This data release contains California Department of Water Resource borehole data that were regularized by the US Geological Survey. This dataset contains borehole lithologic data, and geospatial data of water wells in the Hat Creek basin California, located east of Mount Shasta in southern California. The borehole dataset is released as an excel table and includes (1) individual borehole location, and (2) downhole lithologic interval data derived from well drillers’ lithology logs.

Indonesia Exports: fob: OG: Gas Procurement: Gas data was reported at 4.300 USD mn in Mar 2019. This records an increase from the previous number of 3.000 USD mn for Feb 2019. Indonesia Exports: fob: OG: Gas Procurement: Gas data is updated monthly, averaging 2.550 USD mn from Jan 2015 (Median) to Mar 2019, with 42 observations. The data reached an all-time high of 19.100 USD mn in Apr 2018 and a record low of 0.100 USD mn in Feb 2016. Indonesia Exports: fob: OG: Gas Procurement: Gas data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.JAA001: Trade Statistics. Rest of 2015 figure will be available hand in hand with the on-going monthly update. Sisa angka tahun 2015 akan tersedia seiring dengan update bulanan yang sedang berlangsung.

These flight logs and simulation datafiles form the basis of the analysis of

[1] T. M. Blaha, E. J. J. Smeur, and B. D. W. Remes, “Control of Unknown Quadrotors from a Single Throw,” Jun. 17, 2024, arXiv.2406.11723. Accepted at IROS 2024

[2] T. M. Blaha, E. J. J. Smeur, B. D. W. Remes, and C. C. de Visser “Flying a Quadrotor with Unknown Actuators and Sensor Configuration,” 2024, Accepted at IMAV 2024

They primarily show a small multirotor(s) response to

1. being launched in the air to about 4 meters altitude (by a pre-programmed "catapult" flight-mode)

2. forgetting its internal controller parameters [1]. Also forgetting the IMU orientation with respect to the actuators [2].

3. being subjected to a sequence of motor commands according to [1]

4. autonomous recovery to a position setpoint after the missing parameters have been identified and suitable control parameters have been calculated

The simulation data was generated with <https://github.com/tudelft/indiflightSupport/tree/iros_imav_2024> (snapshot also at <http://doi.org/10.4121/a5fa60a7-805c-4e13-a65e-1c65454eaa53>), which also contains the tools to reproduce the figures in the papers above.

More information on the craft is available at <https://github.com/tudelft/indiflightSupport/wiki>

The Ancillary Data component of the Indicators of Coastal Water Quality Collection includes a 5 arc-minute (approximately 9 x 9 km at the equator) sequence grid, grid cell centroids that relate to the grid cells in the tabular "Indicators of Coastal Water Quality: Change in Chlorophyll-a Concentration 1998-2007" data set, and a country buffer data set that is divided by exclusive economic zones (EEZ). The data are produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

The Barcode of Life Data Systems (BOLD) is designed to support the generation and application of DNA barcode data, but it also provides a unique source of data with potential for many research uses. This paper explores the streamlining of BOLD specimen data to record species distributions – and its fast publication using the Biodiversity Data Journal (BDJ), and its authoring platform, the Pensoft Writing Tool (PWT). We selected a sample of 630 specimens and 10 species of a highly diverse group of parasitoid wasps (Hymenoptera: Braconidae, Microgastrinae) from the Nearctic region and used the information in BOLD to uncover a significant number of new records (of locality, provinces, territories and states). By converting specimen information (such as locality, collection date, collector, voucher depository) from the BOLD platform to the Excel template provided by the PWT, it is possible to quickly upload and generate long lists of "Material Examined" for papers discussing taxonomy, ecology and/or new distribution records of species. For the vast majority of publications including DNA barcodes, the generation and publication of ancillary data associated with the barcoded material is seldom highlighted and often disregarded, and the analysis of those data sets to uncover new distribution patterns of species has rarely been explored, even though many BOLD records represent new and/or significant discoveries. The introduction of journals specializing in – and streamlining – the release of these datasets, such as the BDJ, should facilitate thorough analysis of these records, as shown in this paper.

