In this seminar, the presenters will introduce essential concepts of Collector for ArcGIS and show how this app integrates with other components of the ArcGIS platform to provide a seamless data management workflow. You will also learn how anyone in your organization can easily capture and update data in the field, right from their smartphone or tablet.This seminar was developed to support the following:ArcGIS Desktop 10.2.2 (Basic)ArcGIS OnlineCollector for ArcGIS (Android) 10.4Collector for ArcGIS (iOS) 10.4Collector for ArcGIS (Windows) 10.4

Field Data Capture Software Market Outlook

According to our latest research, the global Field Data Capture Software market size reached USD 2.41 billion in 2024, with a robust year-over-year growth trajectory. The market is expected to expand at a CAGR of 13.2% during the forecast period, reaching approximately USD 6.98 billion by 2033. This significant growth is propelled by increasing digital transformation initiatives across industries, the proliferation of mobile devices, and the growing need for real-time data collection and analytics in field operations. As organizations strive for operational efficiency, compliance, and enhanced decision-making, the adoption of field data capture software continues to accelerate worldwide.

One of the primary growth drivers for the Field Data Capture Software market is the rising emphasis on data-driven decision-making across sectors such as oil & gas, construction, agriculture, and healthcare. Organizations are increasingly recognizing the value of capturing accurate, real-time data from field operations to streamline workflows, reduce manual errors, and ensure compliance with regulatory requirements. The integration of advanced technologies such as IoT sensors, GPS, and cloud computing into field data capture solutions has significantly improved the quality, accessibility, and security of field data. This technological evolution is enabling businesses to optimize resource allocation, monitor assets remotely, and respond proactively to operational challenges, thereby fueling market growth.

Another critical factor contributing to the expansion of the Field Data Capture Software market is the widespread adoption of mobile devices and cloud-based platforms. As field teams become increasingly mobile, the need for seamless, user-friendly solutions that facilitate data entry, validation, and synchronization has become paramount. Cloud-based field data capture software offers scalability, flexibility, and centralized data management, empowering organizations to deploy solutions rapidly and support remote fieldwork. Furthermore, the ongoing shift toward paperless operations and the demand for sustainability have prompted enterprises to invest in digital tools that minimize paperwork, enhance traceability, and support environmental goals.

The market is also experiencing growth due to regulatory pressures and compliance requirements, particularly in highly regulated industries such as energy, utilities, and healthcare. Governments and industry bodies are mandating stricter reporting, documentation, and audit trails, compelling organizations to adopt robust field data capture solutions. These platforms not only help organizations maintain accurate records but also enable real-time monitoring and reporting, reducing the risk of non-compliance and associated penalties. The ability to customize workflows, automate data validation, and generate instant reports further enhances the appeal of field data capture software, driving its adoption across diverse end-user segments.

Regionally, North America holds the largest share of the Field Data Capture Software market, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the early adoption of advanced technologies, significant investments in digital infrastructure, and the presence of leading software vendors. However, Asia Pacific is anticipated to witness the fastest growth during the forecast period, driven by rapid industrialization, expanding construction activities, and increasing awareness of digital solutions among small and medium enterprises. The region's dynamic economic landscape, coupled with government initiatives to promote digitalization, positions Asia Pacific as a key growth engine for the global market.

Component Analysis

The Component segment of the Field Data Capture Software market is bifurcated into software and services, each playing a pivotal role in the market’s overall growth and adoption. The software segment encompasses a wide range of solutions designed to facilitate on-site data collection, including mobile applications, web-based portals, and integrated platforms that support workflow automation, data validation, and real-time analytics. These solutions have evolved to include features such as offline data capture, customizable forms, and seamless integration with enterprise systems, enabling organizations to tailor their fiel

In this seminar, you will learn how to equip field workers with easy-to-use maps that run on a smartphone or tablet using Collector for ArcGIS, an app included with an ArcGIS Online organizational subscriptions or Portal for ArcGIS. You will see how the maps are used to collect accurate data in the field-even when access to a WiFi connection or cellular service is not available-and quickly share data updates with the organization when connected. You will learn how to help your organization reduce errors, increase productivity, and improve data quality by replacing paper-based workflows with maps that feature data-driven, intelligent forms.This seminar was developed to support the following:ArcGIS OnlineArcGIS Online Organizational AccountUser role or equivalentCollector for ArcGIS (Android) 10.4Collector for ArcGIS (iOS) 10.4Collector for ArcGIS (Windows) 10.4

Field Data Collection App Market Outlook

According to our latest research, the global field data collection app market size reached USD 1.98 billion in 2024, reflecting robust digital transformation across sectors. The market is growing at a compelling CAGR of 14.2% and is forecasted to attain USD 5.28 billion by 2033. This impressive growth trajectory is primarily driven by the increasing need for real-time data capture, enhanced operational efficiency, and the proliferation of mobile devices across industries. As per our 2025 insights, organizations are rapidly adopting field data collection apps to streamline workflows, integrate with cloud infrastructure, and support decision-making with accurate, timely information.

