The data provides a summary of the state of development practice for Geographic Information Systems (GIS) software (as of August 2017). The summary is based on grading a set of 30 GIS products using a template of 56 questions based on 13 software qualities. The products range in scope and purpose from a complete desktop GIS systems, to stand-alone tools, to programming libraries/packages.

The template used to grade the software is found in the TabularSummaries.zip file. Each quality is measured with a series of questions. For unambiguity the responses are quantified wherever possible (e.g.~yes/no answers). The goal is for measures that are visible, measurable and feasible in a short time with limited domain knowledge. Unlike a comprehensive software review, this template does not grade on functionality and features. Therefore, it is possible that a relatively featureless product can outscore a feature-rich product.

A virtual machine is used to provide an optimal testing environments for each software product. During the process of grading the 30 software products, it is much easier to create a new virtual machine to test the software on, rather than using the host operating system and file system.

The raw data obtained by measuring each software product is in SoftwareGrading-GIS.xlsx. Each line in this file corresponds to between 2 and 4 hours of measurement time by a software engineer. The results are summarized for each quality in the TabularSummaries.zip file, as a tex file and compiled pdf file.

The Remote Sensing Interpretation Software market is experiencing robust growth, driven by increasing demand across diverse sectors. The market size, while not explicitly stated, can be reasonably estimated based on the provided information and typical market growth rates for technology sectors. Considering a CAGR (Compound Annual Growth Rate) and the given study period (2019-2033), a conservative estimate for the 2025 market size might fall within the range of $5-7 billion USD. This growth is fueled by several key factors. Firstly, the expanding application of remote sensing in precision agriculture, facilitating optimized resource allocation and improved crop yields, is a significant driver. Secondly, the petroleum and mineral exploration sector leverages remote sensing for efficient resource identification and extraction, contributing substantially to market growth. Furthermore, advancements in cloud-based solutions are enhancing accessibility and scalability, lowering barriers to entry for various users. Government initiatives promoting the use of geospatial technologies in various sectors, particularly in developing economies, are creating significant opportunities. The integration of AI and machine learning in remote sensing interpretation tools is further accelerating market expansion, enabling faster and more accurate analysis. However, certain restraints are present. High initial investment costs associated with acquiring sophisticated software and hardware can limit adoption, especially among smaller companies and organizations. The need for skilled professionals to operate and interpret data effectively poses a challenge. Data security concerns and the potential for inaccuracies resulting from environmental factors or data processing errors also present hurdles. Despite these challenges, the market's future trajectory remains optimistic, particularly with ongoing technological advancements and increasing government and private sector investment driving innovation and accessibility. Segmentation by application (Petroleum and Mineral Exploration, Agriculture and Forestry, Medicine, Military, Meteorological, Research, etc.) and type (Cloud-based, On-Premise) reveals the market's diverse and expanding potential, with significant growth anticipated in cloud-based solutions driven by their scalability and accessibility. The competitive landscape comprises both established players like Hexagon, Microsoft, and IBM, and emerging companies like Sense Time and Geovis Technology, reflecting a dynamic and innovative market.

The primary intent of this workshop is to provide practical training in using Statistics Canada geography files with the leading industry standard software: Environmental Systems Research Institute, Inc.(ESRI) ArcGIS 9x. Participants will be introduced to the key features of ArcGIS 9x, as well as to geographic concepts and principles essential to understanding and working with geographic information systems (GIS) software. The workshop will review a range of geography and attribute files available from Statistics Canada, as well as some best practices for accessing this information. A brief overview of complementary data sets available from federal and provincial agencies will be provided. There will also be an opportunity to complete a practical exercise using ArcGIS9x. (Note: Data associated with this presentation is available on the DLI FTP site under folder 1873-221.)

This file presents the catalog of metrics and the topic independent classification done.

Update: We updated the data set in March 2022 by adding newly published papers and by providing more insights on how we analyzed them. Details can be found in the file " SEnti-SMS.xlsx".

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Update: The updated version (-v2) contains the results of one more snowballing iteration and extracted information on the accuracy of the used methods.

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In 2020, we conducted a systematic literature review to explore the development and application of sentiment analysis tools in software engineering.

Information on the execution of the SLR, its scope, the search string, etc. are presented in the paper linked below.