In January 2025, around ***** percent of Germany had 5G coverage. Only *** percent was a so-called dead zone, which is an area where there is no 2G, 4G, or 5G. The number of 5G base stations had increased significantly in recent years.

Pitch bearing, as the core component of wind turbines, brings the blades to the desired position by adjusting the aerodynamic angle. Due to the harsh working environment of wind turbines, the faults of pitch bearing may lead to the overall failure of wind turbine. However, obtaining the sufficient data of faults for pitch bearings under the actual operating environment is difficult and time-consuming. Thus, a precision-machining mechanical platform of proportionally-scaled pitch bearings was designed and built to simulate its actual operating characteristics. Based on it, the vibration and acoustic data of proportionally-scaled pitch bearings with 11 types of faults under three kinds of loads and two rotational speeds are collected and stored in the sequent data files. The faults, such as crack, wear and spalling, are artificially created on inner or/and outer ring raceways, as well as rolling body of proportionally-scaled pitch bearings in advance. More especially, single and compound fault data are provided in this dataset. Ultimately, this dataset provides high-quality data for the studies on fault diagnosis for pitch bearing in wind turbines and rolling bearings with low-speed and heavy loads. It is also employed to validate the effectiveness of newly developed fault diagnosis methods.

Percentage of emergency admissions to any hospital in England occurring within 30 days of the last, previous discharge from hospital after admission: indirectly standardised by age, sex, method of admission and diagnosis/procedure. The indicator is broken down into the following demographic groups for reporting: ● All years and female only, male only and both male and female (persons). ● <16 years and female only, male only and both male and female (persons). ● 16+ years and female only, male only and both male and female (persons) ● 16-74 years and female only, male only and both male and female (persons) ● 75+ years and female only, male only and both male and female (persons) Results for each of these groups are also split by the following geographical and demographic breakdowns: ● Local authority of residence. ● Region. ● Area classification. ● NHS and private providers. ● NHS England regions. ● Deprivation (Index of Multiple Deprivation (IMD) Quintiles, 2019). ● Sustainability and Transformation Partnerships (STP) & Integrated Care Boards (ICB) from 2016/17. ● Clinical Commissioning Groups (CCG) & sub-Integrated Care Boards (sub-ICB). All annual trends are indirectly standardised against 2013/14.

During the fourth quarter of 2024, data breaches exposed more than a million user data records in the United Kingdom (UK). The figure decreased significantly from nearly 41 million in the quarter prior. Overall, the time between the first quarter of 2022 and the fourth quarter of 2023, saw the lowest number of exposed user data accounts.

This dataset contains NMR and HPLC (high performance liquid chromotagraphy) data of all shown compounds. Data from collaborating groups can be found in a seperate data set. The data is structured according to figures and schemes in the research article.

France FR: Business Enterprise Researchers: Per Thousand Employment in Industry data was reported at 10.206 Per 1000 in 2021. This records an increase from the previous number of 10.156 Per 1000 for 2020. France FR: Business Enterprise Researchers: Per Thousand Employment in Industry data is updated yearly, averaging 4.928 Per 1000 from Dec 1981 (Median) to 2021, with 41 observations. The data reached an all-time high of 10.206 Per 1000 in 2021 and a record low of 2.088 Per 1000 in 1981. France FR: Business Enterprise Researchers: Per Thousand Employment in Industry data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s France – Table FR.OECD.MSTI: Number of Researchers and Personnel on Research and Development: OECD Member: Annual.

;

Definition of MSTI variables 'Value Added of Industry' and 'Industrial Employment':

R&D data are typically expressed as a percentage of GDP to allow cross-country comparisons. When compiling such indicators for the business enterprise sector, one may wish to exclude, from GDP measures, economic activities for which the Business R&D (BERD) is null or negligible by definition. By doing so, the adjusted denominator (GDP, or Value Added, excluding non-relevant industries) better correspond to the numerator (BERD) with which it is compared to.

