This graph shows the perceived reliability of internet sources among news consumers in Italy in 2016, ranked by type of source. As of the survey period, search engines were trusted by 36.6 percent of respondents, slightly overrating newspapers apps or websites (36.4 percent). News platforms and websites were considered reliable by 31.9 percent of respondents.

Contents

data.tgz contains all data necessary for reproducing the analysis in the manuscript. After cloning the GitHub repository, extract the contents of this file into folder data. The archive contains the following subfolders:

dtox DTox results, one subfolder per seed

module_relevance.tsv: contains node importance scores, with the following columns:

(first, unnamed): compound identifier

remaining columns: node identifiers (UniProt and Reactome IDs)

test_labels.csv: predictions for the test set, with two columns:

truth: true label (0 or 1)

predicted: predicted label (decimal number between 0 and 1)

mskimpact_[cancer type]_[experiment] P-NET results using the MSK-IMPACT 2017 dataset, one subfolder per seed [cancer type] is one of bc (breast cancer), cc (colorectal cancer), nsclc (non-small cell lung cancer), or pc (prostate cancer) [experiment] is one of original (original setup) and shuffled (shuffled labels)

pnet_[experiment] P-NET results using the original (prostate cancer) dataset, one subfolder per seed [experiment] is one of deterministic (deterministic input data), original (original setup), and shuffled (shuffled labels)

node_importance.csv: contains node importance scores, with the following columns:

(first, unnamed): node name

coef: original node importance scores

coef_graph: indegree plus outdegree of node

coef_combined: adjusted node importance score (= coef / coef_graph if coef_graph > mean(coef_graph) + 5 sd(coef_graph) in the respective layer)

coef_combined_zscore: scaled coef_combined

coef_combined2: z(z(coef_graph) - z(coef))

layer: layer of the node

predictions_test.csv: predictions for the test set, with the following columns:

(first, unnamed): sample name

pred: predicted class (unfortunately, encoded by a double 1.0 or 0.0)

pred_scores: probability of the predicted class

y: true class (encoded as integer 1 or 0)

predictions_train.csv: predictions for the training set (same columns as above)

link_weights_[layer].csv: only in subfolder 234_20080808; matrices with edge weights

Changelog

v1.1.0 – 2023-06-28

added DTox results

added results of P-NET experiments with MSK-IMPACT 2017 dataset

v1.0.0 – 2023-03-22

initial release

Explore the historical Whois records related to reliable-networks.com (Domain). Get insights into ownership history and changes over time.

In the domain of question subjectivity classification, there exists a need for detailed datasets that can foster advancements in Automatic Subjective Question Answering (ASQA) systems. Addressing the prevailing research gaps, this paper introduces the Fine-Grained Question Subjectivity Dataset (FQSD), which comprises 10,000 questions. The dataset distinguishes between subjective and objective questions and offers additional categorizations such as Subjective-types (Target, Attitude, Reason, Yes/No, None) and Comparison-form (Single, Comparative). Annotation reliability was confirmed via robust evaluation techniques, yielding a Fleiss’s Kappa score of 0.76 and Pearson correlation values up to 0.80 among three annotators. We benchmarked FQSD against existing datasets such as (Yu, Zha, and Chua 2012), SubjQA (Bjerva 2020), and ConvEx-DS (Hernandez-Bocanegra 2021). Our dataset excelled in scale, linguistic diversity, and syntactic complexity, establishing a new standard for future research. We employed visual methodologies to provide a nuanced understanding of the dataset and its classes. Utilizing transformer-based models like BERT, XLNET, and RoBERTa for validation, RoBERTa achieved an outstanding F1-score of 97%, confirming the dataset’s efficacy for the advanced subjectivity classification task. Furthermore, we utilized Local Interpretable Model-agnostic Explanations (LIME) to elucidate model decision-making, ensuring transparent and reliable model predictions in subjectivity classification tasks.

According to a survey conducted by Opensurvey in July 2022 in South Korea, around 24 percent of respondents chose internet browsing as a reliable source of information about health functional products. Another major source was the people around them such as family, friends, or acquaintances.

This page provides the source code underlying the manuscript: Chicharro D, Andrzejak RG (2009): Reliable detection of directional couplings using rank statistics. Physical Review E, 80, 026217. If you use any of these resources, please make sure that you cite this reference. For more detailed information, please refer to https://www.upf.edu/web/ntsa/downloads

No description is available. Visit https://dataone.org/datasets/sha256%3A7fb7e41cd92db8de2099264e0f7aa8e042ad5a6adac357045ee981a300dcf938 for complete metadata about this dataset.

Leverage the most reliable and compliant mobile device location/foot traffic dataset on the market!

Veraset Movement (GPS Mobility Data) offers unparalleled insights into footfall traffic patterns across nearly four dozen countries in Africa.

Covering 46+ countries, Veraset's Mobility Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement.

Ideal for ad tech, planning, retail, and transportation logistics, Veraset's Movement data (Mobility data) helps shape strategy and make impactful data-driven decisions.

