[305 Pages Report] The customer journey mapping software market is anticipated to document a CAGR of 16.3% during the period of 2022 to 2032. The market is anticipated to reach US$ 48.5 Billion in 2032, from US$ 10.7 Billion in 2022.

ZIP Code business counts data for Maptitude mapping software are from Caliper Corporation and contain aggregated ZIP Code Business Patterns (ZBP) data and Rural-Urban Commuting Area (RUCA) data.

Background: There is interest in the use geospatial data for development of acute stroke services given the importance of timely access to acute reperfusion therapy. This paper aims to introduce clinicians and citizen scientists to the possibilities offered by open source softwares (R and Python) for analyzing geospatial data. It is hoped that this introduction will stimulate interest in the field as well as generate ideas for improving stroke services.Method: Instructions on installation of libraries for R and Python, source codes and links to census data are provided in a notebook format to enhance experience with running the software. The code illustrates different aspects of using geospatial analysis: (1) creation of choropleth (thematic) map which depicts estimate of stroke cases per post codes; (2) use of map to help define service regions for rehabilitation after stroke.Results: Choropleth map showing estimate of stroke per post codes and service boundary map for rehabilitation after stroke. Conclusions The examples in this article illustrate the use of a range of components that underpin geospatial analysis. By providing an accessible introduction to these areas, clinicians and researchers can create code to answer clinically relevant questions on topics such as service delivery and service demand.

Census Blocks data for use with GIS mapping software, databases, and web applications are from Caliper Corporation and contain block boundaries with associated 2020 Census demographic data.

MRI Datasets from the GUFI Traveling Heads experiment at 7T.

2 Subjects 10 Sites

The same quantitative imaging protocol at all sites consisting of:

B1 and B0 mapping MP2RAGE QSM CEST Relaxometry

The sites were organized in the German Ultrahigh Field Imaging network (GUFI, www.mr-gufi.de) and discriminate by hard- and software differences of the 7T systems from different generations (same vendor): Configuration 1: Magnet: Passively shielded, Gradient Coil: 38mT/m, RFPA: 8kW, RF Coil: 24ch, Software: VB Datasets: BER_20181211, HEI_20190205 Configuration 2: Magnet: Passively shielded, Gradient Coil: 38mT/m, RFPA: 8kW, RF Coil: 32ch, Software: VB Datasets: ES_20181008, ES_20190813 Configuration 3: Magnet: Passively shielded, Gradient Coil: 70mT/m, RFPA: 8kW, RF Coil: 32ch, Software: VB Datasets: MAG_20190114, LEI_20190115, WIE_20190404 Configuration 4: Magnet: Actively shielded, Gradient Coil: 70mT/m, RFPA: 8kW, RF Coil: 32ch, Software: VB Datasets: BN_20181009 Configuration 5: Magnet: Actively shielded, Gradient Coil: 80mT/m, RFPA: 11kW, RF Coil: 32ch, Software: VE Datasets: ERL_20181019, ERL_20190226, ERL_20190618, JUL_20181212, JUL_20190604, WUE_20190125, WUE_20190617

One full dataset includes:

b0fieldHZ: B0 field mapped in Hz

b1map_mtflash_reg: rel. B1 map registered to the mtflash dataset for B1 correction of relaxometry data

b1rel: rel. B1 map original image space (100*measured flip/nominal flip)

brainmask_mp2rage: brain mask calculated with CBS tools, ANTS and FSL for MP2RAGE data

CEST_NOE: rNOE map derived from the CEST analysis

CEST_APT: APT map derived from the CEST analysis

CEST_MT: MT map derived from the CEST analysis

CEST3D06: CEST image data for B1=0.6uT

CEST3D06: CEST image data for B1=0.9uT

CEST3DWASABI: Correction data for the CEST calculation

gre_qsm: QSM Map calculated from the GRE data in ppB

gre_qsm_mag: Multiecho-GRE magnitude image data for QSM

gre_qsm_phs: Multiecho-GRE phase image data for QSM

mp2rage_inv1: MP2RAGE image data first inversion contrast

mp2rage_inv2: MP2RAGE image data second inversion contrast

mp2rage_T1_corr: MP2RAGE derived T1 map after additional transmit B1 correction with B1 data