The MSTI variable 'Value added in industry' is used to this end:

It is calculated as the total Gross Value Added (GVA) excluding 'real estate activities' (ISIC rev.4 68) where the 'imputed rent of owner-occupied dwellings', specific to the framework of the System of National Accounts, represents a significant share of total GVA and has no R&D counterpart. Moreover, the R&D performed by the community, social and personal services is mainly driven by R&D performers other than businesses.

Consequently, the following service industries are also excluded: ISIC rev.4 84 to 88 and 97 to 98. GVA data are presented at basic prices except for the People's Republic of China, Japan and New Zealand (expressed at producers' prices).In the same way, some indicators on R&D personnel in the business sector are expressed as a percentage of industrial employment. The latter corresponds to total employment excluding ISIC rev.4 68, 84 to 88 and 97 to 98.

GC skew denotes the relative excess of G nucleotides over C nucleotides on the leading versus the lagging replication strand of eubacteria. While the effect is small, typically around 2.5%, it is robust and pervasive. GC skew and the analogous TA skew are a localized deviation from Chargaff’s second parity rule, which states that G and C, and T and A occur with (mostly) equal frequency even within a strand.

Most bacteria also show the analogous TA skew. Different phyla show different kinds of skew and differing relations between TA and GC skew. This article introduces an open access database (https://skewdb.org) of GC and 10 other skews for over 28,000 chromosomes and plasmids. Further details like codon bias, strand bias, strand lengths and taxonomic data are also included.

The SkewDB database can be used to generate or verify hypotheses. Since the origins of both the second parity rule, as well as GC skew itself, are not yet satisfactorily explained, such a database may enhance our understanding of microbial DNA.

Methods The SkewDB analysis relies exclusively on the tens of thousands of FASTA and GFF3 files available through the NCBI download service, which covers both GenBank and RefSeq. The database includes bacteria, archaea and their plasmids. Furthermore, to ease analysis, the NCBI Taxonomy database is sourced and merged so output data can quickly be related to (super)phyla or specific species. No other data is used, which greatly simplifies processing. Data is read directly in the compressed format provided by NCBI.

All results are emitted as standard CSV files. In the first step of the analysis, for each organism the FASTA sequence and the GFF3 annotation file are parsed. Every chromosome in the FASTA file is traversed from beginning to end, while a running total is kept for cumulative GC and TA skew. In addition, within protein coding genes, such totals are also kept separately for these skews on the first, second and third codon position. Furthermore, separate totals are kept for regions which do not code for proteins. In addition, to enable strand bias measurements, a cumulative count is maintained of nucleotides that are part of a positive or negative sense gene. The counter is increased for positive sense nucleotides, decreased for negative sense nucleotides, and left alone for non-genic regions.

A separate counter is kept for non-genic nucleotides. Finally, G and C nucleotides are counted, regardless of if they are part of a gene or not. These running totals are emitted at 4096 nucleotide intervals, a resolution suitable for determining skews and shifts. In addition, one line summaries are stored for each chromosome. These line includes the RefSeq identifier of the chromosome, the full name mentioned in the FASTA file, plus counts of A, C, G and T nucleotides. Finally five levels of taxonomic data are stored.

Chromosomes and plasmids of fewer than 100 thousand nucleotides are ignored, as these are too noisy to model faithfully. Plasmids are clearly marked in the database, enabling researchers to focus on chromosomes if so desired. Fitting Once the genomes have been summarised at 4096-nucleotide resolution, the skews are fitted to a simple model. The fits are based on four parameters. Alpha1 and alpha2 denote the relative excess of G over C on the leading and lagging strands. If alpha1 is 0.046, this means that for every 1000 nucleotides on the leading strand, the cumulative count of G excess increases by 46. The third parameter is div and it describes how the chromosome is divided over leading and lagging strands. If this number is 0.557, the leading replication strand is modeled to make up 55.7% of the chromosome. The final parameter is shift (the dotted vertical line), and denotes the offset of the origin of replication compared to the DNA FASTA file. This parameter has no biological meaning of itself, and is an artifact of the DNA assembly process.

