The Field Data Collection Software market is experiencing robust growth, driven by the increasing need for efficient and accurate data capture across diverse industries. The market's expansion is fueled by several key factors. Firstly, the rising adoption of mobile technologies and cloud computing provides seamless data collection and real-time analysis capabilities, enhancing operational efficiency and decision-making. Secondly, the growing demand for data-driven insights across sectors like construction, oil and gas, and environmental monitoring is pushing organizations to adopt sophisticated field data collection solutions. This trend is further amplified by the increasing focus on safety and compliance regulations, demanding meticulous data recording and analysis for risk mitigation. Furthermore, the integration of advanced features like GPS tracking, image capture, and automated data processing streamlines workflows and minimizes manual errors, thereby improving overall productivity and cost-effectiveness. While initial investment costs can pose a challenge for some businesses, the long-term return on investment in terms of improved efficiency, reduced operational costs, and data-driven decision making is increasingly outweighing the initial expenses. The market's segmented nature, with applications spanning environmental monitoring, construction, oil & gas, and transportation, among others, and various deployment models (cloud-based and on-premises), indicates a wide spectrum of user needs and preferences, opening opportunities for tailored software solutions. The competitive landscape is characterized by a mix of established players and emerging startups offering a range of solutions. While established companies like SafetyCulture and ArcGIS bring experience and extensive feature sets, newer companies are entering with innovative technologies and niche solutions. The market is expected to continue its growth trajectory, driven by technological advancements, increasing data demands across industries, and a growing awareness of the benefits of efficient field data management. The North American and European markets currently hold a significant share, but emerging economies in Asia-Pacific and the Middle East & Africa are expected to witness rapid growth in adoption over the forecast period, largely due to increasing infrastructure development and rising digitization efforts in these regions. The shift towards cloud-based solutions is also a major trend, due to scalability and accessibility advantages over on-premises deployments. This trend is likely to intensify further in the coming years, driven by affordability and convenience.

Use the Attachment Viewer template to provide an app for users to explore a layer's features and review attachments with the option to update attribute data. Present your images, videos, and PDF files collected using ArcGIS Field Maps or ArcGIS Survey123 workflows. Choose an attachment-focused layout to display individual images beside your map or a map-focused layout to highlight your map next to a gallery of images. Examples: Review photos collected during emergency response damage inspections. Display the results of field data collection and support downloading images for inclusion in a report. Present a map of land parcel along with associated documents stored as attachments. Data requirements The Attachment Viewer template requires a feature layer with attachments. It includes the capability to view attachments of a hosted feature service or an ArcGIS Server feature service (10.8 or later). Currently, the app can display JPEG, JPG, PNG, GIF, MP4, QuickTime (.mov), and PDF files in the viewer window. All other attachment types are displayed as a link. Key app capabilities App layout - Choose between an attachment-focused layout, which displays one attachment at a time in the main panel of the app with the map on the side, or a map-focused layout, which displays the map in the main panel of the app with a gallery of attachments. Feature selection - Allows users to select features in the map and view associated attachments. Review data - Enable tools to review and update existing records. Zoom, pan, download images - Allow users to interact with and download attachments. Language switcher - Provide translations for custom text and create a multilingual app. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

Students want to add their own information to the map. Exploring their world through maps is interesting... but there is something magical about seeing a picture you took and an observation you made displayed on a map. And what better place to explore your world than out in it, whether around your school, in your neighborhood, or in your backyard? With the ArcGIS field apps for data collection, young (and old) learners can collect data about the world around them.

