As of December 2024, global Google searches using the query "Wiki" stood at 87 percentage points, so far the lowest interest rate despite a relatively stable margin throughout the analyzed period. Meanwhile, searches for the full Wikipedia query were slightly more popular during the analyzed period.

This file contains the National Statistics Postcode Lookup (NSPL) for the United Kingdom as at November 2024 in Comma Separated Variable (CSV) and ASCII text (TXT) formats. To download the zip file click the Download button. The NSPL relates both current and terminated postcodes to a range of current statutory geographies via ‘best-fit’ allocation from the 2021 Census Output Areas (national parks and Workplace Zones are exempt from ‘best-fit’ and use ‘exact-fit’ allocations) for England, Wales, Scotland and Northern Ireland. It supports the production of area-based statistics from postcoded data. The NSPL is produced by ONS Geography, who provide geographic support to the Office for National Statistics (ONS) and geographic services used by other organisations. The NSPL is issued quarterly. (File size - 191 MB).N.B. From the next release (February 2025) this will be known simply as the National Statistics Postcode Lookup (NSPL).[10/12/2024: Updated to correct county codes for all UAs in England to pseudo code E99999999.]

This file contains the National Statistics UPRN Lookup (NSUL) for Great Britain as at July 2023. The NSUL relates the Unique Property Reference Number (UPRN) for each GB address from AddressBase® Epoch 102 to a range of current statutory administrative, electoral, health and other statistical geographies via 'best-fit' allocation from 2021 Census output areas (National Parks and Workplace Zones are exempt from 'best-fit' and use 'exact-fit' allocations). The NSUL is produced by ONS Geography, who provide geographic support to the Office for National Statistics (ONS) and geographic services used by other organisations. The NSUL is issued every 6 weeks and is designed to complement the Ordnance Survey AddressBase® product. For further technical information about this file, please refer to the User Guide document contained within the downloadable zip file. Please note that this product contains Royal Mail, Gridlink, Ordnance Survey and ONS Intellectual Property Rights. (File Size – 462 MB)

According to a February 2023 survey, around 28 percent of adults in the United States stated they would distrust search results generated by an algorithm more or a great deal more. Only 18 percent of these respondents would trust these results more if they were generated by this kind of application, while 39 percent didn't change their perception in such cases. Trust in search results was similar levels if those were generated by an artificial intelligence.

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

We present Qbias, two novel datasets that promote the investigation of bias in online news search as described in

Fabian Haak and Philipp Schaer. 2023. 𝑄𝑏𝑖𝑎𝑠 - A Dataset on Media Bias in Search Queries and Query Suggestions. In Proceedings of ACM Web Science Conference (WebSci’23). ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3578503.3583628.

Dataset 1: AllSides Balanced News Dataset (allsides_balanced_news_headlines-texts.csv)

The dataset contains 21,747 news articles collected from AllSides balanced news headline roundups in November 2022 as presented in our publication. The AllSides balanced news feature three expert-selected U.S. news articles from sources of different political views (left, right, center), often featuring spin bias, and slant other forms of non-neutral reporting on political news. All articles are tagged with a bias label by four expert annotators based on the expressed political partisanship, left, right, or neutral. The AllSides balanced news aims to offer multiple political perspectives on important news stories, educate users on biases, and provide multiple viewpoints. Collected data further includes headlines, dates, news texts, topic tags (e.g., "Republican party", "coronavirus", "federal jobs"), and the publishing news outlet. We also include AllSides' neutral description of the topic of the articles. Overall, the dataset contains 10,273 articles tagged as left, 7,222 as right, and 4,252 as center.

To provide easier access to the most recent and complete version of the dataset for future research, we provide a scraping tool and a regularly updated version of the dataset at https://github.com/irgroup/Qbias. The repository also contains regularly updated more recent versions of the dataset with additional tags (such as the URL to the article). We chose to publish the version used for fine-tuning the models on Zenodo to enable the reproduction of the results of our study.

Dataset 2: Search Query Suggestions (suggestions.csv)

The second dataset we provide consists of 671,669 search query suggestions for root queries based on tags of the AllSides biased news dataset. We collected search query suggestions from Google and Bing for the 1,431 topic tags, that have been used for tagging AllSides news at least five times, approximately half of the total number of topics. The topic tags include names, a wide range of political terms, agendas, and topics (e.g., "communism", "libertarian party", "same-sex marriage"), cultural and religious terms (e.g., "Ramadan", "pope Francis"), locations and other news-relevant terms. On average, the dataset contains 469 search queries for each topic. In total, 318,185 suggestions have been retrieved from Google and 353,484 from Bing.

