The raw data of icon experiment.

Raw data of ICON®️ in the Pompe disease girl infant during the operation.

Open AccessClimate data for modelling data past 2012 can be obtained from the IRRI Climate Group

ICON

Introducing non-flooded crops in rice-dominated landscapes: Impact on CarbOn, Nitrogen and water budgets

Project 4 (Disease epidemics in rice-based systems affected by changes in water management;

IRRI, Savary – no funding requested) will monitor disease progress - in particular sheath blight - in relation to the physical environment of the soil and of the canopy (microclimate), in both the rice and the maize crops (Project 3). The shift from flooded to non-flooded cropping systems directly affects the physical environment and occurrence of natural enemies of the soil-borne pathogens and this, indirectly, affects the physical environment of the canopy, where non soil-borne pathogens may develop (H.1). Rhizoctonia species are soil-borne fungi causing sheath blight in rice, a major disease in rice production, and there is indication that some of the R. solani sub-species can infect maize as well. In this project emphasis will be given to identify the responses of Rhizoctonia as well as other pathogens to (i) crop rotation and (ii) water management regime in order to develop functional relationships between cropping system and crop management and disease progress (H.3).

Change in water management is a prerequisite for adaptation of rice-based agroecosystems in a context of climate change. While water-saving technologies, including supply of agricultural water (the largest user of water in tropical Asia), but also tillage and crop establishment is necessary, singignificant, and possibly considerable changes are to be expected with respect to the entire guild of yield-reducing organisms of rice, including pathogens (bacteria, fungi, and viruses), as well as insects (Savary et al., 2005).

It is worth noting here that this work is congruent with large scale work IRRI has engaged in South Asia, under the umbrella of the Cereal System Initiative for South Asia. This project, among a series of objectives, aims at improving the performances of environmentally constrained – especially, water constrained – intensive cereal systems that must develop to feed South Asia for the decades to come; and this includes a series of heavily instrumented platforms where work similar to what is described below will be conducted.

Over the years, IRRI has developed a set of methodologies – coupled standardized acquisition methods of injuries (IP) due to diseases and insects, as well as weeds; characterization of production situations (PS), including the physiological status of the crop; statistical multivariate, non-parametric methods to link IPs and PSs; and simulation modeling methods to analyze the effects of individual yield reducing organism of the guild within a community. A recent publication summarizes these methods and their applications (Savary et al, 2006).

Project 4 of ICON will look at a series of attributes that will be changed with evolving water supply to rice crops:

• meso-climate (which will be monitored in the overall experiment);
• micro-climate, and I particular, leaf temperature and leaf wetness duration.

We intend to implement the above methodology at successive development stages, including at least:

• Maximum tillering
• Booting
• Early dough

1. leaf diseases (esp., bacterial blight, sheath blight, blast, brown spot, narrow brown spot, bacterial leaf streak)
2. tiller diseases (esp. sheath blight, sheath rot, stem rot)
3. panicle diseases (esp. grain discoloration, false smut, bakanae)
4. whole-plant diseases (esp. rice tungro)
5. insect leaf injuries (esp. leaf folders, whorl maggots)
6. insect tiller injuries (esp. stem borers – “dead hearts”)
7. insect panicle injuries (esp. stem borers – “white heads”)
8. sucking insect populations (brown plant hopper, white-back planthopper, and green leaf hopper)

Groups a and e – leaf injury; b and f – tiller injury; c and g – panicle injury; d – systemic injury; and h – sucking injury represent the framework of the “sub-guilds” developed in the above approach to characterize yield-reducing yields. These also are the basis of RICEPEST (Willocquet et al., a generic, mechanistic, crop physiology-based simulation model which enables to explore the individual impact of specific yield-reducer, and their combined effects on systems’ performances. RIRCEPEST has been parameterized, tested, and validated in China, India, and the Philippines during several cropping seasons.

IRRI’s inputs in Project 4 should thus be seen twofold.

