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46 results for “mapping tools”
Dataset: Systematic Mapping Study on the Development and Application of Sentiment Analysis Tools in Software Engineering
<p>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".</p> <p>----------</p> <p>Update: The updated version (-v2) contains the results of one more snowballing iteration and extracted information on the accuracy of the used methods.</p> <p>----------</p> <p>In 2020, we conducted a systematic literature review to explore the development and application of sentiment analysis tools in software engineering.</p> <p>Information on the execution of the SLR, its scope, the search string, etc. are presented in the paper linked below.</p> <p> </p> <p> </p>
Database for GWAS SVatalog: a visualization tool to aid fine-mapping of GWAS loci with structural variations.
<p>GWAS SVatalog is a novel visualization tool and database for structural variants (SV) found in a predominantly European population of 101 individuals with Cystic Fibrosis (CF). Aside from the CF-causing variants on chromosome 7 and the LD block in which they lie, the remainder of the genome is comparable to a the 1000 Genomes healthy European population. This data is a collection of SV calls and their linkage disequilibrium (LD) statistics with GWAS-significant SNPs reported in the GWAS Catalog.</p> <p> </p> <p>The goal of this project is to provide a resource to aid fine mapping of GWAS loci using SVs. GWAS loci are generally identified by SNPs which account for an incomplete proportion of genetic variation and phenotypic heritability. Their relevance to the phenotype might be limited, tagging other polymorphisms, such as SVs, that could be the cause of the association signal. To leverage this data to its full potential, visit the <a href="https://svatalog.research.sickkids.ca/" target="_blank" rel="noopener">GWAS SVatalog</a> web tool. Here, interactive visualizations can illustrate SVs identified in high LD with GWAS-significant SNPs, suggesting putative causal variation that could guide additional functional investigation.</p> <p> </p> <p>For more information on how to use GWAS SVatalog, visit the<a href="https://gwas-svatalog-docs.readthedocs.io/en/latest/index.html" target="_blank" rel="noopener noreferrer"> documentation</a>.</p> <p> </p> <p>This project was accomplished in collaboration with the <a href="https://lab.research.sickkids.ca/strug/" target="_blank" rel="noopener">Strug Lab</a> at <a href="https://www.sickkids.ca/en/" target="_blank" rel="noopener">The Hospital for Sick Children (SickKids)</a>, <a href="https://www.tcag.ca/" target="_blank" rel="noopener">The Center for Applied Genomics (TCAG)</a>, and <a href="https://www.utoronto.ca/" target="_blank" rel="noopener">University of Toronto</a>.</p>
Point, polygon, or marker? In search of the best geographic entity for mapping Cultural Ecosystem Services using the online PPGIS tool, "My Green Place."
<p>Excel files include the raw database and the processed data that led to the quadrat analyses. The "Matrix_raw data" file includes the raw data as downloaded from the server. This data was cleaned and organized for its posterior use. "Quadrat analyzes "file includes all the quadrat analyses resulting in each research question in the paper except question four. Question 4 can be seen in the file "Water analysis_Blaarmeersen." All excel files come with a "CODE" tab that describes each of the codes used, their meaning, and ways that were calculated where necessary. Two zip files include all the GIS files. The first one includes the GIS files from which "Matrix_raw data" was built from. The second folder includes the resulting maps from the quadrat analyses. In order to visualize them as in the paper, configure the symbology tab at the GIS software in quantile and the categories number, as shown in the paper.</p> <p>The production of the files in the "GIS_Processed data" folder was done via a repetitive line of commands in ArcGIS pro. The same process was followed for each one of the quadrat analysis described in the paper. Refer to "<a href="https://zenodo.org/api/files/72403e0b-78a9-4bbf-8dca-ed9b8702e3ae/Reproduction%20commands%20and%20parameters.pdf">Reproduction commands and parameters.pdf</a>" for further information.</p>
Excel mapping tool for 2023 avian influenza data reporting