The goodness-of-fit number consists of the root mean squared error of the fit, divided by the absolute mean skew. This latter correction is made to not penalize good fits for bacteria showing significant skew. GC skew tends to be defined very strongly, and it is therefore used to pick the div and shift parameters of the DNA sequence, which are then kept as a fixed constraint for all the other skews, which might not be present as clearly. The fitting process itself is a downhill simplex method optimization over the three dimensions, seeded with the average observed skew over the whole genome, and assuming there is no shift, and that the leading and lagging strands are evenly distributed. The simplex optimization is tuned so that it takes sufficiently large steps so it can reach the optimum even if some initial assumptions are off.

Principal Investigator: Dr SIU Yee Man, Noel Institution/Think Tank: Hong Kong Baptist University Five years after completion of the research projects granted under the PPR Funding Scheme, quantitative empirical data generated from the research would be released to the public. Only research raw data (e.g. surveys) of completed projects that are provided in file format of comma-separated values (CSV) will be uploaded under the Open Data Plan. Raw data provided in formats other than CSV will only be uploaded onto the scheme’s webpage. PPR Funding Scheme’s webpage: https://www.cepu.gov.hk/en/PRFS/research_report.html Users of the data sets archived are required to acknowledge the research team and the Government. [Remarks: Parts of the data sets archived may contain Chinese/English version only.]

Techsalerator's Corporate Actions Dataset in Israel offers a comprehensive collection of data fields related to corporate actions, providing valuable insights for investors, traders, and financial institutions. This dataset includes crucial information about the various financial instruments of all 473 companies traded on the Tel-Aviv Stock Exchange (XTAE).

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Top 5 used data fields in the Corporate Actions Dataset for Israel:

• Dividend Declaration Date: The date on which a company's board of directors announces the dividend payout to its shareholders. This information is crucial for investors who rely on dividends as a source of income.

• Stock Split Ratio: The ratio by which a company's shares are split to increase liquidity and affordability. This field is essential for understanding changes in share structure.

• Merger Announcement Date: The date on which a company officially announces its intention to merge with another entity. This field is crucial for investors assessing the impact of potential mergers on their investments.

• Rights Issue Record Date: The date on which shareholders must be on the company's books to be eligible for participating in a rights issue. This data helps investors plan their participation in fundraising events.

• Bonus Issue Ex-Date: The date on which a company's shares start trading without the value of the bonus issue. This information is vital for investors to adjust their portfolios accordingly.

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Top 5 corporate actions in Israel:

Technology and Startups: Corporate actions in Israel's renowned technology sector, including mergers, acquisitions, and initial public offerings (IPOs), are crucial for the country's innovation ecosystem and its reputation as the "Startup Nation."

Healthcare and Life Sciences: Corporate actions related to pharmaceuticals, medical research, and healthcare startups contribute to Israel's reputation as a hub for medical innovation and cutting-edge research.

Cybersecurity and Defense Technology: Corporate actions in the cybersecurity and defense technology sectors reflect Israel's expertise in developing advanced cybersecurity solutions and defense systems.

Renewable Energy and Cleantech: Corporate actions related to renewable energy projects and cleantech initiatives align with Israel's efforts to develop sustainable energy sources and address environmental challenges.

Financial Services and Fintech: Corporate actions involving financial technology (fintech) startups, digital payment solutions, and blockchain technology contribute to Israel's financial services sector's modernization.

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Top 5 financial instruments with corporate action Data in Israel

Israel Stock Exchange (ISE) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Israel Stock Exchange. This index would provide insights into the performance of the Israeli stock market.

Israel Stock Exchange (ISE) Foreign Company Index: The index that tracks the performance of foreign companies listed on the Israel Stock Exchange, if foreign listings were present. This index would give an overview of foreign business involvement in Israel.