1. Quantification of the effects of varying levels of water management on the entire guild of yield-reducing organisms
   This component will make use of field data acquisition procedure that have been heavily tested and validated in China, India, Vietnam, and the Philippines, as well as in Laos and Cambodia. The main approach to analyze the data will be
   • conventional-parametric statistics: relating macro-, micro-climate, and water with individual levels of injuries;
   • non-parametric, including Bayesian, multivariate methods, to address the guild of yield-reducers in a given production situation as a whole.
   • The purpose of this component would primarily be descriptive, hypothesis-forwarding, and analytical.
2. Modeling yield losses due to the guild of yield reducers at different levels of water management

This component would involve RICEPEST, and therefore simulation modeling based on crop cuts at a series of development stages during the cropping season (a minimum of 4 crop cuts, especially at harvest, is necessary; however, 7-8 crop cuts would be desirable). These crop cuts would enable to (1) re-parameterize the model for different water regimes – keeping the flooded regime as a control (year 1), (2) test the model (year 2), and (3) conduct scenario and sensitivity analyses (year 3). The purpose of this second component would enable generating an outlook of the consequences of water regime scenarios on yield-reduction due to diseases and insects, as well as to identify which of the yield-reducing components of the guild are the most import in what context.

This second component would also enable linking Project 4 with other components of ICON in assessing the performances of water-constrained rice-based ecosystems in a holistic manner.

Savary, S., Castilla, N.P., Elazegui, F.A. & Teng, P.S., 2005. Multiple effects of two drivers of agricultural change, labour shortage and water scarcity, on rice pest profiles in tropical Asia. Field Crops Research 91/2-3: 263-271.

Savary, S., Teng, P.S., Willocquet, L. & Nutter, F.W., Jr., 2006. Quantification and modeling of crop losses: a review of purposes. Annual Review of Phytopathology 44: 89-112.

Willocquet, L., Elazegui, F. A., Castilla, N., Fernandez, L., Fischer, K. S., Peng, S., Teng, P. S., Srivastava, R. K., Singh, H. M., Zhu, D., and Savary, S., 2004. Research priorities for rice disease and pest management in tropical Asia: a simulation analysis of yield losses and management efficiencies. Phytopathology 94(7):672-682.

These datasets consist of preprocessed data for the training, validation, and testing of ICON-LEM over Germany and ICON-NWP over Holuhraun, comprising input-output pairs of cloud effective radius (m) and its corresponding autoconversion rates (kg m-3 s-1). The raw model output data used for deriving these datasets are available on request from tape archives at the DKRZ.

This repository contains the data used in the publication "Insights on Tropical High-Cloud Radiative Effect from a New Conceptual Model". It holds the raw data from the ARTS model, processed ARTS and ICON data, output from the conceptual model and the parameters of the conceptual model.

One line correspond to a session for tabs 1–6 and first part of tab 7. Second part of tab 7 contains the details of each session. Tab 8 contains the number of shot during the last session as well as the reaction times. (XLSX)

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Sunday to Saturday). These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities.

The hospital population includes all hospitals registered with Centers for Medicare & Medicaid Services (CMS) as of June 1, 2020. It includes non-CMS hospitals that have reported since July 15, 2020. It does not include psychiatric, rehabilitation, Indian Health Service (IHS) facilities, U.S. Department of Veterans Affairs (VA) facilities, Defense Health Agency (DHA) facilities, and religious non-medical facilities.

For a given entry, the term “collection_week” signifies the start of the period that is aggregated. For example, a “collection_week” of 2020-11-15 means the average/sum/coverage of the elements captured from that given facility starting and including Sunday, November 15, 2020, and ending and including reports for Saturday, November 21, 2020.

Reported elements include an append of either “_coverage”, “_sum”, or “_avg”.

• A “_coverage” append denotes how many times the facility reported that element during that collection week.
• A “_sum” append denotes the sum of the reports provided for that facility for that element during that collection week.
• A “_avg” append is the average of the reports provided for that facility for that element during that collection week.

The file will be updated weekly. No statistical analysis is applied to impute non-response. For averages, calculations are based on the number of values collected for a given hospital in that collection week. Suppression is applied to the file for sums and averages less than four (4). In these cases, the field will be replaced with “-999,999”.