Veraset’s Africa Movement Panel includes the following countries: - algeria-DZ - angola-AO - benin-BJ - botswana-BW - burkina faso-BF - burundi-BI - cameroon-CM - central african republic-CF - chad-TD - comoros-KM - congo-brazzaville-CG - congo-kinshasa-CD - djibouti-DJ - egypt-EG - eritrea-ER - ethiopia-ET - gabon-GA - gambia-GM - ghana-GH - guinea-bissau-GW - kenya-KE - lesotho-LS - liberia-LR - libya-LY - madagascar-MG - malawi-MW - mali-ML - mauritius-MU - morocco-MA - mozambique-MZ - namibia-NA - nigeria-NG - rwanda-RW - senegal-SN - seychelles-SC - sierra leone-SL - somalia-SO - south africa-ZA - south sudan-SS - tanzania-TZ - togo-TG - tunisia-TN - uganda-UG - zambia-ZM - zimbabwe-ZW

Companies use Veraset's Mobility Data for: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

This dataset comprises 10,000 years of synthetic tropical cyclone tracks, meticulously generated using the Tropical Cyclone Wind Statistical Estimation Tool (TCWiSE) algorithm, as detailed in Nederhoff et al., 2021. Focused on the North Atlantic Ocean Basin, these tracks are constructed based on historical data and designed to mirror the present-day climate conditions. The dataset offers a comprehensive and detailed simulation of tropical cyclone activity, serving as a valuable resource for climate research and analysis in the context of current climate patterns.

Credit report of Jiaxing Reliable Import And Export contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

Reliable Carriers is a private company with an estimated 172 employees. In the US, the company has a notable market share in at least one industry: Long-Distance Specialized Freight Trucking, where they account for an estimated 0.1% of total industry revenue and are considered a Laggard because they display lower market share alongside slower profit and revenue growth than their peers.

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Explore the historical Whois records related to reliable-net.net (Domain). Get insights into ownership history and changes over time.

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from Indonesia were selected based on the following criterion: (a) Corn growers in East Java - Location: East Java (Kediri and Probolinggo) and Aceh
- Innovative (early adopter); Progressive (keen to learn about agronomy and pests; willing to try new technology); Loyal (loyal to technology that can help them)
- making of technical drain (having irrigation system)
- marketing network for corn: post-harvest access to market (generally they sell 80% of their harvest)
- mid-tier (sub-optimal CP/SE use)
- influenced by fellow farmers and retailers
- may need longer credit

(b) Rice growers in West and East Java - Location: West Java (Tasikmalaya), East Java (Kediri), Central Java (Blora, Cilacap, Kebumen), South Lampung
- The growers are progressive (keen to learn about agronomy and pests; willing to try new technology)
- Accustomed in using farming equipment and pesticide. (keen to learn about agronomy and pests; willing to try new technology) - A long rice cultivating experience in his area (lots of experience in cultivating rice)
- willing to move forward in order to increase his productivity (same as progressive)
- have a soil that broad enough for the upcoming project
- have influence in his group (ability to influence others) - mid-tier (sub-optimal CP/SE use)
- may need longer credit

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group) o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

Use of JSATS can generate a large volume of data. To manage and visualize the data, an integrated suite of science-based tools known as the Hydropower Biological Evaluation Toolset (HBET) can be used.

The Faint Source Survey (FSS) is the definitive Infrared Astronomical Satellite data set for faint point sources. The FSS was produced by point-source filtering the individual detector data streams and then coadding those data streams using a trimmed-average algorithm. The resulting images, or plates, give the best estimate from the IRAS survey data of the point source flux density at every surveyed point of the sky. The Faint Source Catalog (FSC) is a compilation of the sources extracted from the FSS plates that have met reasonable reliability requirements. Averaged over the whole catalog, the FSC is at least 98.5% reliable at 12 and 25 microns, and ~94% at 60 microns. For comparison, the IRAS Point Source Catalog (PSC) is >99.997% reliable, but the sensitivity of the FSC exceeds that of the PSC by about a factor of 2.5. The FSC contains data for 173,044 point sources in unconfused regions with flux densities typically above 0.2 Jy at 12, 25, and 60 microns, and above 1.0 Jy at 100 microns. The FSS plates are somewhat more sensitive but less reliable than the FSC; typically, only sources with SNR>5-6 in the plates are contained in the FSC. Sources with SNR>3 but which do not meet the reliability requirements of the FSC are catalogued in the Faint Source Reject File (FSR, Cat. II/275). The data products, the processing methods used to produce them, results of an analysis of these products, and cautionary notes are given in the Explanatory Supplement to the IRAS Faint Source Survey (see references in fsc.txt).

The statistics depicts the results of a survey conducted in October 2017 about most trusted media sources for news according to people in Japan. The survey revealed that around 74.9 percent of respondents considered TV as highly reliable. About 43 percent of respondents viewed newspapers as a trustworthy source for news.

Credit report of Reliable Radiators contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

Time Series Data Analysis for the paper "Less reliable media drive interest in anti-vaccine information": This file contains the time series analysis (ADF test for stationarity, fitting of VAR model, granger causality tests, IRF plots) on the final time-series data used in the paper. The VAR model uses the data at levels. We run this analysis for Antivaxx terms across different platforms, and media outlets. The google trends data variable was generated using google trends and is restricted to queries from the US. For further questions, please reach out to the authors at ss5910@columbia.edu.

This repository contains a replication package and experimental results for our study on Scalable and Reliable Multi-Dimensional Sensor Data Aggregation in Data-Streaming Architectures.

It features the presented implementations with Kafka Streams, tools for load generation and data collection, scripts for executing the presented evaluations as well as our raw results and script for analysis. A detailed description is given in the top-level README.md file.