In this blog I’ll share the workflow and tools used in the GIS part of this analysis. To understand where crashes are occurring, first the dataset had to be mapped. The software of choice in this instance was ArcGIS, though most of the analysis could have been done using QGIS. Heat maps are all the rage, and if you want to make simple heat maps for free and you appreciate good documentation, I recommend the QGIS Heatmap plugin. There are also some great tools in the free open-source program GeoDa for spatial statistics.

The USDA Long-Term Agroecosystem Research was established to develop national strategies for sustainable intensification of agricultural production. As part of the Agricultural Research Service, the LTAR Network incorporates numerous geographies consisting of experimental areas and locations where data are being gathered. Starting in early 2019, two working groups of the LTAR Network (Remote Sensing and GIS, and Data Management) set a major goal to jointly develop a geodatabase of LTAR Standard GIS Data Layers. The purpose of the geodatabase was to enhance the Network's ability to utilize coordinated, harmonized datasets and reduce redundancy and potential errors associated with multiple copies of similar datasets. Project organizers met at least twice with each of the 18 LTAR sites from September 2019 through December 2020, compiling and editing a set of detailed geospatial data layers comprising a geodatabase, describing essential data collection areas within the LTAR Network. The LTAR Standard GIS Data Layers geodatabase consists of geospatial data that represent locations and areas associated with the LTAR Network as of late 2020, including LTAR site locations, addresses, experimental plots, fields and watersheds, eddy flux towers, and phenocams. There are six data layers in the geodatabase available to the public. This geodatabase was created in 2019-2020 by the LTAR network as a national collaborative effort among working groups and LTAR sites. The creation of the geodatabase began with initial requests to LTAR site leads and data managers for geospatial data, followed by meetings with each LTAR site to review the initial draft. Edits were documented, and the final draft was again reviewed and certified by LTAR site leads or their delegates. Revisions to this geodatabase will occur biennially, with the next revision scheduled to be published in 2023. Resources in this dataset:Resource Title: LTAR Standard GIS Data Layers, 2020 version, File Geodatabase. File Name: LTAR_Standard_GIS_Layers_v2020.zipResource Description: This file geodatabase consists of authoritative GIS data layers of the Long-Term Agroecosystem Research Network. Data layers include: LTAR site locations, LTAR site points of contact and street addresses, LTAR experimental boundaries, LTAR site "legacy region" boundaries, LTAR eddy flux tower locations, and LTAR phenocam locations.Resource Software Recommended: ArcGIS,url: esri.com Resource Title: LTAR Standard GIS Data Layers, 2020 version, GeoJSON files. File Name: LTAR_Standard_GIS_Layers_v2020_GeoJSON_ADC.zipResource Description: The contents of the LTAR Standard GIS Data Layers includes geospatial data that represent locations and areas associated with the LTAR Network as of late 2020. This collection of geojson files includes spatial data describing LTAR site locations, addresses, experimental plots, fields and watersheds, eddy flux towers, and phenocams. There are six data layers in the geodatabase available to the public. This dataset was created in 2019-2020 by the LTAR network as a national collaborative effort among working groups and LTAR sites. Resource Software Recommended: QGIS,url: https://qgis.org/en/site/

This is the authors’ version of the work. It is based on a poster presented at the Wageningen Conference on Applied Soil Science, http://www.wageningensoilmeeting.wur.nl/UK/ Cite as: Bosco, C., de Rigo, D., Dewitte, O., Montanarella, L., 2011. Towards the reproducibility in soil erosion modeling: a new Pan-European soil erosion map. Wageningen Conference on Applied Soil Science “Soil Science in a Changing World”, 18 - 22 September 2011, Wageningen, The Netherlands. Author’s version DOI:10.6084/m9.figshare.936872 arXiv:1402.3847

Towards the reproducibility in soil erosion modeling:a new Pan-European soil erosion map

Claudio Bosco ¹, Daniele de Rigo ¹ ² , Olivier Dewitte ¹, Luca Montanarella ¹ ¹ European Commission, Joint Research Centre, Institute for Environment and Sustainability,Via E. Fermi 2749, I-21027 Ispra (VA), Italy² Politecnico di Milano, Dipartimento di Elettronica e Informazione,Via Ponzio 34/5, I-20133 Milano, Italy