The file contains a "root_term" column based on the AllSides topic tags. The "query_input" column contains the search term submitted to the search engine ("search_engine"). "query_suggestion" and "rank" represents the search query suggestions at the respective positions returned by the search engines at the given time of search "datetime". We scraped our data from a US server saved in "location".

We retrieved ten search query suggestions provided by the Google and Bing search autocomplete systems for the input of each of these root queries, without performing a search. Furthermore, we extended the root queries by the letters a to z (e.g., "democrats" (root term) >> "democrats a" (query input) >> "democrats and recession" (query suggestion)) to simulate a user's input during information search and generate a total of up to 270 query suggestions per topic and search engine. The dataset we provide contains columns for root term, query input, and query suggestion for each suggested query. The location from which the search is performed is the location of the Google servers running Colab, in our case Iowa in the United States of America, which is added to the dataset.

AllSides Scraper

At https://github.com/irgroup/Qbias, we provide a scraping tool, that allows for the automatic retrieval of all available articles at the AllSides balanced news headlines.

We want to provide an easy means of retrieving the news and all corresponding information. For many tasks it is relevant to have the most recent documents available. Thus, we provide this Python-based scraper, that scrapes all available AllSides news articles and gathers available information. By providing the scraper we facilitate access to a recent version of the dataset for other researchers.

Uncover historical ownership history and changes over time by performing a reverse Whois lookup for the company ons.

This package contains data on five text analysis types (term extraction, contract analysis, topic modeling, network mapping), based on the survey data where researchers selected research output that are related to the 17 Sustainable Development Goals (SDGs). This is used as input to improve the current SDG classification model v4.0 to v5.0

Sustainable Development Goals are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This Aurora SDG dashboard is the initial outcome as proof of practice.)

The initiative started from the Aurora Universities Network in 2017, in the working group "Societal Impact and Relevance of Research", to investigate and to make visible 1. what research is done that are relevant to topics or challenges that live in society (for the proof of practice this has been scoped down to the SDGs), and 2. what the effect or impact is of implementing those research outcomes to those societal challenges (this also have been scoped down to research output being cited in policy documents from national and local governments an NGO's).

Context of this dataset | classification model improvement workflow

The classification model we have used are 17 different search queries on the Scopus database.

SDG search queries version 4.0 (SQv4) have been created, Published here:

Search Queries for "Mapping Research Output to the Sustainable Development Goals (SDGs)" v4.0 by Aurora Universities Network (AUR) doi:10.5281/zenodo.3817443

A survey has been distributed to senior researchers to test the robustness of SQv4. Published here:

Survey data of "Mapping Research output to the Sustainable Development Goals SDGs" by Aurora Universities Network (AUR) doi:10.5281/zenodo.3798385

This text analysis has been made as one of the inputs to improve the classification model. Published here:

Text Analyses of Survey Data on "Mapping Research Output to the Sustainable Development Goals SDGs" by Aurora Universities Network (AUR) doi:10.5281/zenodo.3832090

Improved SDG search queries version 5.0 (SQv5) have been created, Published here:

Search Queries for "Mapping Research Output to the Sustainable Development Goals (SDGs)" v5.0 by Aurora Universities Network (AUR) doi:10.5281/zenodo.3817445

Methods used to do the text analysis

Term Extraction: after text normalisation (stemming, etc) we extracted 2 terms in bigrams and trigrams that co-occurred the most per document, in the title, abstract and keyword

Contrast analysis: the co-occurring terms in publications (title, abstract, keywords), of the papers that respondents have indicated relate to this SDG (y-axis: True), and that have been rejected (x-axis: False). In the top left you'll see term co-occurrences that a clearly relate to this SDG. The bottom-right are terms that are appear in papers that have been rejected for this SDG. The top-right terms appear frequently in both and cannot be used to discriminate between the two groups.

Network map: This diagram shows the cluster-network of terms co-occurring in the publications related to this SDG, selected by the respondents (accepted publications only).

Topic model: This diagram shows the topics, and the related terms that make up that topic. The number of topics is related to the number of of targets of this SDG.

