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

Common data types and the data processing steps applied in the Comprehensive Patient Records project.

It's very simple, open data is information that anyone can find, explore, and reuse. A large amount of this data is collected during the course of normal government activities, including the provision of services, research, or administration. Open data, by definition, must be freely available, easily discoverable, anonymous, accessible, automatically published, and licensed to allow its reuse. By publishing this information in a central location, at datos.congresogto.gob.mx, it is now easy to find, explore, and reuse. Examples of how open data is used include creating an application, researching, supporting evidence-based decision-making, developing a business plan to create goods or services, or simply improving knowledge and understanding of social, economic, and environmental trends. Government open data does not contain private or confidential information, as the data is anonymized before publication. This is done to ensure that the highest standards of privacy are met and security is not compromised. The volume of data from the Legislative Branch of the State of Guanajuato is currently growing at an increasing rate; so is the potential value of this data. Making data open has economic and social benefits. Some of the valuable datasets include transportation data, cartographic (geospatial) data, health data, environmental data, demographic data, and real-time emergency data. Most of the time, the agency publishing the data cannot foresee its future use, or how it might be combined with other data or displayed in a program, interface, or tool. Open data creates connections between the government, the private and research sectors, stimulating entrepreneurial activity and developing knowledge that can benefit all Mexicans. When government data is put online, it also makes things more efficient. Agencies can store, share, and update data across the normal boundaries of departments or areas. Having a single source of truth reduces the time and costs of doing regular government business. There are even more uses: open data helps in the development of evidence-based decisions that lead to better informed policies, research, and social outcomes. The policy of the Legislative Branch of the State of Guanajuato on public data is simple: all agencies and institutional areas must make non-confidential data open by default, and it must be free, easy to use, and reliable. According to the public data policy statement, agencies should publish anonymous data: Translated from Spanish Original Text: Es muy simple, los datos abiertos es información que cualquiera puede encontrar, explorar y reusar. Una gran cantidad de estos datos se recopilan durante el curso de las actividades normales del gobierno, incluida la prestación de servicios, la investigación o la administración. Los datos abiertos, por definición, deben estar disponibles de forma gratuita, fácilmente detectables, anónimos, accesibles, publicados de forma automática y con licencias que permitan su reutilización. Al publicar esta información en una ubicación central, en datos.congresogto.gob.mx, ahora es fácil de encontrar, explorar y reutilizar. Los ejemplos de cómo se utilizan los datos abiertos incluyen crear una aplicación, investigar, respaldar la toma de decisiones basada en evidencia, desarrollar un plan de negocios para crear bienes o servicios, o simplemente mejorar el conocimiento y la comprensión de las tendencias sociales, económicas y ambientales. Los datos abiertos del gobierno no contienen información privada o confidencial, ya que los datos se anonimizan antes de su publicación. Esto se hace para garantizar que se cumplan los más altos estándares de privacidad y no se infrinja la seguridad. El volumen de datos del Poder Legislativo del Estado de Guanajuato está creciendo actualmente a un ritmo cada vez mayor; también el valor potencial de estos datos. Hacer que los datos sean abiertos tiene beneficios económicos y sociales. Algunos de los conjuntos de datos de valor incluyen datos de transporte, datos cartográficos (geoespaciales), datos sanitarios, datos medioambientales, datos demográficos y datos de emergencia en tiempo real. La mayoría de las veces, la dependencia que publica los datos no puede prever su uso futuro, o cómo podría combinarse con otros datos o mostrarse en un programa, interfaz o herramienta. Los datos abiertos crean conexiones entre el gobierno, los sectores privado y de investigación, estimulando la actividad empresarial y desarrollando conocimientos que pueden beneficiar a todos los mexicanos. Cuando los datos del gobierno se ponen en línea, también hace que las cosas sean más eficientes. Las dependencias pueden almacenar, compartir y actualizar datos a través de los límites normales de los departamentos o áreas. Tener una única fuente de verdad reduce el tiempo y los costos de hacer negocios regulares del gobierno. Hay aún más usos: los datos abiertos ayudan en el desarrollo de decisiones basadas en la evidencia que conducen a políticas, investigación y resultados sociales mejor informados. La política del Poder Legislativo del Estado de Guanajuato sobre datos públicos es simple: todas las dependencias y áreas institucionales deben hacer que los datos no confidenciales estén abiertos de forma predeterminada, y deben ser gratuitos, fáciles de usar y confiables. De acuerdo con la declaración de la política de datos públicos, las agencias deberían publicar datos anónimos:

This dataset comprises semi-structured interviews conducted with domain experts in data protection and privacy regulations from various jurisdictions. All interviewee have formal legal education or are recognized as experts in the domain (ex: data protection authority, known authors/speakers). The purpose of the interviews is to identify common and divergent data protection regulatory requirements that impact transborder personal data flows and compliance in multiple systems.

The database includes transcripts of expert interviews that have been made publicly available, upon informed and explicit consent from the participants. By default, all interviews are anonymized to protect the identity of the participants and to reduce potential bias during analysis. Other interviews may be available upon request, and other are kept confidential depending on the participants consent.