This is a video demonstrating how to connect Collector for ArcGIS to an external GNSS receiver.Steps:Connect your mobile device to the external GNSS receiver using bluetooth.Once the connection is successful, open an ArcGIS mobile app for field data collection (e.g., Collector for ArcGIS).Go to Settings, and look for Location setting.Press "Provider", click the add ("+") button, and choose the appropriate external GNSS receiver.You can specify the antenna height, if applicable, and then press "Done".The Collector for ArcGIS can now be used to collect field data by utilising the connected external GNSS receiver.Credits: Anatum GeoMobile Solutions

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

Reporter for MRGPThe Reporter for MRGP doesn't require you to download any apps to complete an inventory; all you need is an internet connection and web browser. The Reporter includes culverts and bridges from VTCULVERTS, town highways from Vtrans and the current status of the MRGP segments and outlets on the map.MRGP Fieldworker SolutionNotes on MRGP fieldworker solution: July 12, 2021. The MRGP map now displays the current status of road segments and outlets. Fieldworkers using the MRGP solution should remove the offline map area(s) from their device, and keep their new offline map current, by syncing their map. Enabling auto-sync will get you the current segment or outlet status automatically. See FAQ section below for more information. Road Erosion Inventory forms are available and have a new look and feel this year. The drainage ditch survey is broken out into three pages for a better user experience. The first page contains survey and segment information, the second; the inventory, and the third; barriers to implementation. You will notice the questions are outlined by section so it’s easier to follow along too. The questions have remained the same. Survey123 has a new option requiring users to update surveys on their mobile device. That option has been enabled for the two MRGP Survey123 forms. Step 1: Download the free mobile appsFor fieldworkers to collect and submit data to VT DEC, two free apps are required: ArcGIS Collector or Field Maps and Survey123. ArcGIS Collector or Field Maps is used first to locate the segment or outlet for inventory, and Survey123, for completing the Road Erosion Inventory. ArcGIS Field Maps is ESRI’s new all-in-one app for field work and will replace ArcGIS Collector. You can download ArcGIS Collector or ArcGIS Fields Maps and Survey123 from the Google Play Store.You can download ArcGIS Collector or ArcGIS Field Maps and Survey123 from Apple Store.

Step 2: Sign into the mobile appYou will need appropriate credentials to access fieldworker solution, please contact your Regional Planning Commission’s Transportation Planner or Jim Ryan (MRGP Program Lead) at (802) 490-6140.Open Collector for ArcGIS, select ‘ArcGIS Online’ as shown below, and enter the user name and password. The credential is saved unless you sign out. Step 3: Open the MRGP Mobile MapIf you’re working in an area that has a reliable data connection (e.g. LTE or 4G), open the map below by selecting it.Step 4: Select a road segment or outlet for inventoryUse your location, button circled in red below, select the segment or outlet you need to inventory, and select 'Update Road Segment Status' from the pop-up to launch Survey123.

Step 5: Complete the Road Erosion Inventory and submit inventory to DECSelecting 'Update Road Segment Status' opens Survey123, downloads the relevant survey and pre-populates the REI with important information for reporting to DEC. You will have to enter the same username and password to access the REI forms. The credential is saved unless you sign out of Survey123.Complete the survey using the appropriate supplement below and submit the assessment directly to VT DEC.Paved Roads with Catch Basin SupplementPaved and Gravel Roads with Drainage Ditches Supplement

Step 6: Repeat!Go back to the ArcGIS Collector or Field Maps and select the next segment for inventory and repeat steps 1-5.

If you have question related to inventory protocol reach out to Jim Ryan, MRGP Program Lead, at jim.ryan@vermont.gov, (802) 490-6140If you have questions about implementing the mobile data collection piece please contact Ryan Knox, ADS-ANR IT, at ryan.knox@vermont.gov, (802) 793-0297

The location where I'm doing inventory does not have a data coverage (LTE or 4G). What can I do?ArcGIS Collector allows you take map areas offline when you think there will be spotty or no data coverage. I made a video to demonstrate the steps for taking map areas offline - https://youtu.be/OEsJrCVT8BISurvey123 operates offline by default but you need to download the survey. My recommendation is to test the fieldworker solution (Steps 1-5) before you go into the field but don't submit the test survey.Where can I download the Road Erosion Scoring shown on the the Atlas? You can download the scoring for both outlets and road segments through the VT Open Geodata Portal.https://geodata.vermont.gov/maps/VTANR::mrgp-scoring-open-data/aboutHow do I use my own ArcGIS Collector map for launching the official MRGP REI survey form? You can use the following custom url for launching Survey123, open the REI and prepopulate answers in the form. More information is here. TIP: add what's below directly in the HTML view of the popup not the link as described in the post I provided.