Contingency matrix: This diagram shows the top 10 of co-occurring terms that correlate the most.

Software used to do the text analyses

CorTexT: The CorTexT Platform is the digital platform of LISIS Unit and a project launched and sustained by IFRIS and INRAE. This platform aims at empowering open research and studies in humanities about the dynamic of science, technology, innovation and knowledge production.

Resource with interactive visualisations

Based on the text analysis data we have created a website that puts all the SDG interactive diagrams together. For you to scrall through. https://sites.google.com/vu.nl/sdg-survey-analysis-results/

Data set content

In the dataset root you'll find the following folders and files:

/sdg01-17/

This contains the text analysis for all the individual SDG surveys.

/methods/

This contains the step-by-step explanations of the text analysis methods using Cortext.

/images/

images of the results used in this README.md.

LICENSE.md

terms and conditions for reusing this data.

README.md

description of the dataset; each subfolders contains a README.md file to futher describe the content of each sub-folder.

Inside an /sdg01-17/-folder you'll find the following:

This contains the step-by-step explanations of the text analysis methods using Cortext.

/sdg01-17/sdg04-sdg-survey-selected-publications-combined.db

his contains the title, abstract, keywords, fo the publications in the survey, including the and accept or rejection status and the number of respondents

/sdg01-17/sdg04-sdg-survey-selected-publications-combined-accepted-accepted-custom-filtered.db

same as above, but only the accepted papers

/sdg01-17/extracted-terms-list-top1000.csv

the aggregated list of co-occuring terms (bigrams and trigrams) extracted per paper.

/sdg01-17/contrast-analysis/

This contains the data and visualisation of the terms appearing in papers that have been accepted (true) and rejected (false) to be relating to this SDG.

/sdg01-17/topic-modelling/

This contains the data and visualisation of the terms clustered in the same number of topics as there are 'targets' within that SDG.

/sdg01-17/network-mapping/

This contains the data and visualisation of the terms clustered in co-occuring proximation of appearance in papers

/sdg01-17/contingency-matrix/

This contains the data and visualisation of the top 10 terms co-occuring

note: the .csv files are actually tab-separated.

Contribute and improve the SDG Search Queries

We welcome you to join the Github community and to fork, branch, improve and make a pull request to add your improvements to the new version of the SDG queries. https://github.com/Aurora-Network-Global/sdg-queries

It is always a struggle to find suitable datasets with which to teach, especially across domain expertise. There are many packages that have data, but finding them and knowing what is in them is a struggle due to inadequate documentation. Here we have compiled a search-able database of dataset metadata taken from R packages on CRAN.

See https://ropenscilabs.github.io/data-packages/

In 2021, the most popular online tool used to look for a job in Poland were portals with job offers. Advertisements posted on them were, for many Poles, the primary source when searching for job offers online.

This dataset consists of the search documentation + a search narrative for a review article on social enterprises, digital affordances and sustainability. Contains strategies for the databases Business Source Elite, Scopus and Web of Science. See the readme.txt for further documentation.

