The U.S. Merit Systems Protection Board (MSPB) has the statutory responsibility to assess the health of Federal merit systems and the authority to conduct special studies of the Federal civil service (see 5 U.S.C. 1204(a)(3) and 5 U.S.C. 1204(e)(3)). MSPB administers a periodic Merit Principles Survey (MPS) to help carry out those studies. Those studies, including summaries and analyses of data from the MPS, are officially submitted to the President and Congress and shared with Federal policymakers and agencies.

The FAIR principles were published in 2016 in a Scientific Data article titled ‘FAIR Guiding Principles for scientific data management and stewardship’. These were developed to aid in the discovery and reuse of research data.FAIR stands for Findable, Accessible, Interoperable, and Reusable. Data that meet these principles are more optimal for reuse and discoverability and in turn increase your research’s exposure.Here’s how your data is more FAIR when it’s on Figshare.Illustration by Jason McDermott of RedPenBlackPen.

Global FAIR Data Principles Market is segmented by Application (IT_Research_Healthcare_Government_Academia), Type (Findable_Accessible_Interoperable_Reusable_Metadata Standards), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

Here published artikel about The FAIR Guiding Principles for scientific data management and stewardship

MPS contains a combination of core items that MSPB tracks over time and special-purpose items developed to support a particular special study. This survey differs from the Federal Employee Viewpoint Survey administered by OPM in several respects, including: a focus on merit system principles and Governmentwide civil service issues; administration every few years instead of annually; and a smaller sample. Agency participation in the MPS was mandatory, but individual response to the survey was voluntary.

Data sets accompanying the paper "The FAIR Assessment Conundrum: Reflections on Tools and Metrics", an analysis of a comprehensive set of FAIR assessment tools and the metrics used by these tools for the assessment.

The data set "metrics.csv" consists of the metrics collected from several sources linked to the analysed FAIR assessments tools. It is structured into 11 columns: (i) tool_id, (ii) tool_name, (iii) metric_discarded, (iv) metric_fairness_scope_declared, (v) metric_fairness_scope_observed, (vi) metric_id, (vii) metric_text, (viii) metric_technology, (ix) metric_approach, (x) last_accessed_date, and (xi) provenance.

The columns tool_id and tool_name are used for the identifier we assigned to each tool analysed and the full name of the tool respectively.

The metric_discarded column refers to the selection we operated on the collected metrics, since we excluded the metrics created for testing purposes or written in a language different from English. The possible values are boolean. We assigned TRUE if the metric was discarded.

The columns metric_fairness_scope_declared and metric_fairness_scope_observed are used for indicating the declared intent of the metrics, with respect to the FAIR principle assessed, and the one we observed respectively. Possible values are: (a) a letter of the FAIR acronym (for the metrics without a link declared to a specific FAIR principle), (b) one or more identifiers of the FAIR principles (F1, F2…), (c) n/a, if no FAIR references were declared, or (d) none, if no FAIR references were observed.

The metric_id and metric_text columns are used for the identifiers of the metrics and the textual and human-oriented content of the metrics respectively.

The column metric_technology is used for enumerating the technologies (a term used in its widest acceptation) mentioned or used by the metrics for the specific assessment purpose. Such technologies include very diverse typologies ranging from (meta)data formats to standards, semantic technologies, protocols, and services. For tools implementing automated assessments, the technologies listed take into consideration also the available code and documentation, not just the metric text.

The column metric_approach is used for identifying the type of implementation observed in the assessments. The identification of the implementation types followed a bottom-to-top approach applied to the metrics organised by the metric_fairness_scope_declared values. Consequently, while the labels used for creating the implementation type strings are the same, their combination and specialisation varies based on the characteristics of the actual set of metrics analysed. The main labels used are: (a) 3rd party service-based, (b) documentation-centred, (c) format-centred, (d) generic, (e) identifier-centred, (f) policy-centred, (g) protocol-centred, (h) metadata element-centred, (i) metadata schema-centred, (j) metadata value-centred, (k) service-centred, and (l) na.