Soil erosion by water is a widespread phenomenon throughout Europe and has the potentiality, with his on-site and off-site effects, to affect water quality, food security and floods. Despite the implementation of numerous and different models for estimating soil erosion by water in Europe, there is still a lack of harmonization of assessment methodologies. Often, different approaches result in soil erosion rates significantly different. Even when the same model is applied to the same region the results may differ. This can be due to the way the model is implemented (i.e. with the selection of different algorithms when available) and/or to the use of datasets having different resolution or accuracy. Scientific computation is emerging as one of the central topic of the scientific method, for overcoming these problems there is thus the necessity to develop reproducible computational method where codes and data are available. The present study illustrates this approach. Using only public available datasets, we applied the Revised Universal Soil loss Equation (RUSLE) to locate the most sensitive areas to soil erosion by water in Europe. A significant effort was made for selecting the better simplified equations to be used when a strict application of the RUSLE model is not possible. In particular for the computation of the Rainfall Erosivity factor (R) the reproducible research paradigm was applied. The calculation of the R factor was implemented using public datasets and the GNU R language. An easily reproducible validation procedure based on measured precipitation time series was applied using MATLAB language. Designing the computational modelling architecture with the aim to ease as much as possible the future reuse of the model in analysing climate change scenarios is also a challenging goal of the research.