Progress Needed on Identifying Expenditures, Building and Utilizing a Data Infrastructure, and Reducing Duplicative Efforts The federal government collects, maintains, and uses geospatial information—data linked to specific geographic locations—to help support varied missions, including national security and natural resources conservation. To coordinate geospatial activities, in 1994 the President issued an executive order to develop a National Spatial Data Infrastructure—a framework for coordination that includes standards, data themes, and a clearinghouse. GAO was asked to review federal and state coordination of geospatial data. GAO’s objectives were to (1) describe the geospatial data that selected federal agencies and states use and how much is spent on geospatial data; (2) assess progress in establishing the National Spatial Data Infrastructure; and (3) determine whether selected federal agencies and states invest in duplicative geospatial data. To do so, GAO identified federal and state uses of geospatial data; evaluated available cost data from 2013 to 2015; assessed FGDC’s and selected agencies’ efforts to establish the infrastructure; and analyzed federal and state datasets to identify duplication. What GAO Found Federal agencies and state governments use a variety of geospatial datasets to support their missions. For example, after Hurricane Sandy in 2012, the Federal Emergency Management Agency used geospatial data to identify 44,000 households that were damaged and inaccessible and reported that, as a result, it was able to provide expedited assistance to area residents. Federal agencies report spending billions of dollars on geospatial investments; however, the estimates are understated because agencies do not always track geospatial investments. For example, these estimates do not include billions of dollars spent on earth-observing satellites that produce volumes of geospatial data. The Federal Geographic Data Committee (FGDC) and the Office of Management and Budget (OMB) have started an initiative to have agencies identify and report annually on geospatial-related investments as part of the fiscal year 2017 budget process. FGDC and selected federal agencies have made progress in implementing their responsibilities for the National Spatial Data Infrastructure as outlined in OMB guidance; however, critical items remain incomplete. For example, the committee established a clearinghouse for records on geospatial data, but the clearinghouse lacks an effective search capability and performance monitoring. FGDC also initiated plans and activities for coordinating with state governments on the collection of geospatial data; however, state officials GAO contacted are generally not satisfied with the committee’s efforts to coordinate with them. Among other reasons, they feel that the committee is focused on a federal perspective rather than a national one, and that state recommendations are often ignored. In addition, selected agencies have made limited progress in their own strategic planning efforts and in using the clearinghouse to register their data to ensure they do not invest in duplicative data. For example, 8 of the committee’s 32 member agencies have begun to register their data on the clearinghouse, and they have registered 59 percent of the geospatial data they deemed critical. Part of the reason that agencies are not fulfilling their responsibilities is that OMB has not made it a priority to oversee these efforts. Until OMB ensures that FGDC and federal agencies fully implement their responsibilities, the vision of improving the coordination of geospatial information and reducing duplicative investments will not be fully realized. OMB guidance calls for agencies to eliminate duplication, avoid redundant expenditures, and improve the efficiency and effectiveness of the sharing and dissemination of geospatial data. However, some data are collected multiple times by federal, state, and local entities, resulting in duplication in effort and resources. A new initiative to create a national address database could potentially result in significant savings for federal, state, and local governments. However, agencies face challenges in effectively coordinating address data collection efforts, including statutory restrictions on sharing certain federal address data. Until there is effective coordination across the National Spatial Data Infrastructure, there will continue to be duplicative efforts to obtain and maintain these data at every level of government.https://www.gao.gov/assets/d15193.pdfWhat GAO Recommends GAO suggests that Congress consider assessing statutory limitations on address data to foster progress toward a national address database. GAO also recommends that OMB improve its oversight of FGDC and federal agency initiatives, and that FGDC and selected agencies fully implement initiatives. The agencies generally agreed with the recommendations and identified plans to implement them.

The Data on Statistical Capacity website provides information on various aspects of national statistical systems of developing countries, including a country-level statistical capacity indicator.

The Statistical Capacity Index (SCI) has been replaced by the Statistical Performance Indicators (SPI), which provide a more comprehensive framework for measuring the performance of national statistical systems. For further details, please visit https://www.worldbank.org/en/programs/statistical-performance-indicators/about-spi#4 to learn about the differences between the SCI and its successor.

This is a lookup file between local authority districts and regions in England as at 31 December 2017. (File Size - 56 KB)Field Names - LAD17CD, LAD17NM, RGN17CD, RGN17NMField Types - Text, Text, Text, TextField Lengths - 9, 28, 9, 24REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LAD17_RGN17_EN_LU_c5e0d2988ca24f54ae16cceb6f101f19/FeatureServer

We conducted two literature searches to help guide the development of a conceptual model of a barrier island and shoreline system in response to cumulative effects of restoration projects. The first search targeted examples of cumulative effects assessments and/or existing conceptual models from which a system-specific conceptual model can be built. The second search targeted the identification of barrier island and shoreline environmental system components, drivers and stressors. There are two data sheets in this dataset; one set of records from each literature search. Each spreadsheet includes record information pulled directly from the Web of Science searches, such as title, authors, abstract, and publication source. We also screened the records for relevance to our needs and additional information contained in the titles and abstracts, including environmental system components (e.g. structure or function), specific drivers and stressors, and more.