mp2rage_T1_gdc_brain: MP2RAGE T1 map after brain extraction and gradient distortion correction (used for inter-site comparisons)

mp2rage_uni_corr: MP2RAGE uniform images after additional transmit B1 correction with B1 data

mp2rage_uni_gdc_brain: MP2RAGE uniform images after brain extraction and gradient distortion correction (used for inter-site comparisons)

mpm_PD: Proton Density map (in %) derived from the multiparametic analysis of the mtflash data

mpm_T1: T1 map (in s) derived from the multiparametic analysis of the mtflash data

mpm_T2s: T2* map (in ms) derived from the multiparametic analysis of the mtflash data

mtflash3dPD: Multiecho FLASH images in PD weighting for multiparametic analysis

mtflash3dT1: Multiecho FLASH images in T1 weighting for multiparametic analysis

Not all data may be available for every measurement.

For further information on the dataset and the methods used for analysis please refer to the corresponding paper: M. N. Voelker et al., “The Traveling Heads 2.0: Multicenter Reproducibility of Quantitative Imaging Methods at 7 Tesla,” Neuroimage, p. 117910, Feb. 2021. https://doi.org/10.1016/j.neuroimage.2021.117910 Please cite if you use the GUFI data!

The first upload (TH2_data_ES_s1.zip) consists of the one full dataset derived at the first measurement at configuration 2 of subject 1 and was intended for the review process (CEST results of this upload were refined during review) of the corresponding paper. The full dataset (TH2_alldata.zip) was uploaded as an update under this project number.

This study aims to investigate and characterize the factors that affect Developer Experience (DX) in Software Ecosystems (SECO). To do so, we reviewed the existing literature on scientific databases and digital libraries to map and analyze the state-of-the-art of DX in software development and SECO. As the main contribution, we provide a set of factors to help organizations, teams, individual software developers, and researchers to have a better understanding of the topic and create a better experience that allows them to promote engagement and other benefits when developing artifacts in SECO or elsewhere areas of Software Engineering.

This is a vector tile service of the fine scale vegetation and habitat map, to be used in web maps and GIS software packages. It is mean to be used in conjunction with the vector tile service that provides labels for each polygon. There is an additional vector tile service that provides hollow outlines for the vegetation map if solid colors are not desired. The Sonoma County fine scale vegetation and habitat map is an 82-class vegetation map of Sonoma County with 212,391 polygons. The fine scale vegetation and habitat map represents the state of the landscape in 2013 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller. The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tD The final report for the fine scale vegetation map, containing methods and an accuracy assessment, is available here: https://sonomaopenspace.egnyte.com/dl/1SWyCSirE9Class definitions, as well as a dichotomous key for the map classes, can be found in the Sonoma Vegetation and Habitat Map Key (https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8) The fine scale vegetation and habitat map was created using semi-automated methods that include field work, computer-based machine learning, and manual aerial photo interpretation. The vegetation and habitat map was developed by first creating a lifeform map, an 18-class map that served as a foundation for the fine-scale map. The lifeform map was created using “expert systems” rulesets in Trimble Ecognition. These rulesets combine automated image segmentation (stand delineation) with object based image classification techniques. In contrast with machine learning approaches, expert systems rulesets are developed heuristically based on the knowledge of experienced image analysts. Key data sets used in the expert systems rulesets for lifeform included: orthophotography (’11 and ’13), the LiDAR derived Canopy Height Model (CHM), and other LiDAR derived landscape metrics. After it was produced using Ecognition, the preliminary lifeform map product was manually edited by photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results. Edits were made to correct two types of errors: 1) unsatisfactory polygon (stand) delineations and 2) incorrect polygon labels. The mapping team used the lifeform map as the foundation for the finer scale and more floristically detailed Fine Scale Vegetation and Habitat map. For example, a single polygon mapped in the lifeform map as forest might be divided into four polygons in the in the fine scale map including redwood forest, Douglas-fir forest, Oregon white oak forest, and bay forest. The fine scale vegetation and habitat map was developed using a semi-automated approach. The approach combines Ecognition segmentation, extensive field data collection, machine learning, manual editing, and expert review. Ecognition segmentation results in a refinement of the lifeform polygons. Field data collection results in a large number of training polygons labeled with their field-validated map class. Machine learning relies on the field collected data as training data and a stack of GIS datasets as predictor variables. The resulting model is used to create automated fine-scale labels countywide. Machine learning algorithms for this project included both Random Forests and Support Vector Machines (SVMs). Machine learning is followed by extensive manual editing, which is used to 1) edit segment (polygon) labels when they are incorrect and 2) edit segment (polygon) shape when necessary. The map classes in the fine scale vegetation and habitat map generally correspond to the alliance level of the National Vegetation Classification, but some map classes - especially riparian vegetation and herbaceous types - correspond to higher levels of the hierarchy (such as group or macrogroup).

