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This study contributes to the conference theme of information science processes and practices by examining the research data discovery contexts of ecological and social scientists who reuse datasets for research. The aim of the research was to gain insight into the data discovery contexts of these scientists and to understand the data needs related to data discoverability and reuse. The study identified four dimensions of data needs, including research processes, making sense of data, data reuse, and data access. Additionally, a conceptualization of data needs within the context of data reuse was proposed, which has not been thoroughly examined in previous studies. The study employed a mixed-method approach within the post-positivist research paradigm to identify the different contexts in which data is discovered. A combination of survey and in-depth interview techniques were used to investigate the broader contexts of data discovery in people’s information-seeking processes. The critical incident technique was used to elicit the contexts of data discovery, and the interview protocol was structured based on the stages of a data lifecycle. Interviews were conducted with 24 participants from three organizations, including TERN, ADA, and CSIRO. Participants held diverse job roles and were at different career stages. The study identified four dimensions of data needs and examined their relationship with the roles of data managers and end-users. The findings contribute to the existing literature on data needs and emphasize the potential usefulness of research data and the need for paradata. The study also suggests that anticipating the contexts of data reuse involves considering what data users may find useful. Ensuring data quality is crucial for successful data reuse, which involves having access to organizational data expertise, providing data in various formats and platforms, and ensuring sufficient data coverage. Data needs are influenced by the specific research ob

This study contributes to the conference theme of information science processes and practices by examining the research data discovery contexts of ecological and social scientists who reuse datasets for research. The aim of the research was to gain insight into the data discovery contexts of these scientists and to understand the data needs related to data discoverability and reuse. The study identified four dimensions of data needs, including research processes, making sense of data, data reuse, and data access. Additionally, a conceptualization of data needs within the context of data reuse was proposed, which has not been thoroughly examined in previous studies. The study employed a mixed-method approach within the post-positivist research paradigm to identify the different contexts in which data is discovered. A combination of survey and in-depth interview techniques were used to investigate the broader contexts of data discovery in people’s information-seeking processes. The critical incident technique was used to elicit the contexts of data discovery, and the interview protocol was structured based on the stages of a data lifecycle. Interviews were conducted with 24 participants from three organizations, including TERN, ADA, and CSIRO. Participants held diverse job roles and were at different career stages. The study identified four dimensions of data needs and examined their relationship with the roles of data managers and end-users. The findings contribute to the existing literature on data needs and emphasize the potential usefulness of research data and the need for paradata. The study also suggests that anticipating the contexts of data reuse involves considering what data users may find useful. Ensuring data quality is crucial for successful data reuse, which involves having access to organizational data expertise, providing data in various formats and platforms, and ensuring sufficient data coverage. Data needs are influenced by the specific research ob

This study contributes to the conference theme of information science processes and practices by examining the research data discovery contexts of ecological and social scientists who reuse datasets for research. The aim of the research was to gain insight into the data discovery contexts of these scientists and to understand the data needs related to data discoverability and reuse. The study identified four dimensions of data needs, including research processes, making sense of data, data reuse, and data access. Additionally, a conceptualization of data needs within the context of data reuse was proposed, which has not been thoroughly examined in previous studies. The study employed a mixed-method approach within the post-positivist research paradigm to identify the different contexts in which data is discovered. A combination of survey and in-depth interview techniques were used to investigate the broader contexts of data discovery in people’s information-seeking processes. The critical incident technique was used to elicit the contexts of data discovery, and the interview protocol was structured based on the stages of a data lifecycle. Interviews were conducted with 24 participants from three organizations, including TERN, ADA, and CSIRO. Participants held diverse job roles and were at different career stages. The study identified four dimensions of data needs and examined their relationship with the roles of data managers and end-users. The findings contribute to the existing literature on data needs and emphasize the potential usefulness of research data and the need for paradata. The study also suggests that anticipating the contexts of data reuse involves considering what data users may find useful. Ensuring data quality is crucial for successful data reuse, which involves having access to organizational data expertise, providing data in various formats and platforms, and ensuring sufficient data coverage. Data needs are influenced by the specific research ob