A story page was created to display both corrected and raw datasets and can be accessed at this link: https://healthdata.gov/stories/s/nhgk-5gpv

This data is preliminary and subject to change as more data become available. Data is available starting on July 31, 2020.

Sometimes, reports for a given facility will be provided to both HHS TeleTracking and HHS Protect. When this occurs, to ensure that there are not duplicate reports, deduplication is applied according to prioritization rules within HHS Protect.

For influenza fields listed in the file, the current HHS guidance marks these fields as optional. As a result, coverage of these elements are varied.

For recent updates to the dataset, scroll to the bottom of the dataset description.

On May 3, 2021, the following fields have been added to this data set.

• hhs_ids
• previous_day_admission_adult_covid_confirmed_7_day_coverage
• previous_day_admission_pediatric_covid_confirmed_7_day_coverage
• previous_day_admission_adult_covid_suspected_7_day_coverage
• previous_day_admission_pediatric_covid_suspected_7_day_coverage
• previous_week_personnel_covid_vaccinated_doses_administered_7_day_sum
• total_personnel_covid_vaccinated_doses_none_7_day_sum
• total_personnel_covid_vaccinated_doses_one_7_day_sum
• total_personnel_covid_vaccinated_doses_all_7_day_sum
• previous_week_patients_covid_vaccinated_doses_one_7_day_sum
• previous_week_patients_covid_vaccinated_doses_all_7_day_sum

On May 8, 2021, this data set has been converted to a corrected data set. The corrections applied to this data set are to smooth out data anomalies caused by keyed in data errors. To help determine which records have had corrections made to it. An additional Boolean field called is_corrected has been added.

On May 13, 2021 Changed vaccination fields from sum to max or min fields. This reflects the maximum or minimum number reported for that metric in a given week.

On June 7, 2021 Changed vaccination fields from max or min fields to Wednesday reported only. This reflects that the number reported for that metric is only reported on Wednesdays in a given week.

On September 20, 2021, the following has been updated: The use of analytic dataset as a source.

On January 19, 2022, the following fields have been added to this dataset:

• inpatient_beds_used_covid_7_day_avg
• inpatient_beds_used_covid_7_day_sum
• inpatient_beds_used_covid_7_day_coverage

On April 28, 2022, the following pediatric fields have been added to this dataset:

• all_pediatric_inpatient_bed_occupied_7_day_avg
• all_pediatric_inpatient_bed_occupied_7_day_coverage
• all_pediatric_inpatient_bed_occupied_7_day_sum
• all_pediatric_inpatient_beds_7_day_avg
• all_pediatric_inpatient_beds_7_day_coverage
• all_pediatric_inpatient_beds_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum
• staffed_icu_pediatric_patients_confirmed_covid_7_day_avg
• staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage
• staffed_icu_pediatric_patients_confirmed_covid_7_day_sum
• staffed_pediatric_icu_bed_occupancy_7_day_avg
• staffed_pediatric_icu_bed_occupancy_7_day_coverage
• staffed_pediatric_icu_bed_occupancy_7_day_sum
• total_staffed_pediatric_icu_beds_7_day_avg
• total_staffed_pediatric_icu_beds_7_day_coverage
• total_staffed_pediatric_icu_beds_7_day_sum

On October 24, 2022, the data includes more analytical calculations in efforts to provide a cleaner dataset. For a raw version of this dataset, please follow this link: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/uqq2-txqb

Due to changes in reporting requirements, after June 19, 2023, a collection week is defined as starting on a Sunday and ending on the next Saturday.

This data was used for a experiments with graph convolutional neural networks for memory forensics as part of a bachelor thesis (included as pdf).

Abstract:

This work presents a neural network based approach for data structure detection in raw memory that does not require an entirely matching description of the target data structure. Instead, it’s merely necessary to provide multiple descriptions of data structures similar to the target as training data in the form of debugging symbols. The core contribution of this work is a formal description and implementation of encoding data structure definitions as well as raw memory contents such that they can be processed by graph convolutional neural networks. A description and implementation of a neural network meant to detect data structures in the memory contents of a Linux Kernel demonstrates the practical applicability of the described approach.