Hydrologically connected segments (lines):Update Road Segment Status

Segment ID: {SegmentID}
Segment Status: {SegmentStatus}
{RoadName}, {Municipality}
Outlets: {Outlets}
Hydrologically connected outlets (points):Update Outlet Status

Outlet ID: {OutletID}
Municipality: {Municipality}
Erosion: {ErosionValue}

How do I save my name and organization information used in subsequent surveys? Watch this short video or execute the steps below:

Open Survey123 and open a blank REI form (Collect button) Note: it's important to open a blank form so you don't save the same segment id for all your surveys Fill-in your 'Name' and 'Organization' and clear the 'Date of Assessment field' (x button). Using the favorites menu in the top-right corner you can use the current state of your survey to 'Set as favorite answers.' Close survey and 'Save this survey in Drafts.' Use Collector to launch survey from selected feature (segment or outlet). Using the favorites menu again, 'Paste answers from favorite.

What if the map doesn't have the outlet or road segment I need to inventory for the MRGP? Go Directly to Survey123 and complete the appropriate Road Erosion Inventory and submit the data to DEC. The survey includes a Geopoint (location) that we can use to determine where you completed the inventory.

Where can I view the Road Erosion Inventories completed with Survey123? Using the MRGP credentials you have access to another map that shows completed REIs.Web map - Completed Road Erosion Inventories for MRGPWhere can I download the 2020-2021 data collected with Survey123?Road Segments (lines) - https://vtanr.maps.arcgis.com/home/item.html?id=f8a11de8a5a0469596ef11429ab49465Outlets (points) - https://vtanr.maps.arcgis.com/home/item.html?id=ae13a925a662490184d5c5b1b9621672Where can I download the 2019 data collected with Survey123?

Road Segments (lines) - https://vtanr.maps.arcgis.com/home/item.html?id=f60050c6f3c04c60b053470483acb5b1 Outlets (points) - https://vtanr.maps.arcgis.com/home/item.html?id=753006f9ecf144ccac8ce37772bb2c03 Where can I download the 2018 data collected with Survey123?Outlets (points) - https://vtanr.maps.arcgis.com/home/item.html?id=124b617d142e4a1dbcfb78a00e8b9bc5Road Segments (lines) - https://vtanr.maps.arcgis.com/home/item.html?id=8abcc0fcec0441ce8ae6cd38e3812b1b Where can I download the Hydrologically Connected Road Segments and Outlets?Vermont Open Data Geoportal - https://geodata.vermont.gov/datasets/VTANR::hydrologically-connected-road-segments-1/about

This 2019 version of the MRGP Outlets is based on professional mapping completed using DEC's Stormwater Infrastructure dataset. In catch basin systems, work was completed to match outlets to road segments that drain to them. The outlets here correspond to Outlet IDs identified in the Hydrologically connected roads segments layer. For outlets that meet standard, road segments will also meet the standard for MRGP compliance.