This study contributes to the conference theme of information science processes and practices by examining the research data discovery contexts of ecological and social scientists who reuse datasets for research. The aim of the research was to gain insight into the data discovery contexts of these scientists and to understand the data needs related to data discoverability and reuse. The study identified four dimensions of data needs, including research processes, making sense of data, data reuse, and data access. Additionally, a conceptualization of data needs within the context of data reuse was proposed, which has not been thoroughly examined in previous studies. The study employed a mixed-method approach within the post-positivist research paradigm to identify the different contexts in which data is discovered. A combination of survey and in-depth interview techniques were used to investigate the broader contexts of data discovery in people’s information-seeking processes. The critical incident technique was used to elicit the contexts of data discovery, and the interview protocol was structured based on the stages of a data lifecycle. Interviews were conducted with 24 participants from three organizations, including TERN, ADA, and CSIRO. Participants held diverse job roles and were at different career stages. The study identified four dimensions of data needs and examined their relationship with the roles of data managers and end-users. The findings contribute to the existing literature on data needs and emphasize the potential usefulness of research data and the need for paradata. The study also suggests that anticipating the contexts of data reuse involves considering what data users may find useful. Ensuring data quality is crucial for successful data reuse, which involves having access to organizational data expertise, providing data in various formats and platforms, and ensuring sufficient data coverage. Data needs are influenced by the specific research ob

This study contributes to the conference theme of information science processes and practices by examining the research data discovery contexts of ecological and social scientists who reuse datasets for research. The aim of the research was to gain insight into the data discovery contexts of these scientists and to understand the data needs related to data discoverability and reuse. The study identified four dimensions of data needs, including research processes, making sense of data, data reuse, and data access. Additionally, a conceptualization of data needs within the context of data reuse was proposed, which has not been thoroughly examined in previous studies. The study employed a mixed-method approach within the post-positivist research paradigm to identify the different contexts in which data is discovered. A combination of survey and in-depth interview techniques were used to investigate the broader contexts of data discovery in people’s information-seeking processes. The critical incident technique was used to elicit the contexts of data discovery, and the interview protocol was structured based on the stages of a data lifecycle. Interviews were conducted with 24 participants from three organizations, including TERN, ADA, and CSIRO. Participants held diverse job roles and were at different career stages. The study identified four dimensions of data needs and examined their relationship with the roles of data managers and end-users. The findings contribute to the existing literature on data needs and emphasize the potential usefulness of research data and the need for paradata. The study also suggests that anticipating the contexts of data reuse involves considering what data users may find useful. Ensuring data quality is crucial for successful data reuse, which involves having access to organizational data expertise, providing data in various formats and platforms, and ensuring sufficient data coverage. Data needs are influenced by the specific research ob

The European Green Deal, its Farm to Fork strategy and Biodiversity strategy will set the scene for the future revisions of the Common Agricultural Policy (CAP). The CAP will address an increasing set of objectives, including contributing to the Sustainable Development Goals and the Paris climate agreement. To enable evidence-based policy making and monitoring, the Farm to Fork strategy proposes to extend the current monitoring system to include a broader range of sustainability issues. The current monitoring system called Farm Accountancy Data Network (FADN) has a strong focus on financial and economic data. The FADN is an instrument for monitoring and evaluation of the EU Common Agricultural Policy and collects bookkeeping results from 80,000 farms. The extension to a Farm Sustainability Data Network (FSDN) should include a broader set of indicators on the sustainability performance of farms. This paper estimates the costs of collecting this broader set of sustainability indicators in the FSDN based on the experiences of a pilot in 9 member states and a survey among all member states. The results show that collecting the sustainability data from all farms included in FADN would increase the costs by about 40%. The results show large differences between countries depending on the current costs of data collection and the expected additional work to include sustainability indicators. Given the pressing need for these data, a scenario was developed where sustainability data are collected from a subsample of 15,000 farms. This can be achieved within current budget limits if the current FADN sample would be reduced from 85,000 to 75,000 farms. The discussion section addresses some concerns raised on the extension of FADN to FSDN such as: willingness of farmers, administrative burden, economic background of FADN and the quality of the data.

