An Open Context "types" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This record is part of the "Pyla-Koutsopetria Archaeological Project I: Pedestrian Survey" data publication.

Please see ReadMe file.

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Data for Figure 3.30 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.30 shows observed and CMIP6 simulated AMOC mean state, variability and long-term trends. --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has 6 subpanels with data provided for all panels in subdirectories named panel_a, panel_b, panel_c, panel_d, panel_e and panel_f. --------------------------------------------------- List of data provided --------------------------------------------------- This dataset contains: - AMOC streamfunction profiles from CMIP5 (1860-2004) and CMIP6 (1860-2014) historical simulations - AMOC mean maximum overturning depth from CMIP5 (1860-2004) and CMIP6 (1860-2014) historical simulations - AMOC mean maximum overturning depth from RAPID observational dataset (2004-2018) - AMOC mean maximum overturning streamfunction from CMIP5 (1860-2004) and CMIP6 (1860-2014) historical simulations - AMOC mean maximum overturning streamfunction from RAPID observational dataset (2004-2018) - AMOC 8-year trends from CMIP5 and CMIP6 simulations and RAPID observations (2004-2012) - Interannual AMOC changes from CMIP5 and CMIP6 simulations and RAPID observations (2008-2010) - Longterm AMOC trends (1850-2014) from CMIP6 simulations - Longterm AMOC trends (1940-1985) from CMIP6 simulations - Longterm AMOC trends (1985-2014) from CMIP6 simulations --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- - panel_a/amoc_mean_state_boxes.csv has the data for the grey observations lines and blue and red boxes with whiskers - panel_a/amoc_profiles_shadings.csv has data for the blue and red profile shadings. - panel_a/amoc_profile_cmip5.csv has data for the blue profile - panel_a/amoc_profile_cmip6.csv has data for the red profile - panel_b/amoc_trends_2004_2012.csv has data for boxes and whiskers and outlier dots - panel_b/amoc_trends_cmip5_cmip6_additional_outliers.csv has data for additional outlier dots for CMIP5 and CMIP6 - panel_c/interannual_variability_AMOC.csv has data for boxes and whiskers and outlier dots - panel_c/interannual_variability_AMOC_cmip5_cmip6_additional_outliers.csv has data for additional outlier dots for CMIP5 and CMIP6 - panel_d/amoc_longtern_trend_1850_2014.csv has data for grey, green, blue and orange boxes and whiskers - panel_e/amoc_longtern_trend_1940_1985.csv has data for grey, green, blue and orange boxes and whiskers - panel_f/amoc_longtern_trend_1985_2014.csv has data for grey, green, blue and orange boxes and whiskers CMIP6 is the sixth phase of the Coupled Model Intercomparison Project. AMOC is the Atlantic Meridional Overturning Circulation. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo - Link to the figure on the IPCC AR6 website

Context

This dataset contains images of 6 fruits and vegetables: apple, banana, bitter gourd, capsicum, orange, and tomato. The images of each fruit or vegetable are grouped into two categories: fresh and stale. The purpose behind the creation of this dataset is the development of a machine learning model to classify fruits and vegetables as fresh or stale. This feature is a part of our final year project titled ‘Food Aayush’. (Github Link)

Data Collection and Preprocessing

For collecting the images to create the dataset, images of the fruits and vegetables were captured daily using a mobile phone camera. Depending on the visual properties of the fruit or vegetable in each image and on the day when the image was captured, each image was labelled as fresh or stale. Additionally, videos of the fruits and vegetables were taken, and the frames of these videos were extracted to collect a large number of images conveniently. The machine learning model requires a 224 x 224-pixel image. So, the images were cropped to extract the center square of the image and resized in 512 x 512 pixels using a data pre-processing library in Keras. Frame Extraction

Data Augmentation: We used ImageDataGenerator library from Keras for augmentation. We on average created 20 augmentations per image which indeed improve our models accuracy. Data Augmentation

Acknowledgements

We would like to give credit to this dataset as we have obtained the images in some of the classes from here. Dataset

Inspiration

Our BE final year project, titled ‘Food Aayush’, is an application that can be used for the classification of fruits and vegetables as fresh or stale, the classification of cooking oils into different rancidity levels, and the analysis of various parameters related to the nutritional value of food and people’s dietary intake. We have trained a machine learning model for the classification of fruits and vegetables. This dataset was created for training the machine learning model. Your data will be in front of the world's largest data science community. What questions do you want to see answered?