The global market for GIS Collectors is experiencing robust growth, driven by increasing adoption of location-based services, the expanding need for precise geospatial data across various sectors, and the continuous advancements in mobile technology and data analytics capabilities. The market is segmented by hardware (handheld devices, tablets, drones) and software (field data collection apps, data management software). Key players like Hexagon, Trimble Geospatial, ESRI, Topcon, Handheld, and Wuhan South are actively innovating and expanding their product portfolios to cater to this growing demand. The market's expansion is further fueled by the rising need for efficient asset management, improved infrastructure planning, and precise mapping for various applications such as environmental monitoring, agriculture, and urban planning. Government initiatives promoting digitalization and smart city development are also contributing significantly to the market's growth trajectory. While high initial investment costs for hardware and software can act as a restraint, the long-term benefits in terms of operational efficiency and data accuracy are overcoming this challenge. We project a steady market growth over the forecast period, with a particular emphasis on the increasing penetration of cloud-based solutions and the integration of AI and machine learning for enhanced data processing and analysis. The period between 2019 and 2024 showed significant market expansion, setting a strong foundation for future growth. We estimate the market size in 2025 at $5 billion, based on observed trends and industry reports. This strong base, coupled with a projected Compound Annual Growth Rate (CAGR) of 12%, will drive considerable market expansion throughout the forecast period (2025-2033). The increasing demand across diverse sectors, from precision agriculture to utility management, will continue to be major drivers. Furthermore, the emergence of new technologies such as 5G and IoT will further enhance data collection and processing capabilities, leading to improved efficiencies and a further expansion of the market. The North American and European markets currently hold a significant share, but emerging economies in Asia-Pacific and Latin America are exhibiting accelerated growth potential, making them crucial regions for future expansion.

This database was designed in response to the Director Memorandum - "Effective January 1, 2019 all structure greater than 120 square feet in the State Responsibility Area (SRA) damaged by wildfire will be inspected and documented in the DINS Collector App."To document and structure damaged or destroyed by the Mosquito wildland fire open the associated Field Map app.NOTE - this feature service is configured to not allow record deletion. If a record needs to be deleted contact the program manager below.This is the schema developed and used by the CAL FIRE Office of State Fire Marshal to assess and record structure damage on wildland fire incidents. The schema is designed to be configured in the Esri Collector/Field Maps app for data collection during or after an incident.

This database was designed in response to the Director Memorandum - "Effective January 1, 2019 all structure greater than 120 square feet in the State Responsibility Area (SRA) damaged by wildfire will be inspected and documented in the DINS Collector App."To document and structure damaged or destroyed by the McKinney wildland fire open the associated Field Map app.NOTE - this feature service is configured to not allow record deletion. If a record needs to be deleted contact the program manager below.This is the schema developed and used by the CAL FIRE Office of State Fire Marshal to assess and record structure damage on wildland fire incidents. The schema is designed to be configured in the Esri Collector/Field Maps app for data collection during or after an incident.

Field Service Mobile Apps Market Outlook

The global field service mobile apps market size was valued at approximately USD 3 billion in 2023 and is anticipated to reach around USD 7.2 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 10.2% during the forecast period. This impressive growth trajectory is fueled primarily by the increasing demand for enhanced operational efficiency and customer satisfaction in field services industries. The adoption of advanced mobile solutions in field services is reshaping traditional business operations, making them more agile, responsive, and customer-centric. As industries such as manufacturing, construction, and utilities increasingly lean on digital transformation strategies, the need for robust mobile applications that streamline field operations is more critical than ever.

One of the primary growth factors driving the field service mobile apps market is the increasing demand for real-time communication and data sharing between field workers and back-office operations. With the proliferation of mobile devices and the integration of IoT technology, field service personnel can now access and share critical information instantaneously, reducing downtime and improving service delivery. Moreover, the emphasis on customer satisfaction and the need for immediate issue resolution have pushed companies to adopt mobile solutions that empower field agents with the tools necessary to deliver superior service. This shift not only enhances the efficiency of field operations but also bolsters the overall customer experience.

Another significant driver is the growing trend towards automation and data-driven decision-making in field services. Field service mobile apps enable businesses to automate scheduling, dispatching, and work order management processes, thus minimizing manual errors and optimizing resource allocation. Additionally, these apps facilitate the collection and analysis of large volumes of field data, providing businesses with valuable insights to improve operational strategies and predict future trends. As industries continue to recognize the benefits of digital solutions, the integration of AI and machine learning into mobile apps is expected to further boost market growth, offering predictive maintenance and advanced analytics capabilities.