Tato bakalářská práce se zabývá problematikou identifikace datových a funkčních prvků. Autor dále stanovuje možné metody, jak identifikované datové a funkční prvky pomocí kontroly vyhodnotit a určit, které mají větší prioritu., This bachelor thesis deals with the problems of identification of data and functionals needs. The author sets out possible methods of identifiing priority of data and functional needs. Also author sets possible methods of checking the correctness of indentified needs., Fakulta ekonomicko-správní, Student obeznámil komisi s výsledky své bakalářské práce, kterou zpracovával na téma Datové a funkční potřeby pro vývoj nového informačního systému. Při obhajobě student zodpověděl položené otázky: Jak náročné bylo pro pracovníky agendy OT pochopení Kano metody a zaznamenávání výsledků do Kano tabulky? Jak vypadal předložený prototyp? Popište formu. Co bylo výstupem bakalářské práce?

This report describes the results of our data needs assessment of the College of Engineering (CoE). With the growing focus on data, the needs of researchers can be unclear if not studied at the source. This led to the creation of this study in which the investigators interviewed faculty from across multiple departments within CoE. This focused study provided information and insight into the daily research practices related to data. The results of our study provided several categories and trends of data related needs and recommendations going forward as to how to better support the needs identified.

Societal expectations about agricultural production are changing. There are increased demands on issues such as food safety, animal welfare and the impact of agriculture on the environment (land, water and air). These changes have been refl ected in the European Union’s (EU) Common Agricultural Policy (CAP), but information on these issues is lacking and this complicates the required evaluation of policies. The EU Framework 7 project FLINT tries to close this gap by analysing the feasibility of collecting data on these new topics. FLINT has established a data infrastructure with up-to-date farm-level indicators for themonitoring and evaluation of the CAP. The project created a pilot network of more than 1,000 farms to collect a set of sustainability indicators at farm level. The pilot represents farm diversity at EU level, including the different administrative environments in the Member States. This paper sets out the context and the main contributions of the project.

Accurate and complete construction records and as-built data are the key prerequisites to the effective management of transportation infrastructure assets throughout their life cycle. The construction phase is the best time to collect such data. Assets such as underground drainage and culverts are visible and physically accessible only during construction. For assets such as guardrails, signals, and pavement, it is safer and more efficient to collect data during construction than after construction when the road segment is open to traffic. The purpose of this project was to conduct a synthesis study to 1) assess the current status at INDOT regarding the collection of asset data during the construction phase and the use of such data in the operation and maintenance (O&M) phase, and 2) develop a framework for INDOT to leverage the construction inspection and documentation process to collect data for assets. Data needs during O&M were identified through rounds of meetings with relevant INDOT business units. The current practice in construction documentation was investigated in detail. A survey of state highway agencies (SHAs) was conducted to assess the state-of-the-practice. A practical framework was developed to leverage the construction inspection and documentation practice to collect asset data that are needed in O&M. The framework uses specific pay items—construction activities that result in physical structures—as the bridge to connect plan assets (i.e. physical structures specified in the design documents) to their corresponding counterparts in the asset management systems. The framework is composed of 1) a data needs component for determining the information requirements from the O&M perspective, 2) a construction documentation module, and 3) a mapping mechanism to link data items to be collected during the construction documentation to data items in the asset management systems. The mapping mechanism was tested and validated using four priority asset classes—u