The files have been named in the following manner: [COUNTRY OF EXPERTISE]-[Random letters]. This way, when referring to the specific subject, we can identify them this way.

Transcripts were done with AI models upon the consent of the subject. The section in bold represent the interviewer, and normal font is the interviewee. For more details on the transcription purpose, you can check here [unavailable for reviewing reasons]

In addition we have included a detailed codebook used to support qualitative analysis. The codebook provides definitions for each code used. When new codes emerged, these were added to the codebook with appropriate annotation (ex: labelled as new), including a definition and possibly verbatims.

The guiding semi-structured interview and blinded consent form are included.

For more information, please refer to [unavailable for reviewing reasons]

Recommended minimum criteria for incorporation into a checklist for data safe haven cross-accreditation.

About this webinar Now more than ever researchers are needing to embrace electronic data capture methods to keep their research moving in the midst of social distancing restrictions and decreased access to survey participants. Using a research specific survey tool can not only solve this problem, but also set your research up for success through intuitive data collection and validation, scheduling and reporting. This webinar will introduce and compare two of the most popular research tools for the collection of survey data and patient records: REDCap and Qualtrics. Webinar Topics Electronic Data Capture: Surveys vs Forms Confidential vs Anonymous data collection Strengths and weaknesses of Qualtrics and REDCap Real-life use cases for each tool Using survey tools for longitudinal studies Licence Copyright © 2021 Intersect Australia Ltd. All rights reserved.

Abstract copyright UK Data Service and data collection copyright owner.

The Business Structure Database (BSD) contains a small number of variables for almost all business organisations in the UK. The BSD is derived primarily from the Inter-Departmental Business Register (IDBR), which is a live register of data collected by HM Revenue and Customs via VAT and Pay As You Earn (PAYE) records. The IDBR data are complimented with data from ONS business surveys. If a business is liable for VAT (turnover exceeds the VAT threshold) and/or has at least one member of staff registered for the PAYE tax collection system, then the business will appear on the IDBR (and hence in the BSD). In 2004 it was estimated that the businesses listed on the IDBR accounted for almost 99 per cent of economic activity in the UK. Only very small businesses, such as the self-employed were not found on the IDBR.

The IDBR is frequently updated, and contains confidential information that cannot be accessed by non-civil servants without special permission. However, the ONS Virtual Micro-data Laboratory (VML) created and developed the BSD, which is a 'snapshot' in time of the IDBR, in order to provide a version of the IDBR for research use, taking full account of changes in ownership and restructuring of businesses. The 'snapshot' is taken around April, and the captured point-in-time data are supplied to the VML by the following September. The reporting period is generally the financial year. For example, the 2000 BSD file is produced in September 2000, using data captured from the IDBR in April 2000. The data will reflect the financial year of April 1999 to March 2000. However, the ONS may, during this time, update the IDBR with data on companies from its own business surveys, such as the Annual Business Survey (SN 7451).

The data are divided into 'enterprises' and 'local units'. An enterprise is the overall business organisation. A local unit is a 'plant', such as a factory, shop, branch, etc. In some cases, an enterprise will only have one local unit, and in other cases (such as a bank or supermarket), an enterprise will own many local units.

For each company, data are available on employment, turnover, foreign ownership, and industrial activity based on Standard Industrial Classification (SIC)92, SIC 2003 or SIC 2007. Year of 'birth' (company start-up date) and 'death' (termination date) are also included, as well as postcodes for both enterprises and their local units. Previously only pseudo-anonymised postcodes were available but now all postcodes are real.

The ONS is continually developing the BSD, and so researchers are strongly recommended to read all documentation pertaining to this dataset before using the data.

Linking to Other Business Studies
These data contain IDBR reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Latest Edition Information
For the sixteenth edition (March 2024), data files and a variable catalogue document for 2023 have been added.

Main Topics:

The following variables are available for enterprises and local units:

• employment (and employees)
• turnover
• Standard Industrial Classification (1992, 2003 and 2007 classifications are available)
• legal status (e.g. sole proprietor, partnership, public corporation, non-profit organisation etc)
• foreign ownership
• birth (company start date)
• death (termination date of trading)
• various geographical variables
  

'Employment' includes business owners, whereas 'employees' measures the number of staff, excluding owners.

Observations for enterprises also include a variable for ownership if the enterprise is part of a large group of companies.

Local units have an additional ‘death code’ variable, which serves as an indicator as to why the plant closed (e.g. as a result of a merger). It should also be noted that there is no turnover information for individual plants. This is because the ONS does not collect financial information at the plant level, which is notoriously difficult, especially for manufacturing plants where often no financial transactions are processed.

The birth and death variables are particularly useful for research, although it should be noted that for businesses that began trading before 1973, their birth date will be set to 1973. This is the year that VAT was introduced in the UK, and hence the first point in time for VAT registration for these companies. Companies that began trading since 1973 have their ‘real’ date of birth listed.