SuperMart Israel: An Israel-based supermarket chain with operations in multiple regions. SuperMart focuses on providing essential products to local communities and contributing to the retail sector's growth.

FinanceIsrael: A financial services provider in Israel with a focus on promoting financial inclusion and access to banking services, particularly among underserved communities.

AgriTech Israel: A company dedicated to advancing agricultural technology in Israel, focusing on optimizing crop yields and improving food security to support the country's agricultural sector.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Israel, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Dividend Declaration Date Stock Split Ratio Merger Announcement Date Rights Issue Record Date Bonus Issue Ex-Date Stock Buyback Date Spin-Off Announcement Date Dividend Record Date Merger Effective Date Rights Issue Subscription Price ‍

Q&A:

How much does the Corporate Actions Dataset cost in Israel?

The cost of the Corporate Actions Dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

How complete is the Corporate Actions Dataset coverage in Israel ?

Techsalerator provides comprehensive coverage of Corporate Actions Data for various companies and...

Objectives: To develop and pilot a tool to measure and improve pharmaceutical companies’ clinical trial data sharing policies and practices. Design: Cross sectional descriptive analysis. Setting: Large pharmaceutical companies with novel drugs approved by the US Food and Drug Administration in 2015. Data sources: Data sharing measures were adapted from 10 prominent data sharing guidelines from expert bodies and refined through a multi-stakeholder deliberative process engaging patients, industry, academics, regulators, and others. Data sharing practices and policies were assessed using data from ClinicalTrials.gov, Drugs@FDA, corporate websites, data sharing platforms and registries (eg, the Yale Open Data Access (YODA) Project and Clinical Study Data Request (CSDR)), and personal communication with drug companies. Main outcome measures: Company level, multicomponent measure of accessibility of participant level clinical trial data (eg, analysis ready dataset and metadata); drug and trial level measures of registration, results reporting, and publication; company level overall transparency rankings; and feasibility of the measures and ranking tool to improve company data sharing policies and practices. Results: Only 25% of large pharmaceutical companies fully met the data sharing measure. The median company data sharing score was 63% (interquartile range 58-85%). Given feedback and a chance to improve their policies to meet this measure, three companies made amendments, raising the percentage of companies in full compliance to 33% and the median company data sharing score to 80% (73-100%). The most common reasons companies did not initially satisfy the data sharing measure were failure to share data by the specified deadline (75%) and failure to report the number and outcome of their data requests. Across new drug applications, a median of 100% (interquartile range 91-100%) of trials in patients were registered, 65% (36-96%) reported results, 45% (30-84%) were published, and 95% (69-100%) were publicly available in some form by six months after FDA drug approval. When examining results on the drug level, less than half (42%) of reviewed drugs had results for all their new drug applications trials in patients publicly available in some form by six months after FDA approval. Conclusions: It was feasible to develop a tool to measure data sharing policies and practices among large companies and have an impact in improving company practices. Among large companies, 25% made participant level trial data accessible to external investigators for new drug approvals in accordance with the current study’s measures; this proportion improved to 33% after applying the ranking tool. Other measures of trial transparency were higher. Some companies, however, have substantial room for improvement on transparency and data sharing of clinical trials.