Accurate and complete construction records and as-built data are the key prerequisites to the effective management of transportation infrastructure assets throughout their life cycle. The construction phase is the best time to collect such data. Assets such as underground drainage and culverts are visible and physically accessible only during construction. For assets such as guardrails, signals, and pavement, it is safer and more efficient to collect data during construction than after construction when the road segment is open to traffic. The purpose of this project was to conduct a synthesis study to 1) assess the current status at INDOT regarding the collection of asset data during the construction phase and the use of such data in the operation and maintenance (O&M) phase, and 2) develop a framework for INDOT to leverage the construction inspection and documentation process to collect data for assets. Data needs during O&M were identified through rounds of meetings with relevant INDOT business units. The current practice in construction documentation was investigated in detail. A survey of state highway agencies (SHAs) was conducted to assess the state-of-the-practice. A practical framework was developed to leverage the construction inspection and documentation practice to collect asset data that are needed in O&M. The framework uses specific pay items—construction activities that result in physical structures—as the bridge to connect plan assets (i.e. physical structures specified in the design documents) to their corresponding counterparts in the asset management systems. The framework is composed of 1) a data needs component for determining the information requirements from the O&M perspective, 2) a construction documentation module, and 3) a mapping mechanism to link data items to be collected during the construction documentation to data items in the asset management systems. The mapping mechanism was tested and validated using four priority asset classes—u

The Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA) established new requirements for data development and dissemination that have had an impact on federal, state, and local transportation planning processes across the United States. As transportation professionals look ahead to the 21st century and the reauthorization of ISTEA, broad scale and rapid changes will surely challenge transportation decision making and affect future needs for data to support sound transportation planning. The conference provided an opportunity for participants to (a) identify the types of data that are critical for planning and policy analysis; (b) identify data-collection requirements; (c) discuss the appropriate roles of and relationships among federal, state, and local agencies in the context of data collection and dissemination; and (d) review the impact of technological advances on data collection and dissemination. The invitation-only conference included participants from state departments of transportation (DOTs), metropolitan planning organizations (MPOs), and federal agencies providing broad representation of the transportation planning and policy communities. Significant effort was made to include a full spectrum of policy, management, and front-line analysts who have extensive experience in answering policy questions, supporting the planning process, and responding to federal reporting requirements. The record is a prepublication copy of the uncorrected proofs in PDF. 92p. The published version can be ordered the Transportation Research Board Bookstore.

SPR-3707, Accurate and complete construction records and as‐built data are the key prerequisites to the effective management of transportation, infrastructure assets throughout their life cycle. The construction phase is the best time to collect such data. Assets such as underground, drainage and culverts are visible and physically accessible only during construction. For assets such as guardrails, signals, and pavement, it is safer and more efficient to collect data during construction than after construction when the road segment is open to traffic., The purpose of this project was to conduct a synthesis study to 1) assess the current status at INDOT regarding the collection of asset, data during the construction phase and the use of such data in the operation and maintenance (O&M) phase, and 2) develop a, framework for INDOT to leverage the construction inspection and documentation process to collect data for assets. Data needs during, O&M were identified through rounds of meetings with relevant INDOT business units. The current practice in construction, documentation was investigated in detail. A survey of state highway agencies (SHAs) was conducted to assess the state‐of‐the‐practice., A practical framework was developed to leverage the construction inspection and documentation practice to collect asset data that are, needed in O&M. The framework uses specific pay items—construction activities that result in physical structures—as the bridge to, connect plan assets (i.e. physical structures specified in the design documents) to their corresponding counterparts in the asset, management systems. The framework is composed of 1) a data needs component for determining the information requirements from, the O&M perspective, 2) a construction documentation module, and 3) a mapping mechanism to link data items to be collected during, the construction documentation to data items in the asset management systems. The mapping mechanism was tested and validated, using four priority asset classes—underdrains, guardrails, attenuators, and small culverts—from an INDOT construction project. The, testing results show that the newly developed framework is viable and solid to collect asset data during the construction phase for O&M, use in the future, without adding extra workload to construction crews. The framework can reduce/eliminate the duplicate data, collection efforts at INDOT, leading to savings and efficiency gains in the long term.