The rising demand for cost-effective and scalable solutions among small and medium enterprises (SMEs) is also contributing significantly to the marketÂ’s expansion. SMEs, which often face budget constraints and resource limitations, find mobile apps an attractive option due to their affordability and ability to scale with business growth. These apps offer SMEs the flexibility to manage field operations efficiently, reduce operational costs, and improve service quality, thereby leveling the playing field with larger competitors. As more SMEs embrace digital transformation, the field service mobile apps market is set to witness substantial growth in the coming years.

Field Service Management (FSM) Software plays a crucial role in enhancing the capabilities of field service mobile apps. By integrating FSM software, businesses can achieve seamless coordination between field operations and back-office processes. This integration allows for real-time tracking of field activities, efficient scheduling, and resource allocation, which are essential for maintaining high levels of service quality. FSM software also supports the automation of routine tasks, reducing the administrative burden on field personnel and enabling them to focus on delivering exceptional customer service. As the demand for comprehensive field service solutions grows, the incorporation of FSM software into mobile apps is becoming increasingly vital for businesses looking to optimize their field operations and achieve a competitive edge.

Regionally, North America holds the largest market share in the field service mobile apps market, driven by the early adoption of advanced technologies and a strong focus on enhancing customer satisfaction. The presence of major players and a well-established IT infrastructure further bolster the market in this region. Meanwhile, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, owing to rapid industrialization, increasing smartphone penetration, and a growing focus on digital transformation across various sectors. As these regions continue to invest in digital solutions, the demand for field service mobile apps is poised to rise, offering lucrative opportunities for

This is a video demonstrating how to connect Survey123 for ArcGIS to an external GNSS receiver.Steps:Connect your mobile device to the external GNSS receiver using bluetooth.Once the connection is successful, open an ArcGIS mobile app for field data collection (e.g., Survey123 for ArcGIS).Go to Settings, and look for Location setting.Click "Add Provider" and choose "External receiver".Once your external GNSS receiver is detected, press it and wait until the app establishes the connection.Author: Esri Indonesia Solution Strategist TeamCopyright © 2020 Esri Indonesia. All rights reserved.

This dataset offers a focused and invaluable window into user perceptions and experiences with applications listed on the Apple App Store. It is a vital resource for app developers, product managers, market analysts, and anyone seeking to understand the direct voice of the customer in the dynamic mobile app ecosystem.

Dataset Specifications:

• Investment: $45.0
• Status: Published and immediately available.
• Category: Ratings and Reviews Data
• Format: Compressed ZIP archive containing JSON files, ensuring easy integration into your analytical tools and platforms.
• Volume: Comprises 10,000 unique app reviews, providing a robust sample for qualitative and quantitative analysis of user feedback.
• Timeliness: Last crawled: (This field is blank in your provided info, which means its recency is currently unknown. If this were a real product, specifying this would be critical for its value proposition.)

Richness of Detail (11 Comprehensive Fields):

Each record in this dataset provides a detailed breakdown of a single App Store review, enabling multi-dimensional analysis:

1. Review Content:

   • review: The full text of the user's written feedback, crucial for Natural Language Processing (NLP) to extract themes, sentiment, and common keywords.
   • title: The title given to the review by the user, often summarizing their main point.
   • isEdited: A boolean flag indicating whether the review has been edited by the user since its initial submission. This can be important for tracking evolving sentiment or understanding user behavior.

2. Reviewer & Rating Information:

   • username: The public username of the reviewer, allowing for analysis of engagement patterns from specific users (though not personally identifiable).
   • rating: The star rating (typically 1-5) given by the user, providing a quantifiable measure of satisfaction.