One of the primary growth factors propelling the field data collection app market is the rising emphasis on digital transformation and automation in both public and private sectors. Organizations are increasingly shifting from traditional paper-based methods to digital solutions that enable faster, more accurate, and cost-effective data collection in the field. The ability to capture, validate, and transmit data instantly from remote locations is significantly reducing manual errors and administrative overhead. Moreover, industries such as agriculture, utilities, and construction are leveraging these apps to monitor assets, track resources, and ensure compliance with regulatory standards. The integration of GPS, photo capture, and offline functionality further enhances the utility of these applications, making them indispensable tools in modern field operations.

Another significant driver is the evolution of cloud computing and the widespread availability of affordable mobile devices. Cloud-based deployment models are enabling organizations to centralize data management, facilitate real-time collaboration, and ensure seamless access to critical information regardless of geographical constraints. The scalability and flexibility offered by cloud infrastructure are particularly attractive to small and medium enterprises (SMEs), which can now leverage enterprise-grade solutions without incurring prohibitive costs. Additionally, advancements in mobile technology, including improved battery life, ruggedized devices, and enhanced connectivity, are fostering the adoption of field data collection apps across diverse environments, from remote agricultural fields to urban infrastructure projects.

Data-driven decision-making is also fueling the expansion of the field data collection app market. As organizations recognize the value of actionable insights derived from field data, there is a growing demand for advanced analytics, reporting, and integration capabilities within these applications. The ability to visualize trends, identify anomalies, and generate comprehensive reports in real time is empowering managers to make informed decisions, optimize resource allocation, and improve service delivery. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) into field data collection platforms is enhancing predictive capabilities, automating routine tasks, and enabling proactive maintenance and risk management.

From a regional perspective, North America continues to dominate the field data collection app market, accounting for the largest share due to early technology adoption, significant investments in digital infrastructure, and stringent regulatory requirements. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid urbanization, expanding industrial sectors, and government initiatives promoting digital transformation. Europe is also witnessing substantial growth, particularly in sectors such as utilities, construction, and environmental monitoring. Latin America and the Middle East & Africa are gradually catching up, supported by increasing mobile penetration and the need to modernize legacy systems.

Component Analysis

The field data collection app market is segmented by component into software and services, each playing a critical role in the ecosystem. Software solutions form the backbone of the market, providing the core functionalities for data capture, validation, storage, and analysis. These applications are designed to be user-friendly, customizable, and compatible with a wide range of devices, ensuring seamless adoption across different industries. The evolution of software platforms has led to the integration of advanced features such as geotagging

In this seminar, you will learn how Workforce for ArcGIS helps organizations reduce errors, increase data currency, and improve the entire field-to-office workflow. The presenters will demonstrate how to get started using Workforce for ArcGIS to improve field-office coordination and expedite field data collection workflows.This seminar was developed to support the following:ArcGIS OnlineWorkforce for ArcGIS

Mobile GIS Data Collection Software Market Outlook

According to our latest research, the global mobile GIS data collection software market size reached USD 1.64 billion in 2024. The market is experiencing robust expansion, driven by the increasing demand for real-time geospatial data across industries. The market is projected to grow at a CAGR of 14.2% from 2025 to 2033, reaching a forecasted value of USD 4.46 billion by 2033. This growth is primarily fueled by the widespread adoption of mobile GIS solutions for field data collection, asset management, and environmental monitoring, as organizations seek efficient, accurate, and scalable geospatial data collection tools to enhance operational decision-making.

One of the primary growth factors propelling the mobile GIS data collection software market is the rapid digital transformation occurring across multiple sectors, such as utilities, government, agriculture, and transportation. Organizations are increasingly recognizing the value of real-time geospatial data in optimizing workflows, improving resource allocation, and ensuring regulatory compliance. The integration of mobile GIS solutions with Internet of Things (IoT) devices and advanced sensors enables seamless data capture, transmission, and analysis, empowering field teams to make informed decisions on the go. Furthermore, advancements in mobile hardware and connectivity, such as the proliferation of 5G networks, have significantly enhanced the usability and effectiveness of mobile GIS platforms, making them indispensable tools for field operations.

Another significant driver is the growing emphasis on environmental monitoring and sustainability initiatives worldwide. Governments and private organizations are leveraging mobile GIS data collection software to track environmental parameters, monitor land use changes, and support conservation efforts. The ability to collect, visualize, and analyze spatial data in real time is critical for managing natural resources, assessing environmental risks, and responding to emergencies such as natural disasters or hazardous material spills. As climate change concerns intensify and regulatory frameworks become more stringent, the demand for robust and scalable mobile GIS solutions is expected to rise, further boosting market growth.

The market is also benefiting from the increasing adoption of cloud-based mobile GIS solutions, which offer unparalleled scalability, flexibility, and cost-effectiveness. Cloud deployment enables organizations to centralize data storage, streamline collaboration, and ensure data integrity across geographically dispersed teams. The shift towards Software-as-a-Service (SaaS) models is reducing the upfront costs associated with traditional GIS deployments and making advanced geospatial analytics accessible to small and medium-sized enterprises (SMEs) as well as large corporations. This democratization of GIS technology is expanding the addressable market and fostering innovation in application development, user experience, and integration capabilities.

Regionally, North America remains the dominant market, accounting for the largest revenue share in 2024, driven by high technology adoption, a mature IT infrastructure, and the presence of leading GIS software providers. However, Asia Pacific is emerging as the fastest-growing region, supported by rapid urbanization, infrastructure development, and government initiatives promoting digital transformation. Europe also holds a significant market share, particularly in sectors such as utilities management and environmental monitoring. Meanwhile, Latin America and the Middle East & Africa are witnessing increasing investments in GIS technologies, reflecting the global trend toward smarter, data-driven decision-making across industries.

