Real-life event log of an information system managing road traffic fines.

RMA_PUB_POLY: This dataset describes the area of established BLM Recreation Management Areas (RMA). RMA data is created through the planning process and exists as a Special Management Area within the Oregon Data Framework (SMA). This data is subject to change without notification. For complete and current documentation of this data please see the 'Resource Management Areas' data standard located here http://www.blm.gov/or/datamanagement/index.php.

SDOT Miscellaneous Structures layer maintained by Seattle Department of Transportation.Feature Class: V_MiscStructuresRefresh Cycle: NightlyContact: Asset Management team

DWR has a long history of studying and characterizing California’s groundwater aquifers as a part of California’s Groundwater (Bulletin 118). The Basin Characterization Program provides the latest data and information about California’s groundwater basins to help local communities better understand their aquifer systems and support local and statewide groundwater management.

Under the Basin Characterization Program, new and existing data (AEM, lithology logs, geophysical logs, etc.) will be integrated to create continuous maps and three-dimensional models. To support this effort, new data analysis tools will be developed to create texture models, hydrostratigraphic models, and aquifer flow parameters. Data collection efforts will be expanded to include advanced geologic, hydrogeologic, and geophysical data collection and data digitization and quality control efforts will continue. To continue to support data access and data equity, the Basin Characterization Program will develop new online, GIS-based, visualization tools to serve as a central hub for accessing and exploring groundwater related data in California.

Additional information can be found on the Basin Characterization Program webpage.

DWR's Evaluation of Groundwater Resources: Maps and Models

DWR will undertake local and regional investigations to evaluate California's groundwater resources and develop state-stewarded maps and models. New and existing data will be combined and integrated using the analysis tools described below to develop maps and models to be developed will describe the grain size, the hydrostratigraphic properties, and hydrogeologic conceptual properties of California’s aquifers. These maps and models help groundwater managers understand how groundwater is stored and moves within the aquifer. The models will be state-stewarded, meaning that they will be regularly updated, as new data becomes available, to ensure that up-to-date information is used for groundwater management activities. The first iterations of the following maps and models will be published as they are developed:

• Texture Models
• Hydrostratigraphic Models
• Aquifer Recharge Potential Maps
• Extent of Important Aquifer Units
• Depth to Basement
• Depth to Freshwater

Local Investigations:

• Madera & North Kings
• Pajaro
• Western San Joaquin Valley

Regional Investigations:

• Sacramento Valley
• Four County Area of San Joaquin Valley (Madera, Fresno, Kings, and Tulare)
• San Joaquin Valley

Data Collection, Compilation, and Digitization

Data Collection

As a part of the Basin Characterization Program, advanced geologic, hydrogeologic, and geophysical data will be collected to improve our understanding of groundwater basins. Data collected under Basin Characterization are collected at a local, regional, or statewide scale depending on the scope of the study.

Datasets collected under the Basin Characterization Program can be found under the following resource:

• Geophysical, Hydrogeologic, & Geologic Datasets

Digitized Existing Lithology and Geophysical Logs

Lithology and geophysical logging data have been digitized to support the Statewide AEM Survey Project and will continue to be digitized to support Basin Characterization efforts. All digitized lithology logs with Well Completion Report IDs will be imported back into the OSWCR database.

Digitized lithology and geophysical logging can be found under the following resource:

• Digitized Lithology and Geophysical Logs.

Analysis Tools and Process Documents

To develop the state-stewarded maps and models outlined above, new tools and process documents will be created to integrate and analyze a wide range of data, including geologic, geophysical, and hydrogeologic information. By combining and assessing various datasets, these tools will help create a more complete picture of California's groundwater basins. All tools, along with guidance documents, will be made publicly available for local groundwater managers to use to support development of maps and models at a local scale. All tools and guidance will be updated as revisions to tools and process documents are made.

Analysis tools and process documents can be found under the following resource:

• Data Analysis Tools and Process Documents.

Data Visualization

Data access equity is a priority for the Basin Characterization Program. To ensure data access equity, the Basin Characterization Program has developed applications and tools to allow data to be visualized without needing access to expensive data visualization software. This list below provides links and descriptions for the Basin Characterization's suite of data viewers.

SGMA Data Viewer: Basin Characterization tab: Provides maps, depth slices, and profiles of Basin Characterization maps, models, and datasets, including the following:

• Aquifer Recharge Potential Maps
• Subsurface Texture Model Depth Slices
• Statewide AEM Survey Texture Depth Slices
• Lithology Log Location Maps
• Geophysical Logs Location Maps
• Statewide AEM Survey Profile Images

3D AEM Data Viewer: Displays the Statewide AEM Survey electrical resistivity and coarse fraction data, along with lithology logs, in a three-dimensional space.