The columns provenance and last_accessed_date are used for the main source of information about each metric (at least with regard to the text) and the date we last accessed it respectively.

The data set "classified_technologies.csv" consists of the technologies mentioned or used by the metrics for the specific assessment purpose. It is structured into 3 columns: (i) technology, (ii) class, and (iii) discarded.

The column technology is used for the names of the different technologies mentioned or used by the metrics.

The column class is used for specifying the type of technology used. Possible values are: (a) application programming interface, (b) format, (c) identifier, (d) library, (e) licence, (f) protocol, (g) query language, (h) registry, (i) repository, (j) search engine, (k) semantic artefact, and (l) service.

The discarded column refers to the exclusion of the value 'linked data' from the accepted technologies since it is too generic. The possible values are boolean. We assigned TRUE if the technology was discarded.

This chapter described at a very high level some of the considerations that need to be made when designing algorithms for a vehicle health management application. The choices made here affect the quality of the diagnosis and prognosis (covered in Chapter 7). Therefore, the algorithmic design choices are made in conjunction with the design choices for diagnostics and prognostics to optimally support these tasks. Furthermore, additional considerations imposed by computational constraints, resource availability, algorithm maintenance, need for algorithm re-tuning, etc. will impact the solutions. It should also be noted that technological advances, both in hardware and software, impose the need for new solutions. For example, as new materials and new sensors are being developed, the algorithmic solutions will need to follow suit. In general, there seems to be a trend to have more sensor data available. While this is potentially a good thing, sensor data provides value only when it is being processed and interpreted properly, in part by the techniques described here. Testing of the methods, however, requires the “right” kind of data. Generally, there is a lack of seeded fault data which are required to train and validate algorithms. It is also important to migrate information from the component to the subsystem to the system levels so that health management technologies can be applied effectively and efficiently at the vehicle level. It may be required to perform elements described in this chapter between different levels of the vehicle architecture.

In a November 2023 survey, only half of data privacy professionals in European companies thought that most companies that they knew of complied with the core principles of GDPR. Data transfer compliance was the most problematic area, with nearly 45 percent of respondents stating that most companies were still having problems and around 24 percent saying that most were not complying at all.

The following concepts detailed in the publication were taken from an article written by Howard Zehr and Henry Mika, (1998),"Fundamental Concepts in Restorative Justice", in Contemporary Justice Review, Vol. 1. At the primary level, restorative justice in Canada is guided by recognizing the need for victims to heal and put right the wrongs. Restorative Justice also grounds itself in engaging with community and recognizing the need for dialogue between victims and offenders as appropriate.

Dataset outlining the eight fundamental principles of protecting personal data in AI systems based on the 2025 guide, along with practitioner-focused explanations.

This chapter described at a very high level some of the considerations that need to be made when designing algorithms for a vehicle health management application. The choices made here affect the quality of the diagnosis and prognosis (covered in Chapter 7). Therefore, the algorithmic design choices are made in conjunction with the design choices for diagnostics and prognostics to optimally support these tasks. Furthermore, additional considerations imposed by computational constraints, resource availability, algorithm maintenance, need for algorithm re-tuning, etc. will impact the solutions. It should also be noted that technological advances, both in hardware and software, impose the need for new solutions. For example, as new materials and new sensors are being developed, the algorithmic solutions will need to follow suit. In general, there seems to be a trend to have more sensor data available. While this is potentially a good thing, sensor data provides value only when it is being processed and interpreted properly, in part by the techniques described here. Testing of the methods, however, requires the “right” kind of data. Generally, there is a lack of seeded fault data which are required to train and validate algorithms. It is also important to migrate information from the component to the subsystem to the system levels so that health management technologies can be applied effectively and efficiently at the vehicle level. It may be required to perform elements described in this chapter between different levels of the vehicle architecture.

