Format (GeoTIFF). NASA Landsat Program, 2009, Landsat TM LT50150332011058GNC01, LPGS_12.0.2, USGS, Sioux Falls, 2012-07-13T00:58:26Z.

Landsat Enhanced Thematic Mapper image data for Baltimore Ecosystem Study collected on 2011-05-02

This LTER Remote Sensing spatial raster dataset consists of Landsat Enhanced Thematic Mapper image data for Baltimore Ecosystem Study, on 2011-05-02 (15:36:04.7160750Z). Data were collected by Landsat 5, row 33, path 15. Cloud cover was 40.09 percent. These are reference data from the USGS EROS archive, not data generated by Baltimore Ecosystem Study. This product was created by the U.S. Geological Survey (USGS) and contains Landsat data files in Geographic Tagged Image-File Format (GeoTIFF). NASA Landsat Program, 2009, Landsat TM LT50150332011122EDC00, LPGS_12.0.2, USGS, Sioux Falls, 2012-07-13T01:00:06Z.

Landsat Enhanced Thematic Mapper image data for Baltimore Ecosystem Study collected on 2011-06-03

This LTER Remote Sensing spatial raster dataset consists of Landsat Enhanced Thematic Mapper image data for Baltimore Ecosystem Study, on 2011-06-03 (15:35:54.2020500Z). Data were collected by Landsat 5, row 33, path 15. Cloud cover was 17.9 percent. These are reference data from the USGS EROS archive, not data generated by Baltimore Ecosystem Study. This product was created by the U.S. Geological Survey (USGS) and contains Landsat data files in Geographic Tagged Image-File Format (GeoTIFF). NASA Landsat Program, 2009, Landsat TM LT50150332011154EDC00, LPGS_12.0.2, USGS, Sioux Falls, 2012-04-23T05:54:21Z.

Landsat Enhanced Thematic Mapper image data for Baltimore Ecosystem Study collected on 2011-07-05

This LTER Remote Sensing spatial raster dataset consists of Landsat Enhanced Thematic Mapper image data for Baltimore Ecosystem Study, on 2011-07-05 (15:35:39.2190560Z). Data were collected by Landsat 5, row 33, path 15. Cloud cover was 7.54 percent. These are reference data from the USGS EROS archive, not data generated by Baltimore Ecosystem Study. This product was created by the U.S. Geological Survey (USGS) and contains Landsat data files in Geographic Tagged Image-File Format (GeoTIFF). NASA Landsat Program, 2009, Landsat TM LT50150332011186EDC00, LPGS_12.0.2, USGS, Sioux Falls, 2012-04-21T20:53:45Z.

Landsat Enhanced Thematic Mapper image data for Baltimore Ecosystem Study collected on 2011-07-21

This LTER Remote Sensing spatial raster dataset consists of Landsat Enhanced Thematic Mapper image data for Baltimore Ecosystem Study, on 2011-07-21 (15:35:31.9450130Z). Data were collected by Landsat 5, row 33, path 15. Cloud cover was 1.98 percent. These are reference data from the USGS EROS archive, not data generated by Baltimore Ecosystem Study. This product was created by the U.S. Geological Survey (USGS) and contains Landsat data files in Geographic Tagged Image-File Format (GeoTIFF). NASA Landsat Program, 2009, Landsat TM LT50150332011202EDC00, LPGS_12.0.2, USGS, Sioux Falls, 2012-05-30T19:17:19Z.

Landsat Enhanced Thematic Mapper image data for Baltimore Ecosystem Study collected on 2011-08-22

This LTER Remote Sensing spatial raster dataset consists of Landsat Enhanced Thematic Mapper image data for Baltimore Ecosystem Study, on 2011-08-22 (15:35:13.8790440Z). Data were collected by Landsat 5, row 33, path 15. Cloud cover was 6.39 percent. These are reference data from the USGS EROS archive, not data generated by Baltimore Ecosystem Study. This product was created by the U.S. Geological Survey (USGS) and contains Landsat data files in Geographic Tagged Image-File Format (GeoTIFF). NASA Landsat Program, 2009, Landsat TM LT50150332011234EDC00, LPGS_12.0.0, USGS, Sioux Falls, 2012-03-28T11:31:10Z.