<p>Data collection is an important task of the European Food Safety Authority (EFSA) and a fundamental component of risk assessment (Articles 22 and 23 of Regulation (EC) No 178/2002). EFSA receives a large volume of data from Member States (MSs) that is used in support of its risk assessment mission.</p> <p>Council Directive 2005/94/EC and Commission Decision 2010/367/EU lay down the guidelines for monitoring and reporting avian influenza surveillance data on poultry and wild birds by European Union (EU) MSs. EFSA has been assigned the task of collating, validating, analysing, and summarising in an annual report the results from the avian influenza surveillance programmes in poultry and wild birds. For the reporting of data, EFSA provides a Data Collection Framework that allows data providers to submit data in eXtensible Markup Language (XML) format through a web interface or a web service. Here, a data model describing the format, and the content requested when submitting data through the DCF, is provided.</p> <p>The data model supports the reporting of laboratory testing results for avian influenza in both wild birds, and poultry. The use of this reporting standard ensures that reported results are comparable between reporting countries. Data reported in this format will be used to generate epidemiological updates on the avian influenza surveillance testing carry out in Europe, and to provide scientific advice to the European Commission.</p>
Organizing the fragmented landscape of multidisciplinary product development: A mapping of approaches, processes, methods and tools from the scientific literature - Searchable cartographies
<p>This document gathers cartographies for the development of mechatronic products, cyber-physical systems and smart products. The three cartographies presented are associated with an open-access article – see the citation box below – and differ from the ones provided in the article in that they are searchable, which makes it easier to pinpoint references, concepts and techniques. This document comprises a legend, the cartographies and a list of associated references. </p> <p>To contextualize the cartographies, the integration of digital and connectivity technologies in new products can invite companies to adapt their development. Organizing the fragmented landscape of multidisciplinary product development to help companies navigate the dense scientific literature corpus is a first step in supporting them in doing so. Multidisciplinary product development can be investigated by analyzing specific types of products that deal with both software and hardware development and can be referred to as cyber-physical systems, mechatronics, and smart products and systems in the literature. To support their development, 236 “concepts and techniques” (an expression that encompasses approaches, processes, methods and tools) were identified from 167 scientific papers through an extensive literature review and organized based on a four-level model paired with a decision tree. The mapping of the sorted concepts and techniques made it possible to generate graphical representations called “cartographies.” These cartographies represent a database of concepts and techniques for multidisciplinary product development and serve to support companies in their transformation from the product development perspective by providing them with a general overview of the related literature.</p>
Concept maps as a novel assessment tool in medical education
<p>This dataset accompanies the manuscript entitled "Concept maps as a novel assessment tool in medical education". The dataset contains data from eight participants in a pilot study investigating the use of Concept Maps (CMs) in medical education. Each participant constructed one CM for three different Problem-Based Learning (PBL) cases. Participant CMs, demographic data, results from pre-intervention questionnaire on their learning style (VARK Learning Styles Self-Assessment Questionnaire) and post-intervention questionnaire results measuring the level of clinical and critical thinking are included. In addition expert CMs are also provided for each of the three PBL cases.</p> <p> </p> <p><br> <br> <br> </p>
Excel mapping tools for 2021 AMR data reporting
<p>The main objective of the mapping tools is to provide a simple and useable platform for Member States and other reporting countries to map their country-specific standard terminology to that used by EFSA and to enable the production of an XML file for the submission of antimicrobial resistance data via the Data Collection Framework (DCF).</p> <p>The tools can be used to report antimicrobial resistance data within the framework of Directive 2003/99/EC and Decision 2020/1729/EU.</p> <p>The catalogues and the specific hierarchy of each data model (AMR and ESBL) are already inserted into each of the specific mapping tool. Specific Excel mapping tools corresponding to each of the two data models are available.</p> <p>Dynamic or manual version of the tool can be chosen for each data models.</p>
Replication Data and Analyses for: J. Monsimet, S. Sjögersten, N.J. Sanders, M. Jonsson, J. Olofsson & M. Siewert, 2024. UAV data and deep learning: efficient tools to map the ecological footprint of ants mounds, Remote Sensing in Ecology and Conservation.
