https://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/CKYCHUhttps://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/CKYCHU
The GIS data maintained by HPPM includes information on buildings and grounds related to Harvard University. Our "standard" base layers are available to Harvard affiliates and their service providers (for example, architects) working on Harvard projects in AutoCAD DWG, ESRI SHP or File Geodatabase format. Additional datasets are sometimes available by special arrangement. http://home.hppm.harvard.edu/pages/gis-data-layers
The file contains a characterization of the welfare-maximizing mechanism and a simplified model with an ex-ante private signal for the paper "Securities Auctions with Pre-project Information Management".
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This report describes the results of a regularly distributed survey of nationwide Emergency Managers as part of The Extreme Weather and Emergency Management Survey (WxEM) series. This project aims to send surveys to Emergency Managers across the United States three to four times a year, although that frequency may change based on Emergency Manager and research needs. The Extreme Weather and Emergency Management Survey, Wave 1 (WxEM Wave 1) was designed and administered by the Institute for Public Policy Research and Analysis (IPPRA) at the University of Oklahoma. It is the first survey in the series (Stormer et al. 2023 and Wanless et al. 2023). WxEM Wave 1 opened on August 4, 2022, using an online questionnaire that as of this writing has been completed by 720 Emergency Management personnel that were contacted from an IPPRA built database of Emergency Managers from across the country (see Stormer et al. 2023 for more information on the database). WxEM Wave 1 was designed to recruit participants for the WxEM project. This survey gathers demographic information on enrollees, including location, jurisdiction type, and experience. This report presents an overview of the methodology of the survey data collection, and a reproduction of the survey instrument with frequencies for the questions that elicited numeric responses. Because this survey acts as an enrollment tool, it will remain open, and results may vary as participants are added and removed. Future reference reports will update the number of participants as the project continues.
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This database includes all secondary data used in the project "Epistemic Market Objects: How Sponsored Content Disrupted Marketing." It includes data from Adweek, podcast data, and third-party platform reviews. Unfortunately, we are not allowed to share primary interviews as we signed a confidentiality form with participants. This form is also protected by the Canadian Tri-Council for Research.
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This technical report aims to provide detailed information on the results of Stage I of the methodology used to find references that are potentially relevant to the topic “Classification of Key Competencies for Construction Project Management.” In Stage I, potentially relevant references were searched using the American Society of Civil Engineers (ASCE) Library. In the ASCE Library, “advanced search” was used to find applicable references via specific search terms, topics and publication dates. For topics, the term “construction” was used. The option “title” was checked to specify where to look for search terms. The search terms used included competencies, competence, skill, capability, knowledge, project manager, project management, construction management, and engineering management. For more representative results, the search was restricted to references inclusively published from 1988 to 2019. When more than one chapter of a book was found, instead of counting all the chapters found, the book was counted as one single reference. In such cases, the book title might exclude all the search terms used. If the same reference was found under different search terms, it was numbered only one time when counting the total number of references initially found. This process resulted in 2,102 references retrieved from the ASCE Library (Table 1 to Table 16). In the following Tables, “Selected: Yes” indicates that the initially-retrieved reference was ultimately selected for content analysis, and “Selected: No” means that the reference was not selected for content analysis.
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This data was collected over two academic years, 2018/19 and 2019/20 from students enrolled in two courses at UTA: ART 4365 Technology in Art Education and IE 4340 Engineering Project Management. The data collection instruments were pre- and post-self assessment surveys, distributed at the beginning and end of the semester. The data includes student-self reported competencies for Maker Competencies 9 and 10, "Assembles Effective Teams" and "Collaborates Effectively" on a range of 1 (low) to 5 (high).
Relational SQLite Database Tables of Kickstarter, including projects, creators, funders, comments, geography, pledge and funding, etc.
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In the ABC metadata project, a metadata model with mappings to international standards and a translation to JSON is developed. The ABC metadata model (Amsterdam UMC Biomedical Concise metadata model) is a model that provides guidance for minimal metadata implementation in the field of biomedicine. Being a model implies that it isn’t a ready-made solution, but it offers guidance to what metadata is needed for FAIR data management. It is deliberately kept minimal, thus facilitating data exchange between persons and systems, reducing workload for researchers and support alike. It could be considered to be a checklist at the utmost minimum of items that should be reported for FAIR data management. It aims to bridge the gap between generic metadata standards and detailed metadata generated by man and machine in the field of biomedicine, covering all disciplines in health and life sciences. The ambition of the development team is that the ABC metadata model eventually will grow to become a standard by wide adaptation in FAIR RDM practice, collective collaboration in improvement of the model and community endorsement.
Startup Cartography Project (http://www.startupcartography.com)
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The Feed the Future Innovation Lab on Small-Scale Irrigation (FTF-ILSSI) is a cooperative agreement funded by USAID under the Feed the Future program to undertake research aimed to increase food production, improve nutrition, accelerate economic development and contribute to the protection of the environment. The project seeks these objectives through identifying, testing and demonstrating technological options in small-scale irrigation and irrigated fodder, supported by a continual dialogue approach with stakeholders and capacity development toward sustained use of research approaches and evidence. Collaborators on this project include Texas A&M University, the International Water Management Institute (IWMI), the International Food Policy Research Institute (IFPRI), the International Livestock Research Institute (ILRI), North Carolina A&T State University (NCAT) and Texas A&M AgriLife Research (TAMUS). As part of this project, IFPRI is undertaking a study of irrigating and non-irrigating households in Tanzania, Ethiopia, and Ghana to investigate the connections between irrigation, gender, nutrition and health. The survey explores these linkages through an in-depth household questionnaire with questions on agricultural production, nutrition and health, a WEAI module and a community questionnaire. This work forms part of the CGIAR Research Program on Water, Land and Ecosystems (WLE).