Component Analysis

The mobile GIS data collection software market is segmented by component into software and services, each playing a pivotal role in driving the adoption and effectiveness of GIS solutions. The software segment encompasses a wide array of applications designed for data capture, visualization, editing, and analysis on mobile devices. These software solutions are increasingly equipped with advanced features such as offline data collection, real-time synchronization, customizable workflows, and integration with third-party systems. The evolution of user-friendly interfaces and mobile-first design principles has further acceler

Explore the dynamic Field Activity Management market: drivers, restraints, industry trends, and growth forecasts from 2025-2033. Discover key insights and regional market share.

Explore the booming Form Automation Software market, driven by efficiency gains and cloud adoption. Discover key insights, market size projections, growth drivers, and leading companies shaping digital workflows globally through 2033.

Mobile GIS Data Collection Software Market Outlook

According to our latest research, the global Mobile GIS Data Collection Software market size reached USD 2.14 billion in 2024, and is anticipated to grow at a robust CAGR of 13.7% during the forecast period, reaching approximately USD 6.42 billion by 2033. This strong growth trajectory is primarily driven by the increasing demand for real-time geospatial data across multiple industries, the proliferation of mobile devices, and the integration of advanced technologies such as IoT and AI into GIS solutions. As organizations globally seek to enhance operational efficiency and decision-making capabilities, the adoption of mobile GIS data collection software continues to accelerate, reshaping the landscape of field data management and spatial analytics.

One of the pivotal growth factors for the Mobile GIS Data Collection Software market is the rapid digital transformation across industries such as utilities, transportation, agriculture, and government. Organizations are increasingly leveraging geospatial data to streamline field operations, optimize resource allocation, and improve asset management. The shift towards digitized workflows has created a surge in demand for mobile GIS solutions that enable real-time data capture, analysis, and sharing from remote locations. Furthermore, the growing emphasis on smart infrastructure and sustainable urban planning has amplified the need for accurate, up-to-date geographic information, positioning mobile GIS software as a critical tool in supporting these initiatives. The convergence of cloud computing, 5G connectivity, and mobile technologies is further enhancing the capabilities and accessibility of GIS platforms, making them indispensable for modern enterprises.

Another significant driver is the increasing adoption of IoT and sensor technologies, which are generating vast volumes of spatial data that require efficient collection, processing, and analysis. Mobile GIS data collection software enables seamless integration with IoT devices, allowing for automated data acquisition and real-time monitoring of assets, environmental conditions, and infrastructure. This capability is particularly valuable in sectors like environmental monitoring, utilities management, and agriculture, where timely and accurate geospatial data is essential for informed decision-making. Additionally, advancements in artificial intelligence and machine learning are empowering GIS software to deliver predictive analytics, anomaly detection, and advanced visualization, further expanding the application scope and value proposition of mobile GIS solutions.

The market is also benefiting from the increasing focus on regulatory compliance and safety standards, particularly in industries such as oil and gas, construction, and transportation. Mobile GIS data collection software facilitates compliance by providing accurate and auditable records of field activities, asset inspections, and environmental assessments. Moreover, the growing need for disaster management, emergency response, and public health surveillance is driving government agencies to invest in robust GIS platforms that support rapid data collection and situational awareness. As a result, vendors are continuously innovating to offer user-friendly, scalable, and secure solutions that cater to the evolving needs of diverse end-users, further fueling market expansion.

The integration of Mobile Mapping System technology into mobile GIS solutions is revolutionizing the way geospatial data is collected and analyzed. By utilizing vehicles equipped with advanced sensors and cameras, Mobile Mapping Systems enable the rapid and accurate capture of geospatial data across large areas. This technology is particularly beneficial for urban planning, infrastructure management, and environmental monitoring, where timely and precise data is crucial. As industries strive to enhance their operational capabilities, the adoption of Mobile Mapping Systems is becoming increasingly prevalent, providing a competitive edge through improved data accuracy and efficiency.

Regionally, North America currently dominates the Mobile GIS Data Collection Software market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The presence of leading technology providers, high adoption rates of digital soluti

BackgroundWith the exponential expansion of clinical trials conducted in (Brazil, Russia, India, and China) and VISTA (Vietnam, Indonesia, South Africa, Turkey, and Argentina) countries, corresponding gains in cost and enrolment efficiency quickly outpace the consonant metrics in traditional countries in North America and European Union. However, questions still remain regarding the quality of data being collected in these countries. We used ethnographic, mapping and computer simulation studies to identify/address areas of threat to near miss events for data quality in two cancer trial sites in Brazil. Methodology/Principal FindingsTwo sites in Sao Paolo and Rio Janeiro were evaluated using ethnographic observations of workflow during subject enrolment and data collection. Emerging themes related to threats to near miss events for data quality were derived from observations. They were then transformed into workflows using UML-AD and modeled using System Dynamics. 139 tasks were observed and mapped through the ethnographic study. The UML-AD detected four major activities in the workflow evaluation of potential research subjects prior to signature of informed consent, visit to obtain subject´s informed consent, regular data collection sessions following study protocol and closure of study protocol for a given project. Field observations pointed to three major emerging themes: (a) lack of standardized process for data registration at source document, (b) multiplicity of data repositories and (c) scarcity of decision support systems at the point of research intervention. Simulation with policy model demonstrates a reduction of the rework problem. Conclusions/SignificancePatterns of threats to data quality at the two sites were similar to the threats reported in the literature for American sites. The clinical trial site managers need to reorganize staff workflow by using information technology more efficiently, establish new standard procedures and manage professionals to reduce near miss events and save time/cost. Clinical trial sponsors should improve relevant support systems.