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.

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

This meta-analysis compared beat-based musical rhythms to non-beat-based musical rhythms.

Collection description

Maps corresponding to the paper "Identifying a brain network for musical rhythm: A functional neuroimaging meta-analysis and systematic review" . Meta-analyses were conducted in the Seed-based d mapping software (SDM-PSI, version 6.12) All maps in this collection contain corrected 1-p values that were set using threshold-free cluster enhancement (TFCE). Correction for multiple comparisons was achieved through 1000 permutations of subject images to control the familywise error rate (FWER). Maps are unthresholded.

Kasdan, A. V., Burgess, A. N., Pizzagalli, F., Scartozzi, A., Chern, A., Kotz, S. A., Wilson, S.M. & Gordon, R. L. (2022). Identifying a brain network for musical rhythm: A functional neuroimaging meta-analysis and systematic review. Neuroscience & Biobehavioral Reviews, 104588. DOI: https://doi.org/10.1016/j.neubiorev.2022.104588

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

meta-analysis

Cognitive paradigm (task)

music comprehension/production

Map type

IP

This is a vector tile service of the fine scale vegetation and habitat map, to be used in web maps and GIS software packages. It is mean to be used in conjunction with the vector tile service that provides labels for each polygon. There is an additional vector tile service that provides solid colored polygons for the vegetation map if hollow outlines are not desired. The Sonoma County fine scale vegetation and habitat map is an 82-class vegetation map of Sonoma County with 212,391 polygons. The fine scale vegetation and habitat map represents the state of the landscape in 2013 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller. The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tD Class definitions, as well as a dichotomous key for the map classes, can be found in the Sonoma Vegetation and Habitat Map Key (https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8). The fine scale vegetation and habitat map was created using semi-automated methods that include field work, computer-based machine learning, and manual aerial photo interpretation. The vegetation and habitat map was developed by first creating a lifeform map, an 18-class map that served as a foundation for the fine-scale map. The lifeform map was created using “expert systems” rulesets in Trimble Ecognition. These rulesets combine automated image segmentation (stand delineation) with object based image classification techniques. In contrast with machine learning approaches, expert systems rulesets are developed heuristically based on the knowledge of experienced image analysts. Key data sets used in the expert systems rulesets for lifeform included: orthophotography (’11 and ’13), the LiDAR derived Canopy Height Model (CHM), and other LiDAR derived landscape metrics. After it was produced using Ecognition, the preliminary lifeform map product was manually edited by photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results. Edits were made to correct two types of errors: 1) unsatisfactory polygon (stand) delineations and 2) incorrect polygon labels. The mapping team used the lifeform map as the foundation for the finer scale and more floristically detailed Fine Scale Vegetation and Habitat map. For example, a single polygon mapped in the lifeform map as forest might be divided into four polygons in the in the fine scale map including redwood forest, Douglas-fir forest, Oregon white oak forest, and bay forest. The fine scale vegetation and habitat map was developed using a semi-automated approach. The approach combines Ecognition segmentation, extensive field data collection, machine learning, manual editing, and expert review. Ecognition segmentation results in a refinement of the lifeform polygons. Field data collection results in a large number of training polygons labeled with their field-validated map class. Machine learning relies on the field collected data as training data and a stack of GIS datasets as predictor variables. The resulting model is used to create automated fine-scale labels countywide. Machine learning algorithms for this project included both Random Forests and Support Vector Machines (SVMs). Machine learning is followed by extensive manual editing, which is used to 1) edit segment (polygon) labels when they are incorrect and 2) edit segment (polygon) shape when necessary. The map classes in the fine scale vegetation and habitat map generally correspond to the alliance level of the National Vegetation Classification, but some map classes - especially riparian vegetation and herbaceous types - correspond to higher levels of the hierarchy (such as group or macrogroup).