References [1] Rusco, E., Montanarella, L., Bosco, C., 2008. Soil erosion: a main threats to the soils in Europe. In: Tóth, G., Montanarella, L., Rusco, E. (Eds.), Threats to Soil Quality in Europe. No. EUR 23438 EN in EUR - Scientific and Technical Research series. Office for Official Publications of the European Communities, pp. 37-45 [2] Casagrandi, R. and Guariso, G., 2009. Impact of ICT in Environmental Sciences: A citation analysis 1990-2007. Environmental Modelling & Software 24 (7), 865-871. DOI:10.1016/j.envsoft.2008.11.013 [3] Stallman, R. M., 2005. Free community science and the free development of science. PLoS Med 2 (2), e47+. DOI:10.1371/journal.pmed.0020047 [4] Waldrop, M. M., 2008. Science 2.0. Scientific American 298 (5), 68-73. DOI:10.1038/scientificamerican0508-68 [5] Heineke, H. J., Eckelmann, W., Thomasson, A. J., Jones, R. J. A., Montanarella, L., and Buckley, B., 1998. Land Information Systems: Developments for planning the sustainable use of land resources. Office for Official Publications of the European Communities, Luxembourg. EUR 17729 EN [6] Farr, T. G., Rosen, P A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., Alsdorf, D., 2007. The Shuttle Radar Topography Mission. Review of Geophysics 45, RG2004, DOI:10.1029/2005RG000183 [7] Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., and New, M., 2008. A European daily high-resolution gridded dataset of surface temperature and precipitation. Journal of Geophysical Research 113, (D20) D20119+ DOI:10.1029/2008jd010201 [8] Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K., and Yoder, D. C., 1997. Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE). Agriculture handbook 703. US Dept Agric., Agr. Handbook, 703 [9] Bosco, C., Rusco, E., Montanarella, L., Panagos, P., 2009. Soil erosion in the alpine area: risk assessment and climate change. Studi Trentini di scienze naturali 85, 119-125 [10] Bosco, C., Rusco, E., Montanarella, L., Oliveri, S., 2008. Soil erosion risk assessment in the alpine area according to the IPCC scenarios. In: Tóth, G., Montanarella, L., Rusco, E. (Eds.), Threats to Soil Quality in Europe. No. EUR 23438 EN in EUR - Scientific and Technical Research series. Office for Official Publications of the European Communities, pp. 47-58 [11] de Rigo, D. and Bosco, C., 2011. Architecture of a Pan-European Framework for Integrated Soil Water Erosion Assessment. IFIP Advances in Information and Communication Technology 359 (34), 310-31. DOI:10.1007/978-3-642-22285-6_34 [12] Bosco, C., de Rigo, D., Dewitte, O., and Montanarella, L., 2011. Towards a Reproducible Pan-European Soil Erosion Risk Assessment - RUSLE. Geophys. Res. Abstr. 13, 3351 [13] Bollinne, A., Laurant, A., and Boon, W., 1979. L’érosivité des précipitations a Florennes. Révision de la carte des isohyétes et de la carte d’erosivite de la Belgique. Bulletin de la Société géographique de Liége 15, 77-99 [14] Ferro, V., Porto, P and Yu, B., 1999. A comparative study of rainfall erosivity estimation for southern Italy and southeastern Australia. Hydrolog. Sci. J. 44 (1), 3-24. DOI:10.1080/02626669909492199 [15] de Santos Loureiro, N. S. and de Azevedo Coutinho, M., 2001. A new procedure to estimate the RUSLE EI30 index, based on monthly rainfall data and applied to the Algarve region, Portugal. J. Hydrol. 250, 12-18. DOI:10.1016/S0022-1694(01)00387-0 [16] Rogler, H., and Schwertmann, U., 1981. Erosivität der Niederschläge und Isoerodentkarte von Bayern (Rainfall erosivity and isoerodent map of Bavaria). Zeitschrift fur Kulturtechnik und Flurbereinigung 22, 99-112 [17] Nearing, M. A., 1997. A single, continuous function for slope steepness influence on soil loss. Soil Sci. Soc. Am. J. 61 (3), 917-919. DOI:10.2136/sssaj1997.03615995006100030029x [18] Morgan, R. P C., 2005. Soil Erosion and Conservation, 3rd ed. Blackwell Publ., Oxford, pp. 304 [19] Šúri, M., Cebecauer, T., Hofierka, J., Fulajtár, E., 2002. Erosion Assessment of Slovakia at regional scale using GIS. Ecology 21 (4), 404-422 [20] Cebecauer, T. and Hofierka, J., 2008. The consequences of land-cover changes on soil erosion distribution in Slovakia. Geomorphology 98, 187-198. DOI:10.1016/j.geomorph.2006.12.035 [21] Poesen, J., Torri, D., and Bunte, K., 1994. Effects of rock fragments on soil erosion by water at different spatial scales: a review. Catena 23, 141-166. DOI:10.1016/0341-8162(94)90058-2 [22] Wischmeier, W. H., 1959. A rainfall erosion index for a universal Soil-Loss Equation. Soil Sci. Soc. Amer. Proc. 23, 246-249 [23] Iverson, K. E., 1980. Notation as a tool of thought. Commun. ACM 23 (8), 444-465. DOI:10.1145/358896.358899 [24] Quarteroni, A., Saleri, F., 2006. Scientific Computing with MATLAB and Octave. Texts in Computational Science and Engineering. Milan, Springer-Verlag [25] The MathWorks, 2011. MATLAB. http://www.mathworks.com/help/techdoc/ref/ [26] Eaton, J. W., Bateman, D., and Hauberg, S., 2008. GNU Octave Manual Version 3. A high-level interactive language for numerical computations. Network Theory Limited, ISBN: 0-9546120-6-X [27] de Rigo, D., 2011. Semantic Array Programming with Mastrave - Introduction to Semantic Computational Modeling. The Mastrave project. http://mastrave.org/doc/MTV-1.012-1 [28] de Rigo, D., (exp.) 2012. Semantic array programming for environmental modelling: application of the Mastrave library. In prep. [29] Bosco, C., de Rigo, D., Dewitte, O., Poesen, J., Panagos, P.: Modelling Soil Erosion at European Scale. Towards Harmonization and Reproducibility. In prep. [30] R Development Core Team, 2005. R: A language and environment for statistical computing. R Foundation for Statistical Computing. [31] Stallman, R. M., 2009. Viewpoint: Why “open source” misses the point of free software. Commun. ACM 52 (6), 31–33. DOI:10.1145/1516046.1516058 [32] de Rigo, D. 2011. Multi-dimensional weighted median: the module "wmedian" of the Mastrave modelling library. Mastrave project technical report. http://mastrave.org/doc/mtv_m/wmedian [33] Shakesby, R. A., 2011. Post-wildfire soil erosion in the Mediterranean: Review and future research directions. Earth-Science Reviews 105 (3-4), 71-100. DOI:10.1016/j.earscirev.2011.01.001 [34] Zuazo, V. H., Pleguezuelo, C. R., 2009. Soil-Erosion and runoff prevention by plant covers: A review. In: Lichtfouse, E., Navarrete, M., Debaeke, P Véronique, S., Alberola, C. (Eds.), Sustainable Agriculture. Springer Netherlands, pp. 785-811. DOI:10.1007/978-90-481-2666-8_48

Curriculum Mapping Software Market Outlook

The global curriculum mapping software market size was valued at USD 1.2 billion in 2023 and is expected to reach an estimated USD 3.8 billion by 2032, growing at a CAGR of 13.2% during the forecast period from 2024 to 2032. This significant growth can be attributed to the increasing emphasis on personalized learning experiences, the necessity for compliance with educational standards, and the growing adoption of digital tools in the education sector.