Field Data Collection Software Market Outlook

As per our latest research, the global field data collection software market size reached USD 2.45 billion in 2024, reflecting a substantial increase driven by digital transformation initiatives across industries. The market is projected to grow at a robust CAGR of 11.2% from 2025 to 2033, with the total market value expected to reach approximately USD 6.52 billion by 2033. This impressive growth trajectory is attributed to the rising demand for real-time data acquisition, improved decision-making capabilities, and the proliferation of mobile technologies enabling seamless data collection in remote and challenging environments.

The primary growth factor for the field data collection software market is the global shift towards digitization and automation in operational workflows. Organizations across sectors such as construction, oil & gas, utilities, and government are increasingly adopting field data collection solutions to replace manual, paper-based methods with digital alternatives. This transition not only enhances productivity but also ensures higher accuracy and faster data processing. The integration of advanced features like geotagging, cloud synchronization, and offline data capture further streamlines field operations, allowing for efficient data management even in areas with limited connectivity. Additionally, the growing emphasis on compliance, safety, and quality assurance is compelling organizations to invest in reliable and scalable field data collection platforms.

Another significant driver is the rapid advancement of mobile devices and wireless connectivity, which has revolutionized the way field data is gathered and transmitted. The widespread use of smartphones and tablets, equipped with sophisticated sensors and GPS capabilities, has empowered field workers to collect, validate, and share data in real time. This has led to a surge in demand for field data collection software that is compatible with various mobile operating systems and can seamlessly integrate with enterprise resource planning (ERP) and geographic information system (GIS) platforms. Furthermore, the advent of cloud computing has enabled organizations to centralize data storage, facilitate collaboration, and ensure secure access to critical information from any location, thereby driving market expansion.

The market’s expansion is also fueled by the increasing need for actionable insights and data-driven decision-making. As industries become more data-centric, the ability to capture, analyze, and visualize field data in real time has become a strategic priority. Field data collection software offers robust analytics and reporting tools that help organizations monitor project progress, identify bottlenecks, and optimize resource allocation. The integration of artificial intelligence (AI) and machine learning (ML) algorithms further enhances the predictive capabilities of these solutions, enabling proactive maintenance, risk mitigation, and improved asset management. This trend is particularly evident in sectors such as agriculture, utilities, and environmental monitoring, where timely and accurate data collection is crucial for operational efficiency and regulatory compliance.

From a regional perspective, North America currently dominates the field data collection software market, accounting for the largest revenue share in 2024, closely followed by Europe and the Asia Pacific. The high adoption rate in North America is attributed to the presence of leading technology providers, advanced IT infrastructure, and a strong focus on innovation across industries. In contrast, the Asia Pacific region is witnessing the fastest growth, driven by rapid urbanization, infrastructure development, and increasing investments in digital technologies by governments and enterprises. Latin America and the Middle East & Africa are also emerging as promising markets, supported by ongoing modernization initiatives and the need for efficient field data management in sectors such as oil & gas, agriculture, and transportation.

Component Analysis

The component segment of the field data collection software market is primarily bifurcated into software and services, each playing a critical role in the overall ecosystem. Software solutions form the backbone of the market, encompassing a wide range of applications designed to facilitate data capture, validation, synchron

Field Data Collection Apps for Civil Engineering Market Outlook

According to our latest research, the Field Data Collection Apps for Civil Engineering market size reached USD 1.45 billion in 2024 and is expected to grow at a robust CAGR of 13.8% during the forecast period, reaching a projected value of USD 4.11 billion by 2033. This dynamic growth is primarily driven by increasing digitalization in the civil engineering sector, the need for real-time data acquisition, and the growing emphasis on project efficiency and compliance. As per our analysis, the market is experiencing accelerated adoption due to the rising demand for accurate field data, streamlined workflows, and integration with advanced analytics platforms.

One of the primary growth factors for the Field Data Collection Apps for Civil Engineering market is the rapid digital transformation across the construction and engineering industries. The adoption of mobile technologies and smart devices on job sites has enabled civil engineers to collect, analyze, and transmit data in real time, significantly reducing manual errors and paperwork. The increasing complexity of civil infrastructure projects, combined with the need for precise data to ensure safety and regulatory compliance, has further fueled the demand for field data collection apps. These solutions empower project teams to collaborate seamlessly, enhance productivity, and maintain up-to-date records, which are essential for timely project delivery and cost control.

Another significant driver is the integration of field data collection apps with other digital platforms such as Building Information Modeling (BIM), Geographic Information Systems (GIS), and cloud-based project management tools. This interoperability allows for the seamless flow of information between field teams and office-based stakeholders, enhancing decision-making and reducing project delays. The ability to capture geospatial data, photographic evidence, and inspection results directly from the field and sync them with centralized databases has become a critical requirement for modern civil engineering projects. Moreover, the increasing emphasis on sustainability and resource optimization is pushing organizations to leverage digital tools that provide actionable insights from field data, further propelling market growth.