This meta-analysis compared more complex (e.g., syncopated) beat-based musical rhythms to less complex (e.g., isochronous) musical rhythms.

Collection description

Maps corresponding to the paper "Identifying a brain network for musical rhythm: A functional neuroimaging meta-analysis and systematic review" . Meta-analyses were conducted in the Seed-based d mapping software (SDM-PSI, version 6.12) All maps in this collection contain corrected 1-p values that were set using threshold-free cluster enhancement (TFCE). Correction for multiple comparisons was achieved through 1000 permutations of subject images to control the familywise error rate (FWER). Maps are unthresholded.

Kasdan, A. V., Burgess, A. N., Pizzagalli, F., Scartozzi, A., Chern, A., Kotz, S. A., Wilson, S.M. & Gordon, R. L. (2022). Identifying a brain network for musical rhythm: A functional neuroimaging meta-analysis and systematic review. Neuroscience & Biobehavioral Reviews, 104588. DOI: https://doi.org/10.1016/j.neubiorev.2022.104588

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

meta-analysis

Cognitive paradigm (task)

music comprehension/production

Map type

IP

This dataset includes the set of papers retrieved to perform a systematic mapping review (SMR) on multi-paradigm modelling (MPM) for cyber-physical systems (CPS). Moreover, it includes several tables that map the studies under several perspectives, notably used modelling formalisms and processes, part of the CPS addressed by the research, domain of expertise of paper authors, and relevance of the paper at review date. The set of papers is selected over a period ranging from 2006 to 2021, according to publication dates. The selection of the papers and their mapping has been performed by means of a rigorous process based on precise aspects to be evaluated and peer reviewing. Further, the process has been supported by a web-based survey management application. Both the selection of existing publications and their mappings by means of the included perspectives provide interested readers/researches with interesting data possible re-usable for multiple purposes: analysing the progress of research on modelling of CPS, studying further the papers pertaining to a specific (set of) characteristic(s), performing a follow-up study related to other development technologies, just to mention a few.

Conceptual models are an effective and unparalleled means to communicate complicated information with a broad variety of stakeholders in a short period of time. However in practice, conceptual models often vary in clarity, employed features, communicated content, and overall quality. This potentially impacts model comprehension to a point where models are factually useless. To counter this, guidelines to create “good” conceptual models have been suggested. However, these guidelines are often abstract, hard to operationalize in different modeling languages, partly overlap, or even contradict on another. In addition, no comparative study of proposed guidelines exists so far. This is issue is exacerbated as no established metrics to measure model comprehension or to estimate model comprehension for a given conceptual model exist. In this paper, we present the results of a literature survey investigating 109 publications in the field and discuss metrics to measure model comprehension, their quantification, and their empirical substantiation. Results show that albeit a number of concrete, quantifiable metrics to measure and guidelines to achieve model comprehension have been proposed, concrete evaluative recommendations are largely missing. Moreover, some suggested guidelines are contradictory and few metrics exist that allow instantiating common frameworks for model quality in a specific way.

This document contains the references to all 109 included primary studies identified by out search.

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Analysis of ‘Trinity County Land Use Survey 2006’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/cc156390-d8f7-45a5-a7a9-b48065a8326e on 27 January 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

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.