Science journalists, traditionally, play a key role in delivering science information to a wider audience. However, changes in the media ecosystem and the science-media relationship are posing challenges to reliable news production. Additionally, recent developments such as ChatGPT and Artificial Intelligence (AI) more generally, may have further consequences for the work of (science) journalists. Through a mixed-methodology, the quality of news reporting was studied within the context of AI. A content analysis of media output about AI (news articles published within the time frame 1 September 2022–28 February 2023) explored the adherence to quality indicators, while interviews shed light on journalism practices regarding quality reporting on and with AI. Perspectives from understudied areas in four European countries (Belgium, Italy, Portugal, and Spain) were included and compared. The findings show that AI received continuous media attention in the four countries. Furthermore, despite four different media landscapes, the reporting in the news articles adhered to the same quality criteria such as applying rigour, including sources of information, accessibility, and relevance. Thematic analysis of the interview findings revealed that impact of AI and ChatGPT on the journalism profession is still in its infancy. Expected benefits of AI related to helping with repetitive tasks (e.g. translations), and positively influencing journalistic principles of accessibility, engagement, and impact, while concerns showed fear for lower adherence to principles of rigour, integrity and transparency of sources of information. More generally, the interviewees expressed concerns about the state of science journalism, including a lack of funding influencing the quality of reporting. Journalists who were employed as staff as well as those who worked as freelancers put efforts in ensuring quality output, for example, via editorial oversight, discussions, or memberships of associations. Further research into the science-media relationship is recommended.

This document sets out the basic principles for the State, local governments and companies to publish and promote utilization of public data in keeping with initiatives to date.

A comprehensive workbook created to facilitate and document the process of decomposing the FAIR Guiding Principles and mapping them to key categories, core concepts, focus elements, and harmonized indicators. It also contains a complete list of the indicators.

This dataset is about books. It has 1 row and is filtered where the book is Principles of data networks & computer communications. It features 7 columns including author, publication date, language, and book publisher.

A newer version of the workbook was released to correct several typos, which can be accessed at: https://doi.org/10.5281/zenodo.8057317

A comprehensive workbook created to facilitate and document the process of decomposing the FAIR Guiding Principles and mapping them to key categories, core concepts, focus elements, and harmonized indicators. It also contains a complete list of the indicators.

"The Principles for Open Government Data Initiatives by the Executive Yuan and its subordinate agencies aim to promote the openness and sharing of government data, enhance administrative transparency, and improve public service effectiveness. The principles regulate the scope, format, and management mechanisms of open data, emphasizing data quality, usability, and personal data protection, thereby promoting interagency cooperation and innovative social applications."

According to INSPIRE transformed development plan “Grundwiesen 1. Change” of the city of Schwäbisch Hall based on an XPlanung dataset in version 5.0.

The objective of this study was to systematically review and statistically synthesize all available research that, at a minimum, compared participants in a restorative justice program to participants processed in a more traditional way using meta-analytic methods. Ideally, these studies would include research designs with random assignment to condition groups, as this provides the most credible evidence of program effectiveness. The systematic search identified 99 publications, both published and unpublished, reporting on the results of 84 evaluations nested within 60 unique research projects or studies. Results were extracted from these studies, related to delinquency, non-delinquency, and victim outcomes for the youth and victims participating in these programs.

We present a detailed exposition of how first-principles methods can be used to guide alkali superionic conductor (ASIC) study and design. Using the argyrodite Li6PS5Cl as a case study, we demonstrate how modern information technology (IT) infrastructure and software tools can facilitate the assessment of alkali superionic conductors in terms of various critical properties of interest such as phase and electrochemical stability and ionic conductivity. The emphasis is on well-documented, reproducible analysis code that can be readily generalized to other material systems and design problems. For our chosen Li6PS5Cl case study material, we show that Li excess is crucial to enhancing its conductivity by increasing the occupancy of interstitial sites that promote long-range Li+ diffusion between cage-like frameworks. The predicted room-temperature conductivities and activation barriers are in reasonably good agreement with experimental values.