The proliferation of government regulations and industry standards mandating accurate documentation and traceability in civil engineering projects is also contributing to the expansion of the Field Data Collection Apps for Civil Engineering market. Regulatory bodies are increasingly requiring project documentation to be digital, auditable, and easily accessible, which has led to widespread adoption of advanced field data collection solutions. Additionally, the rising focus on infrastructure modernization in emerging economies, coupled with substantial investments in smart city initiatives, is creating new growth opportunities. The demand for scalable, customizable, and secure data collection platforms is expected to remain strong as the civil engineering sector continues to embrace digital transformation.

Regionally, North America holds the largest market share in 2024, driven by the presence of leading construction technology providers, high adoption rates of digital tools, and stringent regulatory frameworks. Europe follows closely, with significant investments in infrastructure renewal and sustainability initiatives. The Asia Pacific region is experiencing the fastest growth, fueled by rapid urbanization, government-led infrastructure projects, and increasing awareness of the benefits of digital field data collection. Latin America and the Middle East & Africa are also witnessing steady growth, supported by modernization efforts and the gradual adoption of digital construction practices.

Component Analysis

The Field Data Collection Apps for Civil Engineering market is segmented by component into software and services, each playing a pivotal role in shaping the market landscape. The software segment dominates the market, accounting for the largest revenue share in 2024. This dominance is attributed to the increasing demand for intuitive, feature-rich applications that enable real-time data capture, analysis, and reporting. Modern field data collection software offers functionalities such as offline data entry, GPS integration, photo capture, and automated synchronization with central databases. The continuous evolution

This dataset contains identifiers, metadata, and a map of the locations where field measurements have been conducted at the East River Community Observatory located in the Upper Colorado River Basin, United States. This is version 3.0 of the dataset and replaces the prior version 2.0, which should no longer be used (see below for details on changes between the versions). Dataset description: The East River is the primary field site of the Watershed Function Scientific Focus Area (WFSFA) and the Rocky Mountain Biological Laboratory. Researchers from several institutions generate highly diverse hydrological, biogeochemical, climate, vegetation, geological, remote sensing, and model data at the East River in collaboration with the WFSFA. Thus, the purpose of this dataset is to maintain an inventory of the field locations and instrumentation to provide information on the field activities in the East River and coordinate data collected across different locations, researchers, and institutions. The dataset contains (1) a README file with information on the various files, (2) three csv files describing the metadata collected for each surface point location, plot and region registered with the WFSFA, (3) csv files with metadata and contact information for each surface point location registered with the WFSFA, (4) a csv file with with metadata and contact information for plots, (5) a csv file with metadata for geographic regions and sub-regions within the watershed, (6) a compiled xlsx file with all the data and metadata which can be opened in Microsoft Excel, (7) a kml map of the locations plotted in the watershed which can be opened in Google Earth, (8) a jpeg image of the kml map which can be viewed in any photo viewer, and (9) a zipped file with the registration templates used by the SFA team to collect location metadata. The zipped template file contains two csv files with the blank templates (point and plot), two csv files with instructions for filling out the location templates, and one compiled xlsx file with the instructions and blank templates together. Additionally, the templates in the xlsx include drop down validation for any controlled metadata fields. Persistent location identifiers (Location_ID) are determined by the WFSFA data management team and are used to track data and samples across locations. Dataset uses: This location metadata is used to update the Watershed SFA’s publicly accessible Field Information Portal (an interactive field sampling metadata exploration tool; https://wfsfa-data.lbl.gov/watershed/), the kml map file included in this dataset, and other data management tools internal to the Watershed SFA team. Version Information: The latest version of this dataset publication is version 3.0. The latest version contains a breaking change to the Location Map (EastRiverCommunityObservatory_Map_v3_0_20220613.kml), If you had previously downloaded the map file prior to version 3.0, it will no longer work. Use the updated Location Map (EastRiverCommunityObservatory_Map_v3_0_20220613.kml) in this version of the dataset. This version also contains a total of 51 new point locations, 8 new plot locations, and 1 new geographic region. Additionally, it corrects inconsistencies in existing metadata. Refer to methods for further details on the version history. This dataset will be updated on a periodic basis with new measurement location information. Researchers interested in having their East River measurement locations added in this list should reach out to the WFSFA data management team at wfsfa-data@googlegroups.com. Acknowledgements: Please cite this dataset if using any of the location metadata in other publications or derived products. If using the location metadata for the NEON hyperspectral campaign, additionally cite Chadwick et al. (2020). doi:10.15485/1618130.

he fast growth worldwide of linkable scientific datasets supposes significant challenges in their management and reuse. Large experiments, such as the Latin American Giant Observatory, generate volumes of data that can benefit other kinds of studies. In this sense, there is a modular ecosystem of external radiation tools that should harvest and supply datasets without being part of the main pipeline. Workflows for personal dose estimation, muongraphy in volcanology or mining, or aircraft dose calculations are built with different privacy policies and exploitation licenses. Every numerical method has its own requirements and only parts could make use of the Collaboration’s resources, which implies the convergence with other computing infrastructures. Our work focuses on developing an agnostic methodology to address these challenges while promoting open science. Leveraging the encapsulation of software in nested containers, where the inner layers accomplish specific standardization slices and calculations, the wrapper compiles metadata and data generated and publishes them. All this allows researchers to build a data-driven computer continuum that complies with the findable, accessible, interoperable, and reusable principles. The approach has been successfully tested in the computer-demanding field of radiation-matter interaction with humans, showing the orchestration with the regular pipeline for diverse applications. Moreover, it has been integrated into public or federated cloud environments as well as into local clusters and personal computers to ensure the portability and scalability of the simulations. We postulate that this successful use case can be customized to any other field.