<p>This dataset corresponds to the article: <strong>"Jérémy Monsimet*¹, Sofie Sjögersten², Nathan J. Sanders³, Micael Jonsson¹, Johan Olofsson¹, Matthias Siewert¹, 2024. UAV data and deep learning: efficient tools to map the ecological footprint of ants mounds, <em>Remote Sensing in Ecology and Conservation</em>"</strong></p> <p>DOI: <a href="https://doi.org/10.1002/rse2.400" target="_blank" rel="nofollow noreferrer noopener">10.1002/rse2.400</a></p> <p>1 Department of Ecology and Environmental Science, Umeå University, Sweden<br>2 School of Biosciences, University of Nottingham, Loughborough, UK<br>3 Department of Ecology and Evolutionary Biology, University of Michigan, US</p> <p>The gitlab repository of this dataset is available at: <a href="https://gitlab.com/Monsimet/uav_ants_treeline/-/tree/main/">https://gitlab.com/Monsimet/uav_ants_treeline/-/tree/main/</a></p> <p>In this repository, you will find the analyses and results presented in the paper. In each folder, there is a html file that can be read after downloading locally the whole folder. You can either run the .qmd file used to produce the html file or walk through the html files (see the readme.md for more information).</p> <p>Paper abstract:</p> <p>High‐resolution unoccupied aerial vehicle (UAVs) data have alleviated the mismatch between the scale of ecological processes and the scale of remotely sensed data, while machine learning and deep learning methods allow new avenues for quantification in ecology. Ant nests play key roles in ecosystem functioning, yet their distribution and effects on entire landscapes remain poorly understood, in part because they and their mounds are too small for satellite remote sensing. This research maps the distribution and impact of ant mounds in a 20 ha treeline ecotone. We evaluate the detectability from UAV imagery using a deep learning model for object detection and different combinations of RGB, thermal and multispectral sensor data. We were able to detect ant mounds in all imagery using manual detection and deep learning. However, the highest precision rates were achieved by deep learning using RGB data which has the highest spatial resolution (1.9 cm) at comparable UAV flight height. While multispectral data were outperformed for detection, it allows for novel insights into the ecology of ants and their spatial impact on vegetation productivity using the normalized difference vegetation index. Scaling up, this suggests that ant mounds quantifiably impact vegetation productivity for up to 4% of our study area and up to 8% of the<em> Betula nana</em> vegetation communities, the vegetation type with the highest abundance of ant mounds. Therefore, they could have an overlooked role in nutrient‐limited tundra vegetation, and on the shrubification of this habitat. Further, we show the powerful combination UAV multi‐sensor data and deep learning for efficient ecological tracking and monitoring of mound‐building ants and their spatial impact.</p>
MATISE ("Mapping Tool for Innovation Systems Evaluation")
<p>This dataset contains the underlying data for the following publication: Nikas, A., Doukas, H., Lieu, J., Alvarez Tinoco, R., Charisopoulos, V., & van der Gaast, W. (2017). Managing stakeholder knowledge for the evaluation of innovation systems in the face of climate change. Journal of Knowledge Management, 21(5), 1013-1034, ISSN: 1367-3270<strong><em>. </em></strong>Full details of methods used to create the tool are provided within this publication<em>.</em></p>
Software Variability Tools: Industrial Survey and Systematic Mapping Study
<p>Software Variability Tools: Industrial Survey and Systematic Mapping Study</p>
Chromonomer: a tool set for repairing and enhancing assembled genomes through integration of genetic maps and conserved synteny
<p class="BodyAA">The pace of the sequencing and computational assembly of novel reference genomes is accelerating. Though DNA sequencing technologies and assembly software tools continue to improve, biological features of genomes such as repetitive sequence as well as molecular artifacts that often accompany sequencing library preparation can lead to fragmented or chimeric assemblies. If left uncorrected, defects like these trammel progress on understanding genome structure and function, or worse, positively mislead this research. Fortunately, integration of additional, independent streams of information, such as a marker-dense genetic map and conserved orthologous gene order from related taxa, can be used to scaffold together unlinked, disordered fragments and to restructure a reference genome where it is incorrectly joined. We present a tool set for automating these processes, one that additionally tracks any changes to the assembly and to the genetic map, and which allows the user to scrutinize these changes with the help of web-based, graphical visualizations. Chromonomer takes a user-defined reference genome, a map of genetic markers, and, optionally, conserved synteny information to construct an improved reference genome of chromosome models: a "chromonome". We demonstrate Chromonomer's performance on genome assemblies and genetic maps that have disparate characteristics and levels of quality.</p>