description: This dataset is one of eight datasets produced by this study. Four of the datasets predict the probability of detecting atrazine and(or) desethyl-atrazine (a breakdown product of atrazine) in ground water in Colorado; the other four predict the probability of detecting elevated concentrations of nitrate in ground water in Colorado. The four datasets that predict the probability of atrazine and(or) desethyl-atrazine (atrazine/DEA) are differentiated by whether or not they incorporated atrazine use and whether or not they incorporated hydrogeomorphic regions. The four datasets that predict the probability of elevated concentrations of nitrate are differentiated by whether or not they incorporated fertilizer use and whether or not they incorporated hydrogeomorphic regions. Each of the eight datasets has its own unique strengths and weaknesses. The user is cautioned to read Rupert (2003, Probability of detecting atrazine/desethyl-atrazine and elevated concentrations of nitrate in ground water in Colorado: U.S. Geological Survey Water-Resources Investigations Report 02-4269, 35 p., http://water.usgs.gov/pubs/wri/wri02-4269/) to determine if he(she) is using the most appropriate dataset for his(her) particular needs. This dataset specifically predicts the probability of detecting elevated concentrations of nitrate in ground water in Colorado with hydrogeomorphic regions and fertilizer use included. The following text was extracted from Rupert (2003). Draft Federal regulations may require that each State develop a State Pesticide Management Plan for the herbicides atrazine, alachlor, metolachlor, and simazine. Maps were developed that the State of Colorado could use to predict the probability of detecting atrazine/DEA in ground water in Colorado. These maps can be incorporated into the State Pesticide Management Plan and can help provide a sound hydrogeologic basis for atrazine management in Colorado. Maps showing the probability of detecting elevated nitrite plus nitrate as nitrogen (nitrate) concentrations in ground water in Colorado also were developed because nitrate is a contaminant of concern in many areas of Colorado. Maps showing the probability of detecting atrazine/DEA at or greater than concentrations of 0.1 microgram per liter and nitrate concentrations in ground water greater than 5 milligrams per liter were developed as follows: (1) Ground-water quality data were overlaid with anthropogenic and hydrogeologic data by using a geographic information system (GIS) to produce a dataset in which each well had corresponding data on atrazine use, fertilizer use, geology, hydrogeomorphic regions, land cover, precipitation, soils, and well construction. These data then were downloaded to a statistical software package for analysis by logistic regression. (2) Relations were observed between ground-water quality and the percentage of land-cover categories within circular regions (buffers) around wells. Several buffer sizes were evaluated; the buffer size that provided the strongest relation was selected for use in the logistic regression models. (3) Relations between concentrations of atrazine/DEA and nitrate in ground water and atrazine use, fertilizer use, geology, hydrogeomorphic regions, land cover, precipitation, soils, and well-construction data were evaluated, and several preliminary multivariate models with various combinations of independent variables were constructed. (4) The multivariate models that best predicted the presence of atrazine/DEA and elevated concentrations of nitrate in ground water were selected. (5) The accuracy of the multivariate models was confirmed by validating the models with an independent set of ground-water quality data. (6) The multivariate models were entered into a geographic information system and the probability GRIDS were constructed.; abstract: This dataset is one of eight datasets produced by this study. Four of the datasets predict the probability of detecting atrazine and(or) desethyl-atrazine (a breakdown product of atrazine) in ground water in Colorado; the other four predict the probability of detecting elevated concentrations of nitrate in ground water in Colorado. The four datasets that predict the probability of atrazine and(or) desethyl-atrazine (atrazine/DEA) are differentiated by whether or not they incorporated atrazine use and whether or not they incorporated hydrogeomorphic regions. The four datasets that predict the probability of elevated concentrations of nitrate are differentiated by whether or not they incorporated fertilizer use and whether or not they incorporated hydrogeomorphic regions. Each of the eight datasets has its own unique strengths and weaknesses. The user is cautioned to read Rupert (2003, Probability of detecting atrazine/desethyl-atrazine and elevated concentrations of nitrate in ground water in Colorado: U.S. Geological Survey Water-Resources Investigations Report 02-4269, 35 p., http://water.usgs.gov/pubs/wri/wri02-4269/) to determine if he(she) is using the most appropriate dataset for his(her) particular needs. This dataset specifically predicts the probability of detecting elevated concentrations of nitrate in ground water in Colorado with hydrogeomorphic regions and fertilizer use included. The following text was extracted from Rupert (2003). Draft Federal regulations may require that each State develop a State Pesticide Management Plan for the herbicides atrazine, alachlor, metolachlor, and simazine. Maps were developed that the State of Colorado could use to predict the probability of detecting atrazine/DEA in ground water in Colorado. These maps can be incorporated into the State Pesticide Management Plan and can help provide a sound hydrogeologic basis for atrazine management in Colorado. Maps showing the probability of detecting elevated nitrite plus nitrate as nitrogen (nitrate) concentrations in ground water in Colorado also were developed because nitrate is a contaminant of concern in many areas of Colorado. Maps showing the probability of detecting atrazine/DEA at or greater than concentrations of 0.1 microgram per liter and nitrate concentrations in ground water greater than 5 milligrams per liter were developed as follows: (1) Ground-water quality data were overlaid with anthropogenic and hydrogeologic data by using a geographic information system (GIS) to produce a dataset in which each well had corresponding data on atrazine use, fertilizer use, geology, hydrogeomorphic regions, land cover, precipitation, soils, and well construction. These data then were downloaded to a statistical software package for analysis by logistic regression. (2) Relations were observed between ground-water quality and the percentage of land-cover categories within circular regions (buffers) around wells. Several buffer sizes were evaluated; the buffer size that provided the strongest relation was selected for use in the logistic regression models. (3) Relations between concentrations of atrazine/DEA and nitrate in ground water and atrazine use, fertilizer use, geology, hydrogeomorphic regions, land cover, precipitation, soils, and well-construction data were evaluated, and several preliminary multivariate models with various combinations of independent variables were constructed. (4) The multivariate models that best predicted the presence of atrazine/DEA and elevated concentrations of nitrate in ground water were selected. (5) The accuracy of the multivariate models was confirmed by validating the models with an independent set of ground-water quality data. (6) The multivariate models were entered into a geographic information system and the probability GRIDS were constructed.