Magnetic resonance imaging (MRI) is an invaluable imaging technique used in medicine, generating images of structures inside the body. The images produced are used to examine almost any part of the body and are essential in diagnosing conditions, planning treatments and assessing treatment effectivity. The development of MRI has allowed for great advancements in health and biomedical research. In particular, it has granted us better opportunities to expand our knowledge of the intricate workings of the human brain, relating to its structure and function. Of particular interest both in neuroscience and society, is how structures and connectivity within the brain might differ between men and women, and whether these differences contribute to the differences demonstrated by men and women in their personality traits, behaviors and emotions. Previous MRI-based studies consistently report sex differences in overall brain size. Indeed, it is widely acknowledged that male brains are approximately 9-12% larger compared to female brains. However, published literature addressing whether differences exist in more defined sub-regions of the brain is highly inconclusive and recent research efforts by Joel et al. (2015) have challenged the broadly conceived idea that human brains are distinctly 'male' or 'female'. Many factors can contribute to the reporting of inconsistent findings across studies, including variations in study design, duplicate reporting of results in more than one publication and selective reporting of favorable findings. The goal of a systematic review is to address a specific research question and provide a thorough summary of the existing literature, through the collection and integration of multiple research studies. The selection of these studies for the review is based upon a structured methodological search, thereby preventing any bias in study selection. This review aimed to identify whether structural and connectivity differences exist between the brains of men and women based upon magnetic resonance imaging (MRI) data from existing literature, and in which regions these differences exist. After collating the data from 171 publications that were selected for inclusion, this review found that men and women demonstrate significant structural differences in the regions of the Broca area, cerebellum and in features related with disease throughout the brain. However, several factors including the number and age of subjects in each individual study as well as the MRI-imaging techniques and processing carried out heavily influence these findings. These results highlight the need for future neuroscientific research to account for the differences between men and women in regional brain areas. In addition, studies comprising large numbers of individuals at different stages across the lifespan and in which imaging methods are consistent and reproducible are required to allow more precise conclusions regarding specific sex differences that exist in the brain, where they exist and how they develop over time. Grasping a better understanding of how sex influences the brain will aid in efforts to identify the underlying mechanisms of the differences in the behavior of men and women, as well as in the differences between men and women in vulnerability to certain brain-related disorders.

AbstractFunding agencies are reluctant to support data archiving, even though large research funders such as the National Science Foundation (NSF) and the National Institutes of Health acknowledge its importance for scientific progress. Our quantitative estimates of data reuse indicate that ongoing financial investment in data-archiving infrastructure provides a high scientific return. Usage notesPubMed Central reuse of GEO datasets deposited in 2007This is the raw data behind the analysis. It contains one row for every mention of a 2007 GEO dataset in PubMed Central. Each row identifies the mentioned GEO dataset, the PubMed Central article that mentions the dataset's accession number, whether the authors of the dataset and the attributing article overlap, and whether this is considered an instance of third-party data reuse.PMC_reuse_of_2007_GEO_datasets.csvAggregate Table DataAggregate table data behind the figures and results in the README associated with the main dataset. Includes Baseline metrics used for extrapolating PubMed Central (PMC) results to PubMed, Number of mentions of a 2007 GEO dataset by authors who submitted the dataset, and Number of mentions of a dataset by authors who DID NOT submit the dataset across 2007-2010.tables.csv

Human life is precious and in the event of any unfortunate occurrence, highest efforts are made to safeguard it. To provide timely aid or undertake extraction of humans in distress, it is critical to accurately locate them. There has been an increased usage of drones to detect and track humans in such situations. Drones are used to capture high resolution images during search and rescue purposes. It is possible to find survivors from drone feed, but that requires manual analysis. This is a time taking process and is prone to human errors. This model can detect humans by looking at drone imagery and can draw bounding boxes around the location. This model is trained on IPSAR and SARD datasets where humans are on macadam roads, in quarries, low and high grass, forest shade, and Mediterranean and Sub-Mediterranean landscapes. Deep learning models are highly capable of learning complex semantics and can produce superior results. Use this deep learning model to automate the task of detection, reducing the time and effort required significantly.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.InputHigh resolution (1-5 cm) individual drone images or an orthomosaic.OutputFeature class containing detected humans.Applicable geographiesThe model is expected to work well in Mediterranean and Sub-Mediterranean landscapes but can also be tried in other areas.Model architectureThis model uses the FasterRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 82.2 percent for human class.Training dataThis model is trained on search and rescue dataset provided by IPSAR and SARD.LimitationsThis model has a tendency to maximize detection of humans and errors towards producing false positives in rocky areas.Sample resultsHere are a few results from the model.

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem — (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual disk access for only less than 5% of the observations. To the best of our knowledge, this is the first flexible MTS search algorithm capable of subsequence search on any subset of variables. Moreover, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.