Excel mapping tools for 2020 zoonoses data reporting
<p>The main objective of the mapping tools is to provide a simple and useable platform for Member states and other reporting countries to map their country-specific standard terminology to that used by EFSA and to enable the production of an XML file for the submission of sample or aggregated-based zoonoses monitoring data via the Data Collection Framework (DCF).</p> <p>The catalogues and the specific hierarchy of each data model (AMR, ESBL, PRV, FBO, AP, DS and SSD2 for <em>Echinococcus multilocularis</em>) are already inserted into each of the specific mapping tool. Specific Excel mapping tools corresponding to each of the seven data models are available.</p> <p>Dynamic or manual version of the tool can be chosen for the first six data models.</p> <p>The Emulti_SSD2_tool can be used to report sample-based results of <em>Echinococcus multilocularis</em> under the Commission Delegated Regulation (EU) 2018/772.</p>
Excel mapping tools for 2021 zoonoses data reporting
<p>The main objective of the mapping tools is to provide a simple and useable platform for Member states and other reporting countries to map their country-specific standard terminology to that used by EFSA and to enable the production of an XML file for the submission of sample or aggregated-based zoonoses monitoring data via the Data Collection Framework (DCF).</p> <p>The catalogues and the specific hierarchy of each data model (PRV, FBO, AP, DS and SSD2 for <em>Echinococcus multilocularis</em>) are already inserted into each of the specific mapping tool. Specific Excel mapping tools corresponding to each of the five data models are available.</p> <p>Dynamic or manual version of the tool can be chosen for the first four data models.</p> <p>The Emulti_SSD2_tool can be used to report sample-based results of <em>Echinococcus multilocularis</em> under the Commission Delegated Regulation (EU) 2018/772.</p>
Large scale eDNA monitoring of multiple aquatic pathogens as a tool to provide risk maps for wildlife diseases
<p>Multiple parasites and pathogens cause disease in aquatic wildlife and in aquaculture species, generating a need for monitoring and management. Conventional disease monitoring methods involve laborious, costly and invasive capture and examination of host species, and require specialised expertise for every host and pathogen of interest. These restrictions could be alleviated by using pathogen detection techniques based on environmental DNA that provide simultaneous surveys of multiple aquatic pathogens across different host taxa. This would also be valuable for approaches employing parasite diversity as bioindicators of ecosystem disturbance, which suffer from similar restrictions. Here, we tested the potential for simultaneous detection of four wildlife pathogens in water samples from 280, mainly riverine, sites across Switzerland. We targeted the crayfish pathogen <em>Aphanomyces astaci, </em>the amphibian pathogen <em>Batrachochytrium dendrobatidis, </em>and the fish pathogens <em>Saprolegnia parasitica</em> and <em>Tetracapsuloides bryosalmonae</em>. The eDNA detection showed a widespread distribution of <em>A. astaci</em>, <em>S. parasitica</em> and <em>T. bryosalmonae</em>, although <em>A. astaci </em>and <em>T. bryosalmonae</em> were not detected in some alpine river catchments. <em>B. dendrobatidis</em> was detected only rarely, which was expected since the sampling did not target amphibian breeding sites. Co-detection rates were higher in rivers than in lakes, likely reflecting the habitat preferences and distributions of the host species. We discuss the advantages and limitations of eDNA-based pathogen monitoring and list a set of recommendations for managers. Our study illustrates how eDNA-based techniques can monitor several pathogen species concurrently, thus facilitating more comprehensive disease monitoring schemes. Combined with metabarcoding approaches in the future, eDNA based sampling and detection can facilitate the incorporation of parasite and pathogen occurrence and diversity as an indicator for aquatic ecosystem health, and for revealing the hidden biodiversity and structure of parasite communities.</p>
Excel mapping tools for 2017 zoonoses data reporting
<p>The main objective of the mapping tool is to provide a simple and useable platform for MSs to map their country-specific standard terminology to that used by EFSA and to enable the production of an XML file for the submission of sample or aggregated-based zoonoses monitoring data via the DCF.</p> <p>The catalogues and the specific hierarchy of each data model (AMR, ESBL, PRV, FBO, POP and DST) are already inserted into each of the specific mapping tool. Specific Excel mapping tools correspond to each of the six data models are available.</p> <p>You can choose between the dynamic or the manual version of the tool.</p>