In order to improve the capacity of storage, exploration and processing of sensor data, a spatial DBMS was used and the Aquopts system was implemented.

In field surveys using different sensors on the aquatic environment, the existence of spatial attributes in the dataset is common, motivating the adoption of PostgreSQL and its spatial extension PostGIS. To enable the insertion of new data sets as well as new devices and sensing equipment, the database was modeled to support updates and provide structures for storing all the data collected in the field campaigns in conjunction with other possible future data sources. The database model provides resources to manage spatial and temporal data and allows flexibility to select and filter the dataset.

The data model ensures the storage integrity of the information related to the samplings performed during the field survey in an architecture that benefits the organization and management of the data. However, in addition to the storage specified on the data model, there are several procedures that need to be applied to the data to prepare it for analysis. Some validations are important to identify spurious data that may represent important sources of information about data quality. Other corrections are essential to tweak the data and eliminate undesirable effects. Some equations can be used to produce other factors that can be obtained from the combination of attributes. In general, the processing steps comprise a cycle of important operations that are directly related to the characteristics of the data set. Considering the data of the sensors stored in the database, an interactive prototype system, named Aquopts, was developed to perform the necessary standardization and basic corrections and produce useful data for analysis, according to the correction methods known in the literature.

The system provides resources for the analyst to automate the process of reading, inserting, integrating, interpolating, correcting, and other calculations that are always repeated after exporting field campaign data and producing new data sets. All operations and processing required for data integration and correction have been implemented from the PHP and Python language and are available from a Web interface, which can be accessed from any computer connected to the internet. The data access cab be access online (http://sertie.fct.unesp.br/aquopts), but the resources are restricted by registration and permissions for each user. After their identification, the system evaluates the access permissions and makes available the options of insertion of new datasets.

The source-code of the entire Aquopts system are available at: https://github.com/carmoafc/aquopts

The system and additional results were described on the official paper (under review)

This dataset and code provides radar-based detections of Brazilian free-tailed bats (Tadarida brasiliensis) across select regions of California and Texas, compiled using weather radar data from the NEXRAD (NEXtgeneration weather RADar) system. NEXRAD radars, operated by the US National Weather Service, continuously monitor the airspace, detecting various airborne organisms including birds, insects, and bats. The dataset was generated using the ‘BATS’ Python toolkit (program included), which automates the retrieval, processing, and classification of radar data. It employs a pre-trained machine learning model specifically designed to detect radar echoes associated with Brazilian free-tailed bats. The dataset includes the results from machine learning models trained and tested on radar data, which achieved an AUC of 0.963, demonstrating high accuracy in identifying bat activity. The dataset also includes pre-trained neural network and random forest models for reproducibility. This dataset provides valuable spatiotemporal information on bat presence at a large landscape scale and across extended timeframes. By distilling radar data into efficient summaries of bat occurrence, the dataset enables researchers to explore patterns in bat activity and their potential ecosystem services, such as insect consumption, in agricultural regions.

Methods Data Description This dataset provides detailed radar-based detections of Brazilian free-tailed bats (Tadarida brasiliensis) across select regions of California and Texas. The data were compiled from the NEXRAD (NEXt-generation weather RADar) system, which operates S-band Doppler weather radars across the United States. NEXRAD radars detect various airborne targets such as birds, insects, and bats. The dataset is processed using the 'BATS' Python toolkit, which automates the retrieval and classification of radar data. Using radar data sourced from the Amazon Web Services (AWS) repository, the BATS toolkit classifies radar echoes based on a machine learning model trained to identify Brazilian free-tailed bats. The dataset contains bat presence information at a pixel resolution of 70 meters, derived from radar data over multiple time periods in 2018 and 2019. This data will be useful for researchers exploring bat ecology, insectivorous bat ecosystem services, and landscape-level bat monitoring. The dataset includes:

Radar data processed to detect bat presence in California (2018) and Texas (2019) Classified radar pixels indicating bat presence or absence Machine learning-derived bat occurrence probabilities (thresholded for binary classification) Geotiff files that aggregate radar data over six-month periods