The Code is available on GitHub https://github.com/NiklasBeierl/nosyms.

nokaslr_dump is the qemu memory snapshot used to test the model. nokaslr.raw is the "raw" form of the snapshot as produced by Volatility 3's layerwriter plugin. symbols-training-data contains the Volatility symbol JSON files from which training data was derived. nokaslr_pointers.csv lists the kernel space pointers in the snapshot and nokaslr_tasks.csv lists task structs in the snapshot. Both were extracted via a Volatility plugins that are included in the GitHub Repo. vmlinux-5.4.0-58-generic.json is the symbol file for the kernel the snapshot was taken from. other-symbols.zip contains symbol files I generated vor various other kernels but did not end up using, use at your own discretion.

This application is intended for informational purposes only and is not an operational product. The tool provides the capability to access, view and interact with satellite imagery, and shows the latest view of Earth as it appears from space.For additional imagery from NOAA's GOES East and GOES West satellites, please visit our Imagery and Data page or our cooperative institute partners at CIRA and CIMSS.This website should not be used to support operational observation, forecasting, emergency, or disaster mitigation operations, either public or private. In addition, we do not provide weather forecasts on this site — that is the mission of the National Weather Service. Please contact them for any forecast questions or issues. Using the Maps​What does the Layering Options icon mean?The Layering Options widget provides a list of operational layers and their symbols, and allows you to turn individual layers on and off. 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You may use this material for educational or informational purposes, including photo collections, textbooks, public exhibits, computer graphical simulations and internet web pages. This general permission extends to personal web pages. About this satellite imageryWhat am I looking at in these maps?In this map you are seeing the past 24 hours (updated approximately every 10 minutes) of the Western Hemisphere and Pacific Ocean, as seen by the NOAA GOES East (GOES-16) and GOES West (GOES-18) satellites. In this map you can also view four different ‘layers’. The views show ‘GeoColor’, ‘infrared’, and ‘water vapor’.This maps shows the coverage area of the GOES East and GOES West satellites. GOES East, which orbits the Earth from 75.2 degrees west longitude, provides a continuous view of the Western Hemisphere, from the West Coast of Africa to North and South America. GOES West, which orbits the Earth at 137.2 degrees west longitude, sees western North and South America and the central and eastern Pacific Ocean all the way to New Zealand.What does the GOES GeoColor imagery show?The 'Merged GeoColor’ map shows the coverage area of the GOES East and GOES West satellites and includes the entire Western Hemisphere and most of the Pacific Ocean. This imagery uses a combination of visible and infrared channels and is updated approximately every 15 minutes in real time. GeoColor imagery approximates how the human eye would see Earth from space during daylight hours, and is created by combining several of the spectral channels from the Advanced Baseline Imager (ABI) – the primary instrument on the GOES satellites. The wavelengths of reflected sunlight from the red and blue portions of the spectrum are merged with a simulated green wavelength component, creating RGB (red-green-blue) imagery. At night, infrared imagery shows high clouds as white and low clouds and fog as light blue. The static city lights background basemap is derived from a single composite image from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day Night Band. For example, temporary power outages will not be visible. Learn more.What does the GOES infrared map show?The 'GOES infrared' map displays heat radiating off of clouds and the surface of the Earth and is updated every 15 minutes in near real time. Higher clouds colorized in orange often correspond to more active weather systems. This infrared band is one of 12 channels on the Advanced Baseline Imager, the primary instrument on both the GOES East and West satellites. on the GOES the multiple GOES East ABI sensor’s infrared bands, and is updated every 15 minutes in real time. Infrared satellite imagery can be "colorized" or "color-enhanced" to bring out details in cloud patterns. These color enhancements are useful to meteorologists because they signify “brightness temperatures,” which are approximately the temperature of the radiating body, whether it be a cloud or the Earth’s surface. In this imagery, yellow and orange areas signify taller/colder clouds, which often correlate with more active weather systems. Blue areas are usually “clear sky,” while pale white areas typically indicate low-level clouds. During a hurricane, cloud top temperatures will be higher (and colder), and therefore appear dark red. This imagery is derived from band #13 on the GOES East and GOES West Advanced Baseline Imager.How does infrared satellite imagery work?The infrared (IR) band detects radiation that is emitted by the Earth’s surface, atmosphere and clouds, in the “infrared window” portion of the spectrum. The radiation has a wavelength near 10.3 micrometers, and the term “window” means that it passes through the atmosphere with relatively little absorption by gases such as water vapor. It is useful for estimating the emitting temperature of the Earth’s surface and cloud tops. A major advantage of the IR band is that it can sense energy at night, so this imagery is available 24 hours a day.What do the colors on the infrared map represent?In this imagery, yellow and orange areas signify taller/colder clouds, which often correlate with more active weather systems. Blue areas are clear sky, while pale white areas indicate low-level clouds, or potentially frozen surfaces. Learn more about this weather imagery.What does the GOES water vapor map layer show?The GOES ‘water vapor’ map displays the concentration and location of clouds and water vapor in the atmosphere and shows data from both the GOES East and GOES West satellites. Imagery is updated approximately every 15 minutes in real time. Water vapor imagery, which is useful for determining locations of moisture and atmospheric circulations, is created using a wavelength of energy sensitive to the content of water vapor in the atmosphere. In this imagery, green-blue and white areas indicate the presence of high water vapor or moisture content, whereas dark orange and brown areas indicate little or no moisture present. This imagery is derived from band #10 on the GOES East and GOES West Advanced Baseline Imager.What do the colors on the water vapor map represent?In this imagery, green-blue and white areas indicate the presence of high water vapor or moisture content, whereas dark orange and brown areas indicate less moisture present. Learn more about this water vapor imagery.About the satellitesWhat are the GOES satellites?NOAA’s most sophisticated Geostationary Operational Environmental Satellites (GOES), known as the GOES-R Series, provide advanced imagery and atmospheric measurements of Earth’s Western Hemisphere, real-time mapping of lightning activity, and improved monitoring of solar activity and space weather.The first satellite in the series, GOES-R, now known as GOES-16, was launched in 2016 and is currently operational as NOAA’s GOES East satellite. In 2018, NOAA launched another satellite in the series, GOES-T, which joined GOES-16 in orbit as GOES-18. GOES-17 became operational as GOES West in January 2023.Together, GOES East and GOES West provide coverage of the Western Hemisphere and most of the Pacific Ocean, from the west coast of Africa all the way to New Zealand. Each satellite orbits the Earth from about 22,200 miles away.