One of the primary growth factors for the curriculum mapping software market is the rising demand for personalized and adaptive learning solutions. Educational institutions are increasingly leveraging technology to design curricula that cater to individual student needs. This shift not only enhances the learning experience but also improves student performance and engagement. Additionally, the ability of curriculum mapping software to help educators identify gaps in the curriculum and align teaching methods with learning objectives contributes significantly to its adoption.

Another driving force behind the market's growth is the increased focus on compliance with educational standards and accreditation requirements. Curriculum mapping software allows institutions to systematically design, implement, and review curricula to ensure they meet the necessary standards and regulations. This capability is particularly crucial for higher education institutions seeking accreditation or re-accreditation, as it provides a clear, organized, and accessible record of curriculum alignment and effectiveness.

The growing integration of data analytics and artificial intelligence in curriculum mapping software also plays a crucial role in market expansion. These technologies enable the software to offer advanced analytics, predictive modeling, and insights, which help educators make informed decisions about curriculum design and instruction. The ability to analyze student performance data and predict learning outcomes can facilitate proactive interventions, thus improving the overall educational experience.

Regionally, North America is expected to dominate the market due to the early adoption of advanced educational technologies, the presence of prominent market players, and substantial government funding for educational innovations. However, the Asia Pacific region is anticipated to exhibit the highest growth rate during the forecast period. Countries such as China, India, and Japan are investing heavily in educational technology to enhance their education systems, driven by the increasing demand for skilled professionals and the need for modernized educational infrastructure.

In addition to curriculum mapping software, educational institutions are increasingly turning to Gradebook Software to streamline their assessment and grading processes. Gradebook Software provides educators with a comprehensive platform to manage student grades, track academic progress, and generate detailed reports. This software not only simplifies the grading process but also enhances transparency and communication between teachers, students, and parents. By integrating Gradebook Software with curriculum mapping tools, institutions can create a cohesive educational ecosystem that supports personalized learning and data-driven decision-making. The growing demand for efficient and user-friendly grading solutions is driving the adoption of Gradebook Software across various educational settings.

Component Analysis

The curriculum mapping software market can be segmented based on components into software and services. The software segment accounts for the largest share of the market, driven by the increasing adoption of digital platforms for curriculum design and management. Educational institutions are recognizing the benefits of using specialized software to streamline and enhance the curriculum development process. This software facilitates the creation, organization, and assessment of curricula, providing educators with tools to align instructional practices with learning objectives effectively.

Within the software segment, the market is further divided into cloud-based and on-premises solutions. Cloud-based curriculum mapping software is gaining significant traction due to its scalability, flexibility, and cost-effectiveness. These solutions allow institutions to access the software from anywhere, at any time, and often come with automatic updates