The Phantom of Bern: repeated scans of two volunteers with eight different combinations of MR sequence parameters

The Phantom of Bern consists of eight same-session re-scans of T1-weighted MRI with different combinations of sequence parameters, acquired on two healthy subjects. The subjects have agreed in writing to the publication of these data, including the original anonymized DICOM files and waving the requirement of defacing. Usage is permitted under the terms of the data usage agreement stated below.

CONTENT

The BIDS directory is organized as follows:

└── PhantomOfBern/
  ├─ code/
  │
  ├─ derivatives/
  │ ├─ dldirect_v1-0-0/
  │ │ ├─ results/ # Folder with flattened subject/session inputs and outputs of DL+DiReCT
  │ │ └─ stats2table/ # Folder with tables summarizing all DL+DiReCT outputs
  │ ├─ freesurfer_v6-0-0/
  │ │ ├─ results/ # Folder with flattened subject/session inputs and outputs of freesurfer
  │ │ └─ stats2table/ # Folder with tables summarizing all freesurfer outputs
  │ └─ siena_v2-6/
  │   ├─ SIENA_results.csv # Siena's main output
  │   └─ ... # Flattened subject/session inputs and outputs of SIENA
  │
  ├─ sourcedata/
  │ ├─ POBHC0001/
  │ │ └─ 17473A/
  │ │   └─ ... # Anonymized DICOM folders
  │ └─ POBHC0002/
  │   └─ 14610A/
  │    └─ ... # Anonymized DICOM folders
  │
  ├─ sub-<label>/
  │ └─ ses-<label>/
  │   └─ anat/ # Folder with scan's json and nifti files
  ├─ ...

ACKNOWLEDGEMENT

The dataset can be cited as:

M. Rebsamen, D. Romascano, M. Capiglioni, R. Wiest, P. Radojewski, C. Rummel. The Phantom of Bern:
repeated scans of two volunteers with eight different combinations of MR sequence parameters.
OpenNeuro, 2023.

If you use these data, please also cite the original paper:

M. Rebsamen, M. Capiglioni, R. Hoepner, A. Salmen, R. Wiest, P. Radojewski, C. Rummel. Growing importance
of brain morphometry analysis in the clinical routine: The hidden impact of MR sequence parameters.
Journal of Neuroradiology, 2023.

DATA USE AGREEMENT

The Phantom of Bern is distributed under the following terms, to which you agree by downloading and/or using the dataset:

1. Any intentional identification of a subject or disclosure of his or her confidential information violates the promise of confidentiality given to the providers of the information. Therefore, all users of the dataset agree:

• To use these datasets solely for research and development or statistical purposes and not for investigation of specific subjects

• To make no use of the identity of any subject discovered inadvertently, and to advise the providers of any such discovery (crummel@web.de)

1. When publicly presenting any results or algorithms that benefited from the use of the Phantom of Bern, you should acknowledge it, see above. Papers, book chapters, books, posters, oral presentations, and all other printed and digital presentations of results derived from the Phantom of Bern data should cite the publications listed above.

2. Redistribution of data (complete or in parts) in any manner without explicit inclusion of this data use agreement is prohibited.

3. Usage of the data for testing commercial tools is explicitly allowed. Usage for military purposes is prohibited.

4. The original collector and provider of the data (see acknowledgement) and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.

FUNDING

This work was supported by the Swiss National Science Foundation under grant numbers 204593 (ScanOMetrics) and CRSII5_180365 (The Swiss-First Study).

The Anonymous Employee Feedback Tools market has gained significant traction over recent years as organizations increasingly recognize the importance of fostering a culture of open communication and employee engagement. These tools provide a secure and confidential way for employees to voice their opinions, concerns

Authentication is a critical challenge in fog computing security, especially as fog servers provide services to many IoT users. The conventional authentication process often requires disclosing sensitive personal information, such as usernames, emails, mobile numbers, and passwords that end users are reluctant to share with intermediary services (i.e., Fog servers). With the rapid growth of IoT networks, existing authentication methods often fail to balance low computational overhead with strong security, leaving systems vulnerable to various attacks, including unauthorized access and data interception. Additionally, traditional intrusion detection methods are not well-suited for the distinct characteristics of IoT devices, resulting in a low accuracy in applying existing anomaly detection methods. In this paper, we incorporate a two-step authentication process, starting with anonymous authentication using a secret ID with Elliptic Curve Cryptography (ECC), followed by an intrusion detection algorithm for users flagged as suspicious activity. The scheme allows users to register with a Cloud Service Provider (CSP) using encrypted credentials. The CSP responds with a secret number reserved in the Fog node for the IoT user. To access the services provided by the Fog Service Provider (FSP), IoT users must submit a secret ID. Furthermore, we introduce a staked ensemble learning approach for intrusion detection that achieves 99.86% accuracy, 99.89% precision, 99.96% recall, and a 99.91% F1-score in detecting anomalous instances, with a support count of 50,376. This approach is applied when users fail to provide a correct secret ID. Our proposed scheme utilizes several hash functions through symmetric encryption and decryption techniques to ensure secure end-to-end communication.