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The title of the project was: "Providing collateral and improving product market access for smallholder farmers: a Randomized evaluation of inventory credit in Sierra Leone."
This SPSS file contains data from the 12-item Consideration of Future Consequences scale (Strathman, Gleicher, Boninger, & Edwards, 1994, JPSP), along with demographic information, from the WECT Project: Cohen, T. R. & Panter, A. T. (2011-2012). The WECT Project: Workplace experiences and character traits [project information]. Information available at: http://www.wectproject.org/ and on the Open Science Framework https://osf.io/w3hgr/. These data were first used in: Cohen, T. R., Panter, A. T., Turan, N., Morse, L. A., & Kim, Y. (2014). Moral character in the workplace. Journal of Personality and Social Psychology, 107(5), 943-963. doi: http://dx.doi.org/10.1037/a0037245
China's P2P project Data.The project data has been privacy protected.
Experimental Data and Program Code in VBA for the picker routing problem in multi-block high-level storage systems using genetic algorithms and ACO
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This dataset contains charts of different participatory research tools drawn from gender and age differentiated groups as well as results of focus group discussion carried out separately with men and women of different age groups. The study was conducted in five villages, three in the East Region and two in the South. Villages were selected based on their location (proximity to logging areas, easy access by road and to markets) and composition (similar size of village, different ethnic groups). Participatory research tools such as Seasonal Activity Calendar (SAC) and Access and Control Matrix (ACM) in addition to Focus Group Discussions (FGD)– were selected for comparison with household surveys used in the other components of the Beyond timber project to represent both quantitative and qualitative, and conventional and participatory research methods. Data collection with the use of each tool was done in gender and age segregated groups in each of the sampled communities. These participatory methods and tools were used to: - Examine participants’ knowledge differentiated by gender and age on the collection / gathering / harvesting, processing and management of forest resources and their uses (medicinal, cultural, domestic and social); - To bring together the knowledge of women and men from different ages to inform the broader project about how communities use, manage and benefit from forest resources. FGD segregated by gender and age were held in each community. A number of 5 -10 participants took part in the different group discussions. The participants were split into four groups: (i) younger women (15 - 35 years of age), (ii) older women (> 35 years of age), (iii) younger men (15 - 35 years of age) and older men (> 35 years of age). Gender and age were selected as analytical variables to ensure a wider range of experiences with respect to forest resources
Performance information is overwhelmingly used in program evaluation by both public managers and external stakeholders. In the market-based New Public Management movement, effectiveness is public programs’ major selling point. However, this approach may marginalize the role of democratic values in governance. In the current complex society with anti-government sentiments, we embrace the idea of New Public Service to reiterate the importance of democratic values. Using a conjoint experiment, we compare the effects of effectiveness and democratic values in predicting public program evaluation, conditioned on citizens’ trust in government. Our results show that effectiveness and democratic values contribute similar effects in explaining policy preferences. Distrust in government strengthens the effect of democratic values but reduces the effect of effectiveness. Our findings challenge the prevalent effectiveness centric framework in public management. We suggest that citizen-state interaction should not rely only on performance merits, but also on inclusiveness and openness values.
CDP was founded as the Carbon Disclosure Project in 2000, aimed at encouraging firms to disclose more information about their climate-change-related risks and opportunities. On behalf of investors and governments, the CDP surveys public companies worldwide to assess their dependencies on the world's natural resources and their strategies for reducing greenhouse gas emissions, safeguarding water resources, and preventing deforestation. The 3 annual surveys focus on climate change, forests, and water. We have access to following datasets: Climate Change Data: 2010 - 2021 Climate Change Scores: 2010 - 2021 Climate Change Questionnaires: 2010 - 2021 Water Data: 2010 - 2021 Water Scores: 2016- 2021 Water Questionnaires: 2010 - 2021 Forests Data: 2013 - 2021 Forests Scores: 2016- 2021 Forests Questionnaires: 2013 - 2021 Data files for climate change, forests, and water include tickers and ISINs for the surveyed companies. File format: All data files and scores are in Excel format. All Questionnaires are in PDF format.
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The title of the project was: "Index-Insurance in Gujarat"
This study developed a webtoon education program on preventive self-management related to premature labor (PSM-PL) for women of childbearing age, evaluated its effectiveness via RCT, and assessed its usability for webtoon education for women of childbearing age.
The purpose of TOURism Flows in European destinations during and after the Covid-19 pandemic (TOURCO) is to use computer-based algorithms to scrape large unique data on tourism flows from a popular leading travel portal. The project cover periods before, during, and after the Covid-19 pandemic of three European countries (France, Spain, and Denmark). These data make it possible to capture pre-trends and also changes that are a result of the pandemic. The project is funded and scientifically supervised by the Mobile Lives Forum, as part of its research program on the mobility transition. The Mobile Lives Forum is a research institute created by SNCF.
https://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/CKYCHUhttps://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/CKYCHU
The GIS data maintained by HPPM includes information on buildings and grounds related to Harvard University. Our "standard" base layers are available to Harvard affiliates and their service providers (for example, architects) working on Harvard projects in AutoCAD DWG, ESRI SHP or File Geodatabase format. Additional datasets are sometimes available by special arrangement. http://home.hppm.harvard.edu/pages/gis-data-layers