Landsat Enhanced Thematic Mapper image data for Baltimore Ecosystem Study collected on 2011-10-09

This LTER Remote Sensing spatial raster dataset consists of Landsat Enhanced Thematic Mapper image data for Baltimore Ecosystem Study, on 2011-10-09 (15:34:30.6380310Z). Data were collected by Landsat 5, row 33, path 15. Cloud cover was 0 percent. These are reference data from the USGS EROS archive, not data generated by Baltimore Ecosystem Study. This product was created by the U.S. Geological Survey (USGS) and contains Landsat data files in Geographic Tagged Image-File Format (GeoTIFF). NASA Landsat Program, 2009, Landsat TM LT50150332011282EDC00, LPGS_12.0.0, USGS, Sioux Falls, 2012-03-28T11:42:20Z.

Landsat Enhanced Thematic Mapper image data for Baltimore Ecosystem Study collected on 2011-10-25

This LTER Remote Sensing spatial raster dataset consists of Landsat Enhanced Thematic Mapper image data for Baltimore Ecosystem Study, on 2011-10-25 (15:34:22.7470060Z). Data were collected by Landsat 5, row 33, path 15. Cloud cover was 0.02 percent. These are reference data from the USGS EROS archive, not data generated by Baltimore Ecosystem Study. This product was created by the U.S. Geological Survey (USGS) and contains Landsat data files in Geographic Tagged Image-File Format (GeoTIFF). NASA Landsat Program, 2009, Landsat TM LT50150332011298EDC00, LPGS_12.0.0, USGS, Sioux Falls, 2012-03-28T11:43:38Z.

opencc-by-4.0Jan 2020View details →

Roadmap of MBT to SPL, images and selected studies

Roadmap of MBT to SPL, images and selected studies

opencc-by-4.0May 2020View details →

A Large-scale Data Set and an Empirical Study of Docker Images Hosted on Docker Hub

The data set schema, fields description, and analysis scripts are in the GitHub artifact repository. https://github.com/linncy/icsme2020-docker-study   @inproceedings{LinICSME20, author={Changyuan Lin and Sarah Nadi and Hamzeh Khazaei}, title={A Large-scale Data Set and an Empirical Study of Docker Images Hosted on Docker Hub}, booktitle={Proceedings of the 36th IEEE International Conference on Software Maintenance and Evolution (ICSME)}, year={2020}, url_Paper={https://www.dropbox.com/s/3bktcmdr7rlw1ic/LinICSME20.pdf} }

opencc-zeroAug 2020View details →

Data from: Headache study: The management of chronic headache with referral from primary care to direct access to Magnetic Resonance Imaging (MRI) compared to Neurology services: an observational prospective study in London

Objectives. To evaluate the cost, accessibility and patient satisfaction implications of two clinical pathways used in the management of chronic headache. Intervention. Management of chronic headache following referral from Primary Care that differed in the first appointment, either a Neurology appointment or an MRI brain scan. Design and setting. A pragmatic, non-randomised, prospective, single-center study at a Central Hospital in London. Participants. Adult patients with chronic headache referred from Primary to Secondary Care. Primary and secondary outcome measures. Participants' use of health care services and costs were estimated using primary and secondary care databases and questionnaires quarterly up to 12 months post-recruitment. Cost analyses were compared using generalised linear models (GLM). Secondary outcomes assessed: access to care, patient satisfaction, headache burden and self-perceived quality of life using headache-specific (MIDAS, HIT-6) and a generic questionnaire (EQ-5D-5L). Results. Mean (SD) cost up to 6 months post-recruitment per participant was £578 (£420) for the Neurology group (n=128) and £245 (£172) for the MRI group (n=95), leading to an estimated mean cost difference of £333 (95% CI £253 to £413, p<0.001). The mean cost difference at 12 months increased to £518 (95% CI £401 to £637, p<0.001). When adjusted for baseline and follow-up imbalances between groups, this remained statistically significant. The utilisation of brain MRI improved access to care compared to the Neurology group (p<0.001). Participants in the Neurology group reported higher levels of satisfaction associated with the pathway and led to greater change in care management. Conclusion. Direct referral to brain MRI from Primary Care led to cost-savings and quicker access to care but lower satisfaction levels when compared with referral to Neurology services. Further research into the use of brain MRI for a subset of patient population more likely to be reassured by a negative brain scan should be considered.

opencc-zeroDec 2018View details →

Data from: Accuracy of identifications of mammal species from camera trap images: a northern Australian case study