3. App & Origin Context:

   • app_name: The name of the application being reviewed.
   • app_id: A unique identifier for the application within the App Store, enabling direct linking to app details or other datasets.
   • country: The country of the App Store storefront where the review was left, allowing for geographic segmentation of feedback.

4. Metadata & Timestamps:

   • _id: A unique identifier for the specific review record in the dataset.
   • crawled_at: The timestamp indicating when this particular review record was collected by the data provider (Crawl Feeds).
   • date: The original date the review was posted by the user on the App Store.

Expanded Use Cases & Analytical Applications:

This dataset is a goldmine for understanding what users truly think and feel about mobile applications. Here's how it can be leveraged:

• Product Development & Improvement:

  • Bug Detection & Prioritization: Analyze negative review text to identify recurring technical issues, crashes, or bugs, allowing developers to prioritize fixes based on user impact.
  • Feature Requests & Roadmap Prioritization: Extract feature suggestions from positive and neutral review text to inform future product roadmap decisions and develop features users actively desire.
  • User Experience (UX) Enhancement: Understand pain points related to app design, navigation, and overall usability by analyzing common complaints in the review field.
  • Version Impact Analysis: If integrated with app version data, track changes in rating and sentiment after new app updates to assess the effectiveness of bug fixes or new features.

• Market Research & Competitive Intelligence:

  • Competitor Benchmarking: Analyze reviews of competitor apps (if included or combined with similar datasets) to identify their strengths, weaknesses, and user expectations within a specific app category.
  • Market Gap Identification: Discover unmet user needs or features that users desire but are not adequately provided by existing apps.
  • Niche Opportunities: Identify specific use cases or user segments that are underserved based on recurring feedback.

• Marketing & App Store Optimization (ASO):

  • Sentiment Analysis: Perform sentiment analysis on the review and title fields to gauge overall user satisfaction, pinpoint specific positive and negative aspects, and track sentiment shifts over time.
  • Keyword Optimization: Identify frequently used keywords and phrases in reviews to optimize app store listings, improving discoverability and search ranking.
  • Messaging Refinement: Understand how users describe and use the app in their own words, which can inform marketing copy and advertising campaigns.
  • Reputation Management: Monitor rating trends and identify critical reviews quickly to facilitate timely responses and proactive customer engagement.

• Academic & Data Science Research:

  • Natural Language Processing (NLP): The review and title fields are excellent for training and testing NLP models for sentiment analysis, topic modeling, named entity recognition, and text summarization.
  • User Behavior Analysis: Study patterns in rating distribution, isEdited status, and date to understand user engagement and feedback cycles.
  • Cross-Country Comparisons: Analyze country-specific reviews to understand regional differences in app perception, feature preferences, or cultural nuances in feedback.

This App Store Reviews dataset provides a direct, unfiltered conduit to understanding user needs and ultimately driving better app performance and greater user satisfaction. Its structured format and granular detail make it an indispensable asset for data-driven decision-making in the mobile app industry.