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This review aims to map the current state of knowledge on the epidemiology of rabies in the Amazon region between 2000 and 2023. It will include peer-reviewed articles, case reports, and relevant reviews published in Portuguese, English, or Spanish, conducted in countries within the Amazon Basin or in the states of Brazil's Legal Amazon. The search will be conducted in the Web of Science, SciELO, LILACS, Scopus, and PubMed databases using descriptors related to rabies and the Amazon region. Study selection will occur in two phases: screening of titles/abstracts and full-text review. References will be managed using Mendeley and Rayyan software. Extracted data will include information on authorship, year, study location, target population, and study type, and will be organized in spreadsheets for descriptive analysis. Results will be presented in tables and graphs and disseminated through scientific publications and conferences. This review aims to map the current state of knowledge on the epidemiology of rabies in the Amazon region between 2000 and 2023. It will include peer-reviewed articles, case reports, and relevant reviews published in Portuguese, English, or Spanish, conducted in countries within the Amazon Basin or in the states of Brazil's Legal Amazon. The search will be conducted in the Web of Science, SciELO, LILACS, Scopus, and PubMed databases using descriptors related to rabies and the Amazon region. Study selection will occur in two phases: screening of titles/abstracts and full-text review. References will be managed using Mendeley and Rayyan software. Extracted data will include information on authorship, year, study location, target population, and study type, and will be organized in spreadsheets for descriptive analysis. Results will be presented in tables and graphs and disseminated through scientific publications and conferences.

PCC (people, concept and context) used to identify review questions.

Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process. Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisionaldata sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2006 Trinity County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data was gathered by staff of DWR’s Northern Region using extensive field visits and aerial photography. The land uses that were mapped were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The land use data went through standard quality control procedures before final processing. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters and Northern Region, under the supervision of Tito Cervantes, Senior Land and Water Use Scientist. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Trinity County conducted by the California Department of Water Resources, Northern Region Office staff. Data development: Trinity County was surveyed using the 2005 one-meter resolution National Agriculture Imagery Program (NAIP) digital aerial photos from the U.S. Department of Agriculture's Farm Services Agency as a base for line work. Digital 7.5’ quadrangle sized images were created from the 2005 NAIP imagery. In the spring of 2006, DWR's Northern Region staff digitized land use boundaries using AutoCAD Map software. The digital images and land use boundaries were copied onto laptop computers that were used as the field data collection tools. Staff visited all accessible fields to positively identify agricultural land uses. These site visits occurred between June and August 2006. Land use codes were digitized directly into the laptop computers in the field using AutoCAD Map (using a standardized digitizing process). Some staff took printed aerial photos into the field and wrote land use codes directly onto these photo field sheets. The data from the photo field sheets were digitized using AutoCAD Map back in the office. For both data gathering techniques, any land use boundary changes were noted and then corrected in the office. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using primarily aerial photo interpretation, so some urban areas may have been missed. In some rural residential areas, urban land use was delineated by drawing polygons to surround houses or other buildings along with a minimal area of land surrounding these structures. These footprint areas represent the locations of structures but do not represent the entire footprint of urban land. Sources of irrigation water were not mapped in this survey. The linework and attributes from each AutoCAD drawing file were brought into ArcInfo and both quadrangle and survey-wide coverages were created, and underwent quality checks. The coverages were converted to shapefiles using ArcView. After quality control procedures were completed on each file, the data was finalized. Before final processing, standard quality control procedures were performed jointly by staff at DWR's Northern District, and at DPLA headquarters under the leadership of Jean Woods, Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the 2005 orthorectified NAIP imagery, is approximately 6 meters, but in some areas linework may be 10 meters from the actual location. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

Weekly snapshot of Cleveland City Planning Commission datasets that are featured on the City Planning Zoning Viewer. For the official, most current record of zoning info, use the CPC Zoning Viewer.This file is an open-source geospatial (GIS) format called GeoPackage, which can contain multiple layers. It is similar to Esri's file geodatabase format. Free and open-source GIS software like QGIS, or software like ArcGIS, can read the information to view the tables and map the information.It includes the following mapping layers officially maintained by Cleveland City Planning Commission:Planner Assignment AreasPlanned Unit Development OverlayResidential FacilitiesResidential Facilities 1000 ft. BufferPolice DistrictsLandmarks / Historic LayersLocal Landmark PointsLocal Landmark ParcelsLocal Landmark DistrictsNational Historic DistrictsCentral Business DistrictDesign Review RegionsDesign Review DistrictsOverlay Frontage LinesForm & PRO Overlay DistrictsLive-Work Overlay DistrictsSpecific SetbacksStreet CenterlinesZoningUpdate FrequencyWeekly on Mondays at 4:30 AMContactCity Planning Commission, Zoning & Technology