Excel mapping tools for 2018 zoonoses data reporting
<p>The main objective of the mapping tool is to provide a simple and useable platform for MSs to map their country-specific standard terminology to that used by EFSA and to enable the production of an XML file for the submission of sample or aggregated-based zoonoses monitoring data via the DCF.</p> <p>The catalogues and the specific hierarchy of each data model (AMR, ESBL, PRV, FBO, POP and DST) are already inserted into each of the specific mapping tool. Specific Excel mapping tools correspond to each of the six data models are available.</p> <p>You can choose between the dynamic or the manual version of the tool.</p>
Excel mapping tool for 2019 avian influenza data reporting
<p>Data collection is an important task of the European Food Safety Authority (EFSA) and a fundamental component of risk assessment (Articles 22 and 23 of Regulation (EC) No 178/2002). EFSA receives a large volume of data from Member States (MSs) that is used in support of its risk assessment mission.</p> <p>Council Directive 2005/94/EC and Commission Decision 2010/367/EU lay down the guidelines for monitoring and reporting avian influenza surveillance data on poultry and wild birds by European Union (EU) MSs. EFSA has been assigned the task of collating, validating, analysing, and summarising in an annual report the results from the avian influenza surveillance programmes in poultry and wild birds. For the reporting of data, EFSA provides a Data Collection Framework that allows data providers to submit data in eXtensible Markup Language (XML) format through a web interface or a web service. Here, a data model describing the format, and the content requested when submitting data through the DCF, is provided.</p> <p>The data model supports the reporting of laboratory testing results for avian influenza in both wild birds, and poultry. The use of this reporting standard ensures that reported results are comparable between reporting countries. Data reported in this format will be used to generate epidemiological updates on the avian influenza surveillance testing carry out in Europe, and to provide scientific advice to the European Commission.</p>
Systematic mapping study on engineering FaaS platforms and tools
<p>A dataset for the systematic mapping study on engineering FaaS platforms and tools.</p>
Excel mapping tool for 2022 avian influenza data reporting
<p>Data collection is an important task of the European Food Safety Authority (EFSA) and a fundamental component of risk assessment (Articles 22 and 23 of Regulation (EC) No 178/2002). EFSA receives a large volume of data from Member States (MSs) that is used in support of its risk assessment mission.</p> <p>Council Directive 2005/94/EC and Commission Decision 2010/367/EU lay down the guidelines for monitoring and reporting avian influenza surveillance data on poultry and wild birds by European Union (EU) MSs. EFSA has been assigned the task of collating, validating, analysing, and summarising in an annual report the results from the avian influenza surveillance programmes in poultry and wild birds. For the reporting of data, EFSA provides a Data Collection Framework that allows data providers to submit data in eXtensible Markup Language (XML) format through a web interface or a web service. Here, a data model describing the format, and the content requested when submitting data through the DCF, is provided.</p> <p>The data model supports the reporting of laboratory testing results for avian influenza in both wild birds, and poultry. The use of this reporting standard ensures that reported results are comparable between reporting countries. Data reported in this format will be used to generate epidemiological updates on the avian influenza surveillance testing carry out in Europe, and to provide scientific advice to the European Commission.</p>
Excel mapping tools for 2022 zoonoses data reporting
<p>The main objective of the mapping tools is to provide a simple and useable platform for Member states and other reporting countries to map their country-specific standard terminology to that used by EFSA and to enable the production of an XML file for the submission of sample or aggregated-based zoonoses monitoring data via the Data Collection Framework (DCF).</p> <p>The catalogues and the specific hierarchy of each data model (AMR, ESBL, PRV, FBO, AP, DS and SSD2 for <em>Echinococcus multilocularis</em>) are already inserted into each of the specific mapping tool. Specific Excel mapping tools corresponding to each of the five data models are available.</p> <p>Dynamic or manual version of the tool can be chosen for the first six data models.</p> <p>The Emulti_SSD2_tool can be used to report sample-based results of <em>Echinococcus multilocularis</em> under the Commission Delegated Regulation (EU) 2018/772.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.