DWR's Subsurface Viewer: Provides a map view and profile view of the Statewide AEM Survey electrical resistivity and coarse fraction data, along with lithology logs. The map view dynamically shows the exact location of AEM data displayed.

Basin Characterization Exchange

The Basin Characterization Exchange (BCX) is a meeting series and network space for the Basin Characterization community to exchange ideas, share lessons learned, define needed guidance, and highlight research topics. The BCX is open to federal, state, and local agencies, consultants, NGOs, academia, and interested parties who participate in Basin Characterization efforts. The BCX also plays a pivotal role in advancing the Basin Characterization Program’s activities and goals. BCX meetings will include regular updates from the Basin Characterization Program and participants can provide feedback and recommendations. Participants will also be provided with early opportunities to test data analysis tools and submit comments on draft process and guidance documents. BCX meetings are (generally) held the 3rd Tuesday of the month from 12:30 - 1:30 pm (PST).

Please email your contact information to Basin.Characterization@water.ca.gov if you’re interested in attending BCX meetings and to join the BCX listserv.

This dataset represents the information collected during the claims management process.

The dataset refers to the measurement of axes of Ti-6Al-4V cylindrical surfaces obtained by lathe turning. The machined surfaces were measured using a Coordinate Measuring Machine (CMM) and the axis of each cylinder was derived from the CMM measures.

The dataset consists of a MAT-file including the CMM measurements and a Matlab function “LoadData.m” to extract and convert the data into Cartesian coordinates.

All the details about the dataset can be found in:

Colosimo, B.M., Pacella, M. Analyzing the effect of process parameters on the shape of 3D profiles (2011) Journal of Quality Technology, 43 (3), pp. 169-195.DOI: 10.1080/00224065.2011.11917856 Pacella, M., Colosimo, B.M. Multilinear principal component analysis for statistical modeling of cylindrical surfaces: a case study (2018) Quality Technology and Quantitative Management, 15 (4), pp. 507-525.DOI: 10.1080/16843703.2016.1226710

This description is part of the blog post "Systematic Literature Review of teaching Open Science" https://sozmethode.hypotheses.org/839

According to my opinion, we do not pay enough attention to teaching Open Science in higher education. Therefore, I designed a seminar to teach students the practices of Open Science by doing qualitative research.About this seminar, I wrote the article ”Teaching Open Science and qualitative methods“. For the article ”Teaching Open Science and qualitative methods“, I started to review the literature on ”Teaching Open Science“. The result of my literature review is that certain aspects of Open Science are used for teaching. However, Open Science with all its aspects (Open Access, Open Data, Open Methodology, Open Science Evaluation and Open Science Tools) is not an issue in publications about teaching.

Based on this insight, I have started a systematic literature review. I realized quickly that I need help to analyse and interpret the articles and to evaluate my preliminary findings. Especially different disciplinary cultures of teaching different aspects of Open Science are challenging, as I myself, as a social scientist, do not have enough insight to be able to interpret the results correctly. Therefore, I would like to invite you to participate in this research project!

I am now looking for people who would like to join a collaborative process to further explore and write the systematic literature review on “Teaching Open Science“. Because I want to turn this project into a Massive Open Online Paper (MOOP). According to the 10 rules of Tennant et al (2019) on MOOPs, it is crucial to find a core group that is enthusiastic about the topic. Therefore, I am looking for people who are interested in creating the structure of the paper and writing the paper together with me. I am also looking for people who want to search for and review literature or evaluate the literature I have already found. Together with the interested persons I would then define, the rules for the project (cf. Tennant et al. 2019). So if you are interested to contribute to the further search for articles and / or to enhance the interpretation and writing of results, please get in touch. For everyone interested to contribute, the list of articles collected so far is freely accessible at Zotero: https://www.zotero.org/groups/2359061/teaching_open_science. The figure shown below provides a first overview of my ongoing work. I created the figure with the free software yEd and uploaded the file to zenodo, so everyone can download and work with it:

To make transparent what I have done so far, I will first introduce what a systematic literature review is. Secondly, I describe the decisions I made to start with the systematic literature review. Third, I present the preliminary results.

Systematic literature review – an Introduction

Systematic literature reviews “are a method of mapping out areas of uncertainty, and identifying where little or no relevant research has been done.” (Petticrew/Roberts 2008: 2). Fink defines the systematic literature review as a “systemic, explicit, and reproducible method for identifying, evaluating, and synthesizing the existing body of completed and recorded work produced by researchers, scholars, and practitioners.” (Fink 2019: 6). The aim of a systematic literature reviews is to surpass the subjectivity of a researchers’ search for literature. However, there can never be an objective selection of articles. This is because the researcher has for example already made a preselection by deciding about search strings, for example “Teaching Open Science”. In this respect, transparency is the core criteria for a high-quality review.