Camera traps are a powerful and increasingly popular tool for mammal research, but like all survey methods, they have limitations. Identifying animal species from images is a critical component of camera trap studies, yet while researchers recognize constraints with experimental design or camera technology, image misidentification is still not well understood. We evaluated the effects of a species' attributes (body mass and distinctiveness) and individual observer variables (experience and confidence) on the accuracy of mammal identifications from camera trap images. We conducted an Internet‐based survey containing 20 questions about observer experience and 60 camera trap images to identify. Images were sourced from surveys in northern Australia and included 25 species, ranging in body mass from the delicate mouse (Pseudomys delicatulus, 10 g) to the agile wallaby (Macropus agilis, >10 kg). There was a weak relationship between the accuracy of mammal identifications and observer experience. However, accuracy was highest (100%) for distinctive species (e.g. Short‐beaked echidna [Tachyglossus aculeatus]) and lowest (36%) for superficially non‐distinctive mammals (e.g. rodents like the Pale field‐rat [Rattus tunneyi]). There was a positive relationship between the accuracy of identifications and body mass. Participant confidence was highest for large and distinctive mammals, but was not related to participant experience level. Identifications made with greater confidence were more likely to be accurate. Unreliability in identifications of mammal species is a significant limitation to camera trap studies, particularly where small mammals are the focus, or where similar‐looking species co‐occur. Integration of camera traps with conventional survey techniques (e.g. live‐trapping), use of a reference library or computer‐automated programs are likely to aid positive identifications, while employing a confidence rating system and/or multiple observers may lead to a collection of more robust data. Although our study focussed on Australian species, our findings apply to camera trap studies globally.

opencc-zeroDec 2013View details →

Data from: Early treatment response in non-small cell lung cancer patients using diffusion-weighted imaging and functional diffusion maps - a feasibility study

Objective: The aim of this study was to prospectively evaluate the feasibility of monitoring treatment response to chemotherapy in patients with non-small cell lung carcinoma using functional diffusion maps (fDMs). Materials and Methods: This study was approved by the Cantonal Research Ethics Committee and informed written consent was obtained from all patients. Nine patients (mean age = 66 years; range = 53–76 years, 5 females, 4 males) with overall 13 lesions were included. Imaging was performed within two weeks before initiation of chemotherapy and at one, two, and six weeks after initiation of chemotherapy. Imaging included a respiratory-triggered diffusion-weighted sequence including three b-factors (100, 600, and 800 s/mm2). Treatment response was defined by change in tumor diameter on computed tomography (CT) after two cycles of chemotherapy. Changes in the apparent diffusion coefficient (ADC) on a per-lesion basis and the percentages of voxel with significantly increased or decreased ADCs on fDMs were analyzed using repeated measures analysis of variance (ANOVA). Changes in tumor size were used as covariate to examine the ability of ADCs and fDM parameters to predict treatment response. Results: Repeated measures ANOVA revealed that the percentage of voxels with increased ADCs on fDMs (p = 0.002) as well as the mean ADC increase (p = 0.011) were significantly higher in good responders with a large reduction in tumor size on CT. Conclusion: Our results indicate that the percentage of voxels with significantly increased ADCs on fDMs seems to be a promising biomarker for early prediction of treatment response in patients with non-small cell lung carcinoma. Contrary to averaged values, this approach allows the spatial heterogeneity of treatment response to be resolved.

opencc-by-4.0Dec 2023View details →

Plagiarm The Study Investigates How R&D Can Mitigate The Influence Of A CEO'S Masculine Image On Leverage

Plagiarm The Study Investigates How R&D Can Mitigate The Influence Of A CEO'S Masculine Image On Leverage

opennotspecifiedNov 2021View details →

FIGURE. SEM images of the studied Coelastrella strains: (A) SYKOA Ch-045-09. (B) SYKOA Ch-047-11. (C) SYKOA Ch-072-17. (D) IRK-A 173. (E) IRK-A 2. Scale bar: 10μm. in Morphological and phylogenetic relations of members of the genus Coelastrella (Scenedesmaceae, Chlorophyta) from the Ural and Khentii Mountains (Russia, Mongolia)

FIGURE. SEM images of the studied Coelastrella strains: (A) SYKOA Ch-045-09. (B) SYKOA Ch-047-11. (C) SYKOA Ch-072-17. (D) IRK-A 173. (E) IRK-A 2. Scale bar: 10μm.

opencc-by-4.0Dec 2021View details →

Considerations on baseline generation for Imaging AI studies illustrated on the CT-based prediction of empyema and outcome assessment.