This pilot study data was collected to test the feasibility of a new methodological approach that could help to investigate how environmental behaviour (transport behaviour, energy consumption, food consumption, goods consumption, wasting) dilemmas can be overcome on an individual level in real life by using smartphones to collect daily behavioural data in a field-experimental setup. The data includes information on the above-mentioned behaviour based on survey responses, GPS records, barcode scans and electric meter counter images. The data were collected in June 2017 daily over two weeks from 20 study participants of whom 12 were female and 8 male. Moreover, 13 were University students and 7 had a professional background. The two field-experimental interventions were implemented in the second week of data collection and included (1) behavioural targeting (individualised message nudges based on past behaviour) and (2) social monitoring (messages that allowed participants to monitor their own and others' environmental performance). The 20 study participants were randomly and evenly assigned to the two field-experimental interventions. Given the lack of a control group (due to financial limitations to include more study participants), the first week serves as a reference point for assessing treatment effects. Additional to the smartphone-based daily data, basic socio-demographic and attitudinal data were collected through an initial online survey. This data includes information on study participants' gender, age, financial situation and environmental attitudes (e.g. on climate change and recycling). Moreover, a final online survey was conducted after the two-weeks smartphone-based data collection to assess study participants' experience with the study design. The study participants were compensated with a 50 GBP Amazon vouchers for their study participation. This project is a pilot (feasibility) research project to study environmental behaviour (transport behaviour, energy consumption, food consumption, goods consumption, waste production) in real life situations by using smartphones to collect daily behavioural data over two weeks in a field-experimental setup. Demonstrating the feasibility of a novel approach to studying environmental behaviour will enable us to subsequently raise funds for and conduct a major study with additional field-experimental treatments and a larger, more representative sample. For the pilot project, 20 study participants will be recruited among University students and members of staff. They will be assigned to two groups to study to what extent two experimental treatments can alter environmental behaviour: (1) behavioural targeting: study participants' past behaviour will be analysed to deliver individually tailored tips on how they can increase the sustainability of their behaviour, testing nudge theory assumptions; (2) social monitoring: study participants (anonymised) will be able to monitor each other's environmental behaviour through the smartphone application, testing social influence theory assumptions. Data collection will include short survey question responses (e.g. type of transport used and why) on environmental behaviour, GPS coordinates, electric meter data and barcode scans. In the first week, the data will be collected without a field-experimental intervention. In the second week, the 20 study participants will be split into two groups of 10 in order to receive one of the two field-experimental treatments. EpiCollect 5 Smartphone application was used for data collection. The app operated on Android and iOS phones. The data collection fields implemented in the app and used in the project are free text entry (username), multiple choice and single choice responses to survey questions (see questionnaire), images (of electric meter counters, voluntarily), GPS coordinates (voluntarily), barcode scans (voluntarily). The users could collect the data throughout the day and would then upload the data actively to the server in the evening via the EpiCollect 5 app. All data was time-stamped. Furthermore, initial and final online survey data was collected before and after the smartphone-based data collection. The online survey data was collected via Q-set. The initial survey data contains single choice survey responses. The final survey data contains single choice survey responses as well as free text entry data (see questionnaires).

This statistic shows results from a survey conducted in Germany in 2015 among 200 mechanical & plant engineering companies rearding the use of machinery and process data. 88 percent of respondents reported that they used tha data for quality management.

Data:The Web Application shows collected field data between the year 2018 - 2021. The mapped area covers:Silty zone, Sankura woreda: Bercho, Bercho Kulufo, Feten, Jejebicho Seyato, Kore, Kulufo Shegder, Menzo, Menzo Feten, Regdina, Sankura Weteta, Werabe Sinbita kebelesSilty zone, Wera woreda: Laygnawo Bedane, Sorge Dargosa kebeles Halaba zone - woreda Weradijo: Bendo Choloksa, Besheno, Bubissa, Hantazo, Kulufo, Qulibi, Sinbita, Weteta kebelesData were collected by Ethiopian field workers within following projects:"Participatory Development of Productive Landscapes in Sidama Zone, SNNPR, Ethiopia" (2017-2020)"Increasing Ecological stability of Dijo and Bilate Watershed II" - Halaba and Silty zone (2019-2021)Layers:https://arcg.is/1O8ziy0Map tools and it's symbols: Legend widget displays labels and symbols for layers in the map. Layer List widget provides a list of operational layers and their symbols. It allows to turn individual layers on and off.Add Data widget enables to add data to the map by searching layers in ArcGIS content, entering URLs, or uploading local files. The layer is temporarily added and will be removed after closing the web map application. Basemap Gallery widget allows to change background map, or visual context for data in the map. Chart widget displays quantitative attributes from an operational layer as a graphical representation of data. It allows end users to observe possible patterns and trends out of raw data. The operational layer is in the current map -DOPLNITInfographic widget, - ODSRANIT ?? nebo jich tam dat vice??Draw widget allows to draw simple graphics and text on the map. It can also be used add line distance or polygon area to the feature as text.Measurement widget allows to measure the area of a polygon or length of a line, or find the coordinates of a point.Select widget enables you to interactively select features on the map and take actions on the selected features. The selected features can be passed on to other widgets as input, such as the Geoprocessing widget, Attribute Table widget, Directions widget - PRIDAT??, and so on. Share widget allows to share an app by posting it to your social media account, sending an email with a link, or embedding it in a website or blog. It also provides an easy way to define URL parameters for the app.About widget displays information about the Web Map Application, data collection and funding.