Methods Data Collection The dataset was generated using NEXRAD radar data, sourced from AWS. The BATS Python toolkit facilitated the collection and processing of radar data files, automating the pipeline from raw radar retrieval to bat detection. Radar data was selected based on specific regions, timeframes, and weather conditions associated with confirmed Brazilian free-tailed bat emergence events. The radar data collected spans 11 weather-free days in California (2018) and 7 days in Texas (2019). Reference data on bat emergence was gathered from field observations provided by local bat monitoring organizations. Data Processing Once downloaded, the raw radar data (Level II “.gz” files) was processed using the Py-ART library, which is designed for radar data manipulation. Py-ART converted the radar data from its native polar coordinates into a uniform Cartesian grid, with a resampled pixel resolution of 70 meters to facilitate accurate bat detection. The processed radar data was then classified using a machine learning pipeline. The BATS toolkit includes scripts for classification, in which radar echoes were evaluated by pre-trained machine learning models. The dataset was classified using three machine learning models: random forest (RF), support vector machines (SVM), and artificial neural networks (ANN). The ANN model, selected for its superior performance (AUC of 0.963), was used to classify each radar pixel as either containing or not containing Brazilian free-tailed bats. The model outputs a binary classification based on a 90% probability threshold to ensure accurate detection while minimizing false positives. Evaluation and Quality Control To ensure the accuracy of the model and its classifications, the dataset was evaluated using standard binary classification metrics: precision, recall, AUC (Area Under the ROC Curve), and precision-recall curves. Hyperparameter tuning and spatial cross-validation were performed to account for spatial autocorrelation in the radar data and to improve the generalization of the machine learning models. Training data for the model was primarily sourced from California, while independent testing was conducted using radar data from Texas. The dataset also includes labeled data representing noise sources (such as birds, vehicles, and weather phenomena) to reduce false positives during classification. By processing large volumes of radar data and applying machine learning algorithms, the BATS toolkit condensed terabytes of raw radar data into concise geotiff maps of bat presence, enabling efficient analysis of bat populations across landscapes.

This presentation was given at IDCC 2025 and appended with a structural overview of the proposed network. The subtitle was added for context during publication. Although well-trained staff at the facility can help with research data management requests, they are generally not able to handle all cases orare not provided for in the facilities. This is where domain-specific helpdesks come into play, providing additional expertise. However, eventhese staff soon reach their limits in tricky scenarios resulting from variety of data formats, workflows and available repositories. Agriculturaldata management requests for example may range from large geodata to growth parameters all the way to genetic information and the properhandling of qualitative data. In these cases, it is paramount to be able to rely on a functioning network of associated data stewards who areexperts in their (sub)field of research to engage with expertise requested. In this talk, we will present our concept of associated data stewards:When a request reaches a helpdesk and goes beyond its respective expertise, it is forwarded to a committed data steward, researcher or poolthereof, from another - in this case European - institution to which the request fits thematically, methodologically or locally. The associated datastewards may answer the request and return it, or several data stewards from interlinking fields may discuss and iterate the request. Currently,the Leibniz Centre for Agricultural Landscape Research (ZALF, Germany), the Wageningen Data Competence Center (WDCC, TheNetherlands) and the Swedish University of Agricultural Sciences (SLU, Sweden) are piloting a domain-specific associated data stewardnetwork for agricultural research data management. In this talk, we will highlight the proposed setup and advantages of this network which hasthree important prerequisites: a) a clear definition of the agricultural sub-domains covered by each helpdesk including expertise of each datasteward, b) a mutual understanding of each other's workflows and technical solutions and c) a structured workflow used to distribute requestswhich also re-informs the original receiving helpdesk of the result of the request (monitoring and feedback) - all the while complying with dataprotection regulations. The presentation uses agricultural data as a real-life example but reflects on issues and possible solutions that apply toa variety of research domains.

This repository holds the necessary data and code as well as a descriptive Readme file that was used for our publication "A formative usability study of workflow management systems in label-free digital pathology" by Markus Jelonek et al. (2022), submitted to F1000Research.

Abstract:

We present a formative usability study that investigates the usability of different workflow management systems in the field of biomedical data analysis. Specifically, we study a task in the field of so-called label-free digital pathology and investigate one graphical user interface based workflow and one script-based workflow to solve the task. Our main intention is to gain first insights into the systematic study of usability in the context of biomedical image analysis, and formulate experiences and guidelines for future usability studies dealing with workflow management systems. Embedded in a specific setup dealing with label-free digital pathology, the core question behind our contribution is how usability studies for scientific workflow management can be conducted, and how they can be used systematically to improve such tools. Further, we address specific questions about the resource utilisation and management of usability studies, including the recruitment of participants as well as the design of specific workflows to be investigated.

This resource contains data collected by the Whitefish Lake Institute (WLI) as well as R code used to compile and conduct quality assurance on the data. This resource reflects joint publication efforts between WLI and the Montana State University Extension Water Quality (MSUEWQ) program. All data included here was uploaded to the National Water Quality Portal (WQX) in 2022. It is the intention of WLI to upload all future data to WQX and this HydroShare resource may also be updated in the future with data for 2022 and forward.

Data Purpose: The ‘Data’ folder of this resource holds the final data products for the extensive dataset collected by WLI between 2007 and 2021. This folder is likely of interest to users who want data for research and analysis purposes. This dataset contains physical water parameter field data collected by Hydrolab MS5 and DS5 loggers, including water temperature, specific conductance, dissolved oxygen concentration and saturation, barometric pressure, and turbidity. Additional field data that needs further quality assurance prior to use includes chlorophyll a, ORP, pH, and PAR. This dataset also contains water chemistry data analyzed at certified laboratories including total nitrogen, total phosphorus, nitrate, orthophosphate, total suspended solids, organic carbon, and chlorophyll a. The data folder includes R scripts with code for examples of data visualization. This dataset can provide insight to water quality trends in lakes and streams of northwestern Montana over time. Data Summary: During the time-period, WLI collected water quality data for 63 lake sites and 17 stream and river sites in northwestern Montana under two separate monitoring projects. The Northwest Montana Lakes Network (NMLN) project currently visits 41 lake sites in Northwestern Montana once per summer. Field data from Hydrolabs are collected at discrete depths throughout a lake's profile, and depth integrated water chemistry samples are collected as well. The Whitefish Water Quality Monitoring Project (WWQMP) currently visits two sites on Whitefish Lake, one site on Tally Lake, and 11 stream and river sites in the Whitefish Lake and Upper Whitefish River watersheds monthly between April and November. Field data is collected at one depth for streams and many depths throughout the lake profiles, and water chemistry samples are collected at discrete depths for Whitefish Lake and streams. The final dataset for both programs includes over 112,000 datapoints of data passing quality assurance assessment and an additional 72,000 datapoints that would need further quality assurance before use.