In order to achieve high quality and transparency, Fink (2019: 6-7) proposes the following seven steps:

1. Selecting a research question.
2. Selecting the bibliographic database.
3. Choosing the search terms.
4. Applying practical screening criteria.
5. Applying methodological screening criteria.
6. Doing the review.
7. Synthesizing the results.

I have adapted these steps for the “Teaching Open Science” systematic literature review. In the following, I will present the decisions I have made.

Systematic literature review – decisions I made

1. Research question: I am interested in the following research questions: How is Open Science taught in higher education? Is Open Science taught in its full range with all aspects like Open Access, Open Data, Open Methodology, Open Science Evaluation and Open Science Tools? Which aspects are taught? Are there disciplinary differences as to which aspects are taught and, if so, why are there such differences?
2. Databases: I started my search at the Directory of Open Science (DOAJ). “DOAJ is a community-curated online directory that indexes and provides access to high quality, open access, peer-reviewed journals.” (https://doaj.org/) Secondly, I used the Bielefeld Academic Search Engine (base). Base is operated by Bielefeld University Library and “one of the world’s most voluminous search engines especially for academic web resources” (base-search.net). Both platforms are non-commercial and focus on Open Access publications and thus differ from the commercial publication databases, such as Web of Science and Scopus. For this project, I deliberately decided against commercial providers and the restriction of search in indexed journals. Thus, because my explicit aim was to find articles that are open in the context of Open Science.
3. Search terms: To identify articles about teaching Open Science I used the following search strings: “teaching open science” OR teaching “open science” OR teach „open science“. The topic search looked for the search strings in title, abstract and keywords of articles. Since these are very narrow search terms, I decided to broaden the method. I searched in the reference lists of all articles that appear from this search for further relevant literature. Using Google Scholar I checked which other authors cited the articles in the sample. If the so checked articles met my methodological criteria, I included them in the sample and looked through the reference lists and citations at Google Scholar. This process has not yet been completed.
4. Practical screening criteria: I have included English and German articles in the sample, as I speak these languages (articles in other languages are very welcome, if there are people who can interpret them!). In the sample only journal articles, articles in edited volumes, working papers and conference papers from proceedings were included. I checked whether the journals were predatory journals – such articles were not included. I did not include blogposts, books or articles from newspapers. I only included articles that fulltexts are accessible via my institution (University of Kassel). As a result, recently published articles at Elsevier could not be included because of the special situation in Germany regarding the Project DEAL (https://www.projekt-deal.de/about-deal/). For articles that are not freely accessible, I have checked whether there is an accessible version in a repository or whether preprint is available. If this was not the case, the article was not included. I started the analysis in May 2019.
5. Methodological criteria: The method described above to check the reference lists has the problem of subjectivity. Therefore, I hope that other people will be interested in this project and evaluate my decisions. I have used the following criteria as the basis for my decisions: First, the articles must focus on teaching. For example, this means that articles must describe how a course was designed and carried out. Second, at least one aspect of Open Science has to be addressed. The aspects can be very diverse (FOSS, repositories, wiki, data management, etc.) but have to comply with the principles of openness. This means, for example, I included an article when it deals with the use of FOSS in class and addresses the aspects of openness of FOSS. I did not include articles when the authors describe the use of a particular free and open source software for teaching but did not address the principles of openness or re-use.
6. Doing the review: Due to the methodical approach of going through the reference lists, it is possible to create a map of how the articles relate to each other. This results in thematic clusters and connections between clusters. The starting point for the map were four articles (Cook et al. 2018; Marsden, Thompson, and Plonsky 2017; Petras et al. 2015; Toelch and Ostwald 2018) that I found using the databases and criteria described above. I used yEd to generate the network. „yEd is a powerful desktop application that can be used to quickly and effectively generate high-quality diagrams.” (https://www.yworks.com/products/yed) In the network, arrows show, which articles are cited in an article and which articles are cited by others as well. In addition, I made an initial rough classification of the content using colours. This classification is based on the contents mentioned in the articles’ title and abstract. This rough content classification requires a more exact, i.e., content-based subdivision and