Considerations on baseline generation for Imaging AI studies illustrated on the CT-based prediction of empyema and outcome assessment. Introduction: For AI-based classification tasks in computed tomography, a reference standard for evaluating the clinical diagnostic accuracy of individual classes is essential. To enable the implementation of an AI tool in clinical practice, this should be drawn from clinical routine data, using State-of-the-art scanners, evaluated in a blinded manner, and verified with a reference test. Methods: 2659 consecutive CTs performed between 01/2016 and 01/2021 with reported pleural effusion were retrospectively included. Pathology reports from thoracocentesis or biopsy within 7 days of CT were used as reference standard (n = 335). Two radiologists (4 and 10 PGY) blindly assessed chest CTs (n=335, 81 empyemas) for pleural CT features and ICC was determined. In addition, both pleural CT features and radiological diagnosis were extracted from written radiological reports. If needed, consensus was achieved using an experienced radiologist's opinion (29 PGY). We assessed the correlation of these findings with the following patient outcomes: mortality and median hospital stay. Results: Specificity and sensitivity for clinical detection of empyema (N=81) were 90.94 (95%-CI 86.55-94.05) and 72.84 (95%-CI: 61.63-81.85%) in all effusions, with moderate to almost perfect interrater agreement for all pleural findings associated with empyema (Cohen's kappa = 0.41-0.82). Features describing pleural enhancement or thickening achieved the highest accuracy with 87.02% and 81.49%, respectively. Empyema was associated with a longer hospital stay (median= 20 versus 14 days), and findings consistent with pleural carcinosis impacted mortality.

opencc-by-4.0Nov 2021View details →

Dataset for Eye image effect in the context of pedestrian safety: a French questionnaire study

Dataset for the paper Eye image effect in the context of pedestrian safety: a French questionnaire study Introduction: Human behavior is therefore influenced by the presence of others, which scientists also call ‘the audience effect’. The use of social control to produce more cooperative behaviors may positively influence road use and safety. This study uses an online questionnaire to test how eyes images affect the behavior of pedestrians when crossing a road. Material and methods: Different eyes images of men, women and a child with different facial expressions -neutral, friendly and angry- were presented to participants who were asked what they would feel by looking at these images before crossing a signalized road. Participants completed a questionnaire of 20 questions about pedestrian behaviors (PBQ). The questionnaire was received by 1,447 French participants, 610 of whom answered the entire questionnaire. 71% of participants were women, and the mean age was 35±14 years. Results: Eye images give individuals the feeling they are being observed at 33%, feared at 5% and surprised at 26%, and thus seem to indicate mixed results about avoiding crossing at the red light. The expressions shown in the eyes are also an important factor: feelings of being observed increased by about 10-15% whilst feelings of being scared or inhibited increased by about 5% as the expression changed from neutral to friendly to angry. No link was found between the results of our questionnaire and those of the Pedestrian Behavior Questionnaire (PBQ). Conclusion: This study shows that the use of eye images could reduce illegal crossings by pedestrians, and is thus of key interest as a practical road safety tool. However, the effect is limited and how to increase this nudge effect needs further consideration.

opencc-zeroFeb 2022View details →

PRMI: A dataset of minirhizotron images for diverse plant root study

Understanding a plant's root system architecture (RSA) is crucial for a variety of plant science problem domains including sustainability and climate adaptation. Minirhizotron (MR) technology is a widely-used approach for phenotyping RSA non-destructively by capturing root imagery over time. Precisely segmenting roots from the soil in MR imagery is a critical step in studying RSA features. In this paper, we introduce a large-scale dataset of plant root images captured by MR technology. In total, there are over 72K RGB root images across six different species including cotton, papaya, peanut, sesame, sunflower, and switchgrass in the dataset. The images span a variety of conditions including varied root age, root structures, soil types, and depths under the soil surface. All of the images have been annotated with weak image-level labels indicating whether each image contains roots or not. The image-level labels can be used to support weakly supervised learning in plant root segmentation tasks. In addition, 63K images have been manually annotated to generate pixel-level binary masks indicating whether each pixel corresponds to root or not. These pixel-level binary masks can be used as ground truth for supervised learning in semantic segmentation tasks. By introducing this dataset, we aim to facilitate the automatic segmentation of roots and the research of RSA with deep learning and other image analysis algorithms.

opencc-by-4.0Mar 2014View details →

Data from: Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers.

Genotype data from Li H, Giger ML, Sun C, Ponsukcharoen U, Huo D, Lan L, Olopade OI, Jamieson AR, Brown JB, Di Rienzo A. (2014) Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers. Med Phys. 41(3)