A minority of headache patients have a secondary headache disorder. The medical literature presents and promotes red flags to increase the likelihood of identifying a secondary etiology. In this review, we aim to discuss the incidence and prevalence of secondary headaches as well as the data on sensitivity, specificity, and predictive value of red flags for secondary headaches. We review the following red flags: (1) systemic symptoms including fever; (2) neoplasm history; (3) neurologic deficit (including decreased consciousness); (4) sudden or abrupt onset; (5) older age (onset after 65 years); (6) pattern change or recent onset of new headache; (7) positional headache; (8) precipitated by sneezing, coughing, or exercise; (9) papilledema; (10) progressive headache and atypical presentations; (11) pregnancy or puerperium; (12) painful eye with autonomic features; (13) posttraumatic onset of headache; (14) pathology of the immune system such as HIV; (15) painkiller overuse or new drug at onset of headache. Using the systematic SNNOOP10 list to screen new headache patients will presumably increase the likelihood of detecting a secondary cause. The lack of prospective epidemiologic studies on red flags and the low incidence of many secondary headaches leave many questions unanswered and call for large prospective studies. A validated screening tool could reduce unneeded neuroimaging and costs.

This record describes ancient sites and monuments as well archaeological excavations undertaken by Danish museums. Excerpt of the Danish description of events: 1936-11-28: R 186: Jernalderboplads (orange) i Tovrup i husmand Andersens mark. Kulturlag med karskår, knusesten og en ildbuk af brændt ler. 20.11.1936. Se bilag.1936-11-28: R 186: Jernalderboplads (orange) i Tovrup i husmand Andersens mark. Kulturlag med karskår, knusesten og en ildbuk af brændt ler. 20.11.1936. Se bilag.

This dataset contains shapefile boundaries for CA State, counties and places from the US Census Bureau's 2023 MAF/TIGER database. Current geography in the 2023 TIGER/Line Shapefiles generally reflects the boundaries of governmental units in effect as of January 1, 2023.

This record describes ancient sites and monuments as well archaeological excavations undertaken by Danish museums. Excerpt of the Danish description of events: 1932 : R 117-119 [sb. 39 og 230203 sb. 132-133]: Jernalderboplads (orange). Kom fremved ombygningen af Mommark-banen efteråret 1932. Store brandpletter med mange karskår. Se bilag. Kat.nr. 2484-86, 2494.1932 : R 117-119 [sb. 39 og 230203 sb. 132-133]: Jernalderboplads (orange). Kom fremved ombygningen af Mommark-banen efteråret 1932. Store brandpletter med mange karskår. Se bilag. Kat.nr. 2484-86, 2494.

Diatoms are diverse and widespread freshwater Eukaryotes that make excellent microbial subjects for addressing questions in metacommunity ecology. In the McMurdo Dry Valleys of Antarctica, the simple trophic structure of glacier-fed streams provides an ideal outdoor laboratory where well-described diatom assemblages are found within two cyanobacterial mat types, which occupy different habitats and vary in coverage within and among streams. Specifically, black mats of Nostoc spp. occur in marginal wetted habitats, and orange mats (Oscillatoria spp. and Phormidium spp.) occur in areas of consistent stream flow. Despite their importance as bioindicators for changing environmental conditions, the role of dispersal in structuring dry valley diatom metacommunities remains unclear. Here, we use MCSim, a spatially explicit metacommunity simulation package for R, to test alternative hypotheses about the roles of dispersal and species sorting in maintaining the biodiversity of diatom assemblages residing in black and orange mats. The spatial distribution and patchiness of cyanobacterial mat habitats was characterized by remote imagery of the Lake Fryxell sub-catchment in Taylor Valley. The available species pool for diatom metacommunity simulation scenarios was informed by the Antarctic Freshwater Diatoms Database, maintained by the McMurdo Dry Valleys Long Term Ecological Research program. We used simulation outcomes to test the plausibility of alternative community assembly hypotheses to explain empirically observed patterns of freshwater diatom biodiversity in the long-term record. The most plausible simulation scenarios suggest species sorting by environmental filters, alone, was not sufficient to maintain biodiversity in the Fryxell Basin diatom metacommunity. The most plausible scenarios included either (1) neutral models with different immigration rates for diatoms in orange and black mats or (2) species sorting by a relatively weak environmental filter, such that dispersal dynamics also influenced diatom community assembly, but there was not such a strong disparity in immigration rates between mat types. The results point to the importance of dispersal for understanding current and future biodiversity patterns for diatoms in this ecosystem, and more generally, provide further evidence that metacommunity theory is a useful framework for testing hypotheses about microbial community assembly.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

No description is available. Visit https://dataone.org/datasets/19cb0e940ed7325fb1fa812ee1d648f7 for complete metadata about this dataset.