The 'WFD Management Catchments Cycle 1' dataset is a polygon Shapefile defined for the implementation of the Water Framework Directive. Management catchments are the unit of geography for which action plans are drafted in implementing the first cycle of the WFD. In Cycle 2 there are three new Management Catchment datasets; WFD Surface Water Management Catchments Cycle 2, WFD Artificial Water Management Catchments Cycle 2 and WFD Groundwater Management Catchments Cycle 2. WFD Management Catchments Cycle 1 have been delineated by using WFD River Water body Catchments Cycle 1 [these were delineated through use of the CEH Flow Grid hydrological model run with CEH Integrated Hydrological Digital Terrain Model (IHDTM) data and from a 50m resolution CEH flow grid] as “building blocks” that have been aggregated together within a GIS, ensuring that WFD rivers do not intersect boundaries. This process was conducted by using expert judgement in consultation. This dataset was previously known as WFD Management Catchments Please note that the Environment Agency no longer provide data for water bodies in Wales - this should now available from Natural Resources Wales. Attribution statement: Contains Environment Agency information © Environment Agency and/or database rights 2017.© Contains Ordnance Survey data © Crown copyright and database right.2013.Based on digital spatial data licensed from the Centre for Ecology & Hydrology, © NERC (CEH).

The swine production datasets are the product of ongoing work by the University of Arkansas Center for Agricultural and Rural Sustainability, the United States Department of Agriculture, and the National Pork Board. The data documentation in this metadata record describes the project background and nomenclature, in addition to a description of the dataset structure, individual unit processes, and production scenarios. Flow-level metadata descriptions for selected unit processes within the U.S. swine dataset can be found in the Appendix section. The goal of this work was to provide pork producers and consumers with objective, science-based information on the environmental performance of various pork production practices in the United States. The scope of this work was a cradle-to-farm gate assessment with emphasis on the different management strategies used in the live swine housing and production phases. The system boundaries encompassed the extraction of raw material and feed production through the live swine production facility processes to the farm gate (see Figure 1). The reference flow for the system is one market pig at the farm gate. The market weight of the pig is assumed to be 275 pounds. It should be noted that it is not appropriate for the user to assume different market weights when using this dataset. Pork production scenarios developed for Iowa, Illinois, and North Carolina represent 86% of production in the U.S. Resources in this dataset:Resource Title: Development of Life Cycle Inventory Data for U.S. Swine Production Scenarios: Dataset Documentation and User’s Guide, Version 2. File Name: Development of Life Cycle Inventory Data for U.S. Swine Production Scenarios.pdfResource Title: Development of Life Cycle Inventory Data for U.S. Swine Production Scenarios. File Name: swine.zip

This dataset was developed for the River Basin Management Plan for Ireland 2018 – 2021 (second cycle River Basin Management Plan). The Areas for Action are areas where action will be carried out in the second cycle. The data consists of polygon geometry representing the location and extent of the Areas for Action (waterbodies) and tabular attribute data describing the waterbody. The Areas for Action were selected based on the priorities in the draft river basin management plan, the evidence from the Water Framework Directive characterisation process, and the expertise, data and knowledge of public body staff with responsibilities for water and the different pressure types. Following the selection process, the Local Authorities Water and Communities Office (LAWCO) undertook public engagement and feedback sessions on the Areas for Action. These were considered in the drafting of the final River Basin Management Plan, which was published on April 17th 2018. The Action Plan Start Year is the year the Local Authority Waters Programme (LAWPRO) plan to begin assessment work within the Area for Action. This is not a final dataset and will likely change over the lifecycle of the River Basin Management Plan.

This dataset contains supplementary materials to accompany Kung (2022), including a complete list of questions designed to guide the reader through the process of writing a data management plan (DMP) and some sample DMPs specific to linguistic research. File list: DMP-Resources.pdf - a list of resources that are useful in creating a DMP, such as file size calculators and equipment lists. Linguistics-DMP-questions.pdf - a list of questions designed to guide the reader through the process of writing a DMP. sample_dmp.pdf - an anonymized and annotated DMP for a senior research grant submitted to the Documenting Endangered Languages program of the National Science Foundation (NSF), shared here with the permission of the original authors. sample_dmp-comprehensive.pdf - a comprehensive sample DMP that addresses all of the questions contained in DMP-questions document.

Research data is increasingly viewed as an important scholarly output. While a growing body of studies have investigated researcher practices and perceptions related to data sharing, information about data-related practices throughout the research process (including data collection and analysis) remains largely anecdotal. Building on our previous study of data practices in neuroimaging research, we conducted a survey of data management practices in the field of psychology. Our survey included questions about the type(s) of data collected, the tools used for data analysis, practices related to data organization, maintaining documentation, backup procedures, and long-term archiving of research materials. Our results demonstrate the complexity of managing and sharing data in psychology. Data is collected in multifarious forms from human participants, analyzed using a range of software tools, and archived in formats that may become obsolete. As individuals, our participants demonstrated relatively good data management practices, however they also indicated that there was little standardization within their research group. Participants generally indicated that they were willing to change their current practices in light of new technologies, opportunities, or requirements.