Purpose: The mission of the Geometric Design Laboratory (GDL) is to support the Office of Safety Research and Development in research related to the geometric design of roadways and the impacts on safety. The GDL provides technical support to develop, maintain, and enhance tools for the safety evaluation of highway geometric design alternatives. This includes coordination of the Highway Safety Manual (HSM) with related tools, e.g., the Interactive Highway Safety Design Model (IHSDM) and SafetyAnalyst. The GDL supports the HSM through implementation of HSM methods in IHSDM software; by providing technical support to HSM users; by performing HSM-related technology facilitation; and by conducting HSM-related training and research.The GDL also contributes to Federal Highway Administration's (FHWA's) Roadway Safety Data Program (RSDP) initiatives to advance State and local safety data systems and safety data analyses by supporting the use of Geographic Information Systems (GIS) for advancing the quantification of highway safety (e.g., through the integration of GIS with highway safety analysis tools); and supports the Safety Training and Analysis Center (STAC) in its mission to assist the research community and State departments of transportation (DOTs) in using data from the second Strategic Highway Research Program's (SHRP2) Naturalistic Driving Study (NDS) and Roadway Information Database (RID).Laboratory Description: GDL staff focuses on the following tasks.Research: Support IHSDM, Highway Safety Manual, and other highway safety-related research efforts.Software Development: Support the full life cycle of IHSDM software development, including developing functional specifications; performing verification and validation of the models that are core IHSDM components; providing recommendations to the IHSDM software developer on all facets of the software (e.g., the graphical user interface, output/reporting); preparing IHSDM documentation; performing alpha testing of IHSDM software; and coordinating the beta testing of IHSDM software by end users. The GDL also helps coordinate the interaction of key players in IHSDM software development, including research contractors, software developers, end users, and commercial computer-aided design (CAD)/roadway design software vendors.Technology Facilitation: Support technology facilitation for the IHSDM and HSM. The GDL provides the sole source of technical support to IHSDM users and provides technical support to HSM users. GDL markets IHSDM and HSM to decisionmakers and potential end users, and participates in developing and delivering IHSDM/HSM training.Laboratory Capabilities: The staff of the GDL includes professionals with expertise in transportation engineering and familiarity with software development, which allows the GDL to support IHSDM development in various ways and to assume a unique coordination role. The GDL's transportation engineering expertise supports the laboratory's function of reviewing and assisting the development of the engineering models included in IHSDM for evaluating the safety of roadway designs. By combining transportation engineering and software development expertise, the GDL has the unique ability to evaluate software from both the software developer and end-user perspective.Communications and engineering skills help GDL staff to understand the needs of the audience (e.g., design engineers), thereby supporting effective technical assistance to end users.IHSDM development is a long-term effort, involving many research contractors, software developers, and FHWA staff. In addition, FHWA seeks input from end users and user organizations to help ensure that IHSDM is responsive to user needs. The staff of the GDL helps coordinate the interaction of all those involved with IHSDM development.Staff at the GDL participates in HSM development and technology facilitation. In addition, the IHSDM Crash Prediction Module is a faithful implementation of HSM Part C (Predictive Method). Therefore, GDL staff is well equipped to support HSM-related activities.Laboratory Equipment: The GDL is equipped with computer hardware and software typically employed by users of IHSDM, including commercial CAD/roadway design software.Laboratory Services: The GDL supports the HSM through implementation of HSM methods in IHSDM software; by providing technical support to HSM users; by performing HSM-related technology facilitation; and by conducting HSM-related research.To develop and promote IHSDM, GDL staff provides or has provided the following services:For all IHSDM safety evaluation modules (Crash Prediction, Design Consistency, Intersection Review, Policy Review, Traffic Analysis and Driver/Vehicle), the GDL conducts software testing to verify, validate, and evaluate the IHSDM software system and develops and/or finalizes the software's functional specifications.Participates in development and delivery of IHSDM training.Provides the sole source of technical assistance to IHSDM users ( ihsdm.support@dot.gov; 202-493-3407).Supports coordination and integration of IHSDM with civil design software packages.Develops, reviews, maintains, and enhances documentation for IHSDM users.Conducts technical reviews and prepares review comments on contract research deliverables.Provides technical support in the development, production, and dissemination of IHSDM-related marketing materials.Provides technical content for the IHSDM Web site.The GDL also contributes to FHWA Roadway Safety Data Program (RSDP) initiatives to advance State and local safety data systems and safety data analyses by supporting the use of GIS for advancing the quantification of highway safety; e.g., through the integration of GIS with highway safety analysis tools (including extraction of data from GIS for input to safety analyses and representation of safety analysis results in the GIS environment). Such contributions support efforts by State and local agencies to:Extract roadway geometrics from GIS/GPS data.Develop GIS-based tools for collecting roadway inventory data.Process data gathered using instrumented vehicles (e.g., LiDAR).Leverage GIS/GPS data for populating safety databases and performing safety analyses (e.g., safety management - HSM Part B, and crash prediction - HSM Part C). The GDL supports the Safety Training and Analysis Center (STAC) in assisting the research community and State DOTs in using data from the SHRP2 Naturalistic Driving Study (NDS) and Roadway Information Database (RID); e.g., by assessing analytical possibilities associated with GIS data linkages to the RID.

Address point data for use with GIS mapping software, databases, and web applications are from Caliper Corporation and contain a point layer of over 48 million addresses in 22 states and the District of Columbia.