139 Global import shipment records of Catalyst Bed with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Key information about India Household Debt: % of GDP

• India household debt accounted for 17.1 % of the country's Nominal GDP in Mar 2024, compared with the ratio of 14.9 % in the previous year.
• India household debt to GDP ratio is updated yearly, available from Mar 1998 to Mar 2024.
• The data reached an all-time high of 17.1 % in Mar 2024 and a record low of 2.2 % in Mar 1999.

CEIC calculates annual Household Debt as % of Nominal GDP from annual Household Debt and annual Nominal GDP. The Reserve Bank of India provides Household Debt in local currency. Ministry of Statistics and Programme Implementation provides Nominal GDP in local currency based on SNA 2008, at 2011-2012 prices. Loans are used due to the lack of Flow of Funds statistics. Household Debt includes Consumer Durables, Housing and Rest of Personal Loans. Household Debt as % of Nominal GDP is in annual frequency, ending in March of each year.


Related information about India Household Debt: % of GDP

• In the latest reports, India Household Debt reached 617.3 USD bn in Mar 2024.
• Money Supply M2 in India increased 7.9 % YoY in Nov 2024.
• India Foreign Exchange Reserves was measured at 547.5 USD bn in Dec 2024.
• The Foreign Exchange Reserves equaled 9.2 Months of Import in May 2023.
• India Domestic Credit reached 3,168.1 USD bn in Dec 2024, representing an increased of 11.1 % YoY.
• The country's Non Performing Loans Ratio stood at 2.8 % in Mar 2024, compared with the ratio of 3.9 % in the previous year.

This is the documentation of the tomographic X-ray data of a dynamic cross phantom made available at http://www.fips.fi/dataset.php. The data can be freely used for scientific purposes with appropriate references to the data and to this document in http://arxiv.org/. The data set consists of (1) the X-ray sinogram with 16 or 30 time frames (depending on resolution) of 2D slices of the cross phantom, made by crossing aluminum and graphite sticks in melted candle wax and (2) the corresponding static and dynamic measurement matrices modeling the linear operation of the X-ray transform. Each of these sinograms was obtained from a measured 360-projection fan-beam sinogram by down-sampling and taking logarithms. The original (measured) sinogram is also provided in its original form and resolution.