[The SeizeIT1 dataset will no longer be shared upon request due to expiry of the ethical approval. You can get access to SeizeIT2 if you sign up to the challenge at https://biomedepi.github.io/seizure_detection_challenge/] This dataset is obtained during an ICON project (2017-2018) in collaboration with KU Leuven (ESAT-STADIUS), UZ Leuven, UCB, Byteflies and Pilipili. The goal of this project was to design a system using Behind the ear (bhE) EEG electrodes for monitoring the patient in a home environment. This way, a nice balance can be found between sufficient accuracy of seizure detection algorithms (because EEG is used) and wearability (bhe EEG is relatively subtle, similar to a hear-aid device). The dataset acquired in the hospital during presurgical evaluation. During such presurgical evaluation, neurologists try to see if a specific part of the brain is causing the seizures, and if so, if that part of the brain can be removed during surgery. During the presurgical evaluation, patients are monitored using the vEEG for multiple days (typically a week). Patients are however restricted to move within their room because of the wiring and video analysis. In this dataset, following data is available per patient: • Full 10-20 scalp EEG data of the patient during the presurgical evaluation. • Behind-the-ear data (2 sensors positioned behind each ear) • Single-lead ECG data (typically lead II) Seizures are annotated by the clinicians based on the gold standard vEEG system. These seizure annotations are also available in the dataset. In total 82 patients were recorded between 23/01/2017 and 26/10/2018. From those patients, 54 were recorded with the bhe channels. Forty-two of those patients had seizures during their presurgical evaluation, while for twelve patients no seizure has been recorded. The number of seizures per patient ranged from 1 to 22, with a median of 3 seizures per patient. The duration of the seizures, the time difference of seizure EEG onset and end, varied between 11 and 695 seconds with a median of 50 seconds. 89% of the seizures were Focal Impaired Awareness seizures. 91% of the seizures originated from the (fronto-) temporal lobe. In the folder ’Data’ the raw data in the form of .edf, are provided with annotations for all the patients. The annotations are provided in .tsv (tab separated values) files. For every seizure the first column represents the starting point (in seconds) of the seizure, the second one the end point of the seizure, the third one the type of the seizure, while in the last column extra information are provided. The extra information includes the origin of the seizure, the hemisphere and if the seizure can be noted from the behind the ear channels (bhe:1 in that case). In the header section of every file information concerning the dataset and the annotations used are included. For every subject and for every session (even if no seizure is present) two different sets of annotations are provided. The ”a1”set of annotations is the annotations as provided by the doctors. The ”a2” set of annotations are the annotations used in [2] for training of the algorithm. The annotations provided from the doctors were not always perfectly aligned with the typical rhythmic ictal pattern, hence in ”a2” a refinement of the start of each annotation was performed visually by an engineer. Furthermore, in the annotations of the doctor the end point of some seizures was missing (”none”) in the ”a2” subset of annotations each seizure was considered with a stable length of 10 seconds.