Research Questions, Form Questions and Results, and Analysis Results, related to the paper Reproducibility Practices of Software Engineering Controlled Experiments: Survey and Prospective Actions. Insertion of one register missing in the previous version.

The Mapping Software market has evolved significantly over the past decade, emerging as a vital tool across various industries such as transportation, real estate, urban planning, and logistics. As organizations strive for enhanced efficiency and competitive advantage, mapping software provides comprehensive solutio

Replication package of the study "Grey Literature in Software Engineering: A Critical Review" published in Information and Software Technology (IST).

Public Land Survey System (PLSS) Data for use with GIS mapping software, databases, and web applications are from Caliper Corporation and contain boundaries for Townships, First Divisions, and Second Divisions.

Supplemental material about the paper filtering in each phase of the systematic mapping study

Context: Software defect prediction is a trending research topic, and a wide variety of the published papers focus on coding phase or after. A limited number of papers, however, includes the prior (early) phases of the software development lifecycle (SDLC). Objective: The goal of this study is to obtain a general view of the characteristics and usefulness of Early Software Defect Prediction (ESDP) models reported in scientific literature. Method: A systematic mapping and systematic literature review study has been conducted. We searched for the studies reported between 2000 and 2016. We reviewed 52 studies and analyzed the trend and demographics, maturity of state-of-research, in-depth characteristics, success and benefits of ESDP models. Results: We found that categorical models that rely on requirement and design phase metrics, and few continuous models including metrics from requirements phase are very successful. We also found that most studies reported qualitative benefits of using ESDP models. Conclusion: We have highlighted the most preferred prediction methods, metrics, datasets and performance evaluation methods, as well as the addressed SDLC phases. We expect the results will be useful for software teams by guiding them to use early predictors effectively in practice, and for researchers in directing their future efforts.

Abstract

Context: The use of theories in Software Engineering research is not as common or explicit as in other fields,with most studies focusing on technical aspects. However, establishing a more robust theoretical foundation couldsignificantly contribute to the maturation of Software Engineering as a science.

Objective: Therefore, this study investigates the use of theory in software engineering by applying the snowballingtechnique to systematic literature reviews indexed by the main online databases over the last 16 years. It also analyzesthe extent of theory use and what, how, and where these theories are used.

Method: We conducted a systematic mapping study to classify evidence on theory definitions, papers’ quality,research topics, methods, types, theory types, theory roles, and publication venues.

Results: Our results showed that the term “theory” varied among the literature due to inconsistent terminology,thus necessitating a comprehensive approach for accurate identification. Although many theories are cited, only a tinypercentage see repeated application across studies, with limited testing for relevance to practical software engineeringcontexts.

Conclusion: Despite the increase in proposed theories, software engineering requires further attention to mature, asmost papers primarily use theory to justify or motivate experimental research questions. Furthermore, although thereis a diversity of research topics and an adaptation of external theories, only 16% of studies explicitly operationalizetheory, highlighting the need for more intentional theoretical integration and developing SE-specific frameworks.

The Indoor Mapping Software market is experiencing significant growth as organizations increasingly recognize the importance of spatial data in enhancing operational efficiency and user experiences. This software provides a sophisticated solution for visualizing and navigating indoor spaces, whether in large facilit

Riyadh Metro Stations Dataset

Dataset Overview

This dataset contains information about metro stations in Riyadh, Saudi Arabia. It includes details such as station names, types, ratings, and geographic coordinates. The dataset is valuable for transportation analysis, urban planning, and navigation applications.

Dataset Contents

The dataset consists of the following columns:

Potential Use Cases

• Urban Mobility Analysis: Study metro station distribution and accessibility.
• Transportation Planning: Analyze station usage based on ratings and reviews.
• Navigation & Mapping: Enhance public transit applications with station locations.
• Service Optimization: Identify areas needing better metro services.

How to Use

1. Load the dataset into a data analysis tool like Python (pandas), R, or Excel.
2. Filter or group data based on ratings, locations, or number of reviews.
3. Use visualization tools like matplotlib, seaborn, or Power BI for insights.
4. Integrate with GIS software for geospatial mapping.

License & Acknowledgments

• Data sourced from publicly available platforms.
• This dataset is open for non-commercial research and analysis purposes.
• Proper attribution is required when using this dataset in research or publications.

Contact Information

For questions or collaboration, reach out via Kaggle comments or email.