Workflow Purpose: The ‘Workflow’ folder of this resource contains the raw data, folder structure, and R code used during this data compilation and upload process. This folder is likely of interest to users who have similar datasets and are interested in code for automating data compilation or upload processes. The R scripts included here have code to stitch together many individual Hydrolab MS5 and DS5 logger files as well as lab electronic data deliverables (EDDs), which may be useful for users who are interested in compiling one or multiple seasons' worth of data into a single file. Reformatting scripts format data to match the multi-sheet excel workbook format required by the Montana Department of Environmental Quality for uploads to WQX, and may be useful to others hoping to automate database uploads. Workflow Summary: Compilation code in the workflow folder compiles data from its most original forms, including Hydrolab sonde export files and lab EDDs. This compilation process includes extracting dates and times from comment fields and producing a single file from many input files. Formatting code then reformats the data to match WQX upload requirements, which includes generating unique activity IDs for data collected at the same site, date, and time then linking these activity IDs with results across worksheets in an excel workbook. Code for generating all quality assurance figures used in the decision-making process outlined in the Quality Assurance Document and resulting data removal decisions are included here as well. Finally, this folder includes code for combining data from the separate program uploads for WQX to the more user-friendly structure for analysis provided in the 'Data' file for this HydroShare resource.

Digital Sign-Off for Field Tests Market Outlook

According to our latest research, the global Digital Sign-Off for Field Tests market size reached USD 1.92 billion in 2024, reflecting a robust demand for digital transformation solutions across critical industries. The market is expected to grow at a CAGR of 12.7% from 2025 to 2033, reaching a forecasted value of USD 5.66 billion by 2033. This significant growth is primarily driven by the increasing need for real-time, error-free data validation and compliance in field operations, particularly in sectors like telecommunications, energy, and construction. As organizations worldwide strive to enhance operational efficiency and reduce manual errors, the adoption of digital sign-off solutions for field tests is accelerating.

One of the primary growth factors propelling the Digital Sign-Off for Field Tests market is the rapid digitization of field operations. Organizations across various sectors are increasingly leveraging advanced software and hardware to automate and streamline their field testing processes. This transition is reducing the dependency on paper-based workflows, minimizing human errors, and ensuring greater accuracy in data collection and validation. The integration of digital sign-off solutions allows for seamless, real-time communication between field personnel and central management systems, enhancing transparency and traceability. Moreover, the growing emphasis on regulatory compliance and the need to maintain detailed audit trails are making digital sign-off solutions indispensable for organizations aiming to meet stringent industry standards.

Another significant driver for market expansion is the proliferation of IoT devices and mobile technologies in field operations. With the advent of smart sensors, connected devices, and mobile applications, field teams can now capture and transmit test results instantly to centralized platforms. Digital sign-off solutions are increasingly being designed to integrate with these IoT ecosystems, enabling automated data capture, validation, and reporting. This not only accelerates decision-making but also enhances the overall safety and reliability of field operations. Furthermore, the rise in remote and distributed workforces, especially in the wake of global disruptions, has underscored the importance of secure, cloud-based digital sign-off platforms that can be accessed from anywhere, ensuring business continuity and operational resilience.

The market is also witnessing strong growth due to the increasing focus on sustainability and cost reduction. By eliminating paper-based documentation and reducing the need for physical storage, digital sign-off solutions contribute to environmental sustainability initiatives while also lowering operational costs. Organizations are recognizing the long-term value of investing in digital solutions that not only improve efficiency but also support their corporate social responsibility goals. Additionally, the ability to rapidly scale digital sign-off processes across multiple sites and geographies is enabling large enterprises and service providers to achieve greater consistency and quality control in their field operations, further fueling market growth.

Digitaling Software is playing a transformative role in the Digital Sign-Off for Field Tests market by providing innovative solutions that enhance data accuracy and streamline workflows. With the increasing complexity of field operations, organizations are turning to digital platforms that offer seamless integration with existing systems and provide real-time data validation. Digitaling Software's advanced algorithms and user-friendly interfaces are enabling field teams to efficiently capture, verify, and report data, thereby reducing manual errors and improving compliance with industry standards. As the demand for digital transformation continues to rise, Digitaling Software is positioned as a key player in driving operational excellence and supporting the evolving needs of various industries.

From a regional perspective, North America currently leads the Digital Sign-Off for Field Tests market, driven by early technology adoption and the presence of major industry players. However, Asia Pacific is emerging as a high-growth region, supported by massive infrastructure development projects and increas