This data accompanies the Paper "How much demand side flexibility do we need? - Analyzing where to exploit flexibility in industrial processes".[0] The raw data which this data set is based on, is the HIPE dataset[1], which can be found at https://www.energystatusdata.kit.edu/hipe.php . In the accompanying publication, you can find an in-depth description of the data, how it was gathered, what types of machines were covered, etc. This data package contains: * The instances of the four test sets in [0]. These can be found in the subfolder "instances". The "PS_Nonuniform", "PS_Uniform", "PSG" and "OM" subfolders contain the 450 instances of each set, one instance per file. The file format is explained in the "file_format.{md, html, pdf}" files. * Information about our computational results as a SQLite3 database in the "results" subfolder. Information about the database structure can be found in the "db_structure.{md, html, pdf}" files. [0] Lukas Barth, Veit Hagenmeyer, Nicole Ludwig, and Dorothea Wagner. 2018. How much demand side flexibility do we need? Analyzing where to exploit flexibility in industrial processes. In Proceedings of ACM eEnergy Conference (eEnergy’18). ACM, New York, NY, USA, 20 pages. (to appear) [1] Simon Bischof, Holger Trittenbach, Michael Vollmer, Dominik Werle, Thomas Blank, and Klemens Böhm. 2018. HIPE – an Energy-Status-Data Set from Industrial Production. In Proceedings of ACM e-Energy (e-Energy ’18). ACM, New York, NY, USA, 5 pages. (to appear)

knmfi proposalID 2020-024029394 APT FIB

Raw data behind the publication with the title "Tribologically induced crystal rotation kinematics revealed by electron backscatter diffraction". Contains EBSD data, friction data, secondary electron microscopy images as well as optical topography data.

Raw Data behind publication "Waviness Affects Friction and Abrasive Wear"

Data can be used for InSAR processing or for seismological source inversion. Furthermore, 'results' data can be used for post-processing and the creation of plots and maps.

This data repository contains two folders: * 01 Equal Traffic Intensities – Raw data for the analysis of decomposition error in tandem queues with equal traffic intensities, * 02 Bottleneck Analyses – Raw data for the analysis of decomposition error in tandem queues with bottlenecks. The first folder contains a training data and a test data file. The second folder contains three files: * Data set with downstream bottleneck queues, * Data set with upstream bottleneck queues, * Data set with similar traffic intensities.

Raw data containing friction data, profilometry and transmission electron microscopy data

Raw data from tribological experiment, topography measurements and optical micrographs

Raw ACOM data, raw STEM and (HR)TEM images

Raw data from microscopy, nanoindantion, XRD and friction tests