These data support poscrptR (wright et al. 2021). poscrptR is a shiny app that predicts the probability of post-fire conifer regeneration for fire data supplied by the user. The predictive model was fit using presence/absence data collected in 4.4m radius plots (60 square meters). Please refer to Stewart et al. (2020) for more details concerning field data collection, the model fitting process, and limitations. Learn more about shiny apps at https://shiny.rstudio.com. The app is designed to simplify the process of predicting post-fire conifer regeneration under different precipitation and seed production scenarios. The app requires the user to upload two input data sets: 1. a raster of Relativized differenced Normalized Burn Ratio (RdNBR), and 2. a .zip folder containing a fire perimeter shapefile. The app was designed to use Rapid Assessment of Vegetative Condition (RAVG) data inputs. The RAVG website (https://fsapps.nwcg.gov/ravg) has both RdNBR and fire perimeter data sets available for all fires with at least 1,000 acres of National Forest land from 2007 to the present. The fire perimeter must be a zipped shapefile (.zip file, include all shapefile components: .cpg, .dbf, .prj, .sbn, .sbx, .shp, and .shx). RdNBR must be 30m resolution, and both the RdNBR and fire perimeter must use the USA Contiguous Albers Equal Area Conic coordinate reference system (USGS version). RDNBR must be alligned (same origin) as RAVG raster data. References: Stewart, J., van Mantgem, P., Young, D., Shive, K., Preisler, H., Das, A., Stephenson, N., Keeley, J., Safford, H., Welch, K., Thorne, J., 2020. Effects of postfire climate and seed availability on postfire conifer regeneration. Ecological Applications. Wright, M.C., Stewart, J.E., van Mantgem, P.J., Young, D.J., Shive, K.L., Preisler, H.K., Das, A.J., Stephenson, N.L., Keeley, J.E., Safford, H.D., Welch, K.R., and Thorne, J.H. 2021. poscrptR. R package version 0.1.3.

Geoform is a configurable app template for form based data editing of a Feature Service. This application allows users to enter data through a form instead of a map's pop-up while leveraging the power of the Web Map and editable Feature Services. This app geo-enables data and workflows by lowering the barrier of entry for completing simple tasks. Use CasesProvides a form-based experience for entering data through a form instead of a map pop-up. This is a good choice for users who find forms a more intuitive format than pop-ups for entering data.Useful to collect new point data from a large audience of non technical staff or members of the community.Configurable OptionsGeoform has an interactive builder used to configure the app in a step-by-step process. Use Geoform to collect new point data and configure it using the following options:Choose a web map and the editable layer(s) to be used for collection.Provide a title, logo image, and form instructions/details.Control and choose what attribute fields will be present in the form. Customize how they appear in the form, the order they appear in, and add hint text.Select from over 15 different layout themes.Choose the display field that will be used for sorting when viewing submitted entries.Enable offline support, social media sharing, default map extent, locate on load, and a basemap toggle button.Choose which locate methods are available in the form, including: current location, search, latitude and longitude, USNG coordinates, MGRS coordinates, and UTM coordinates.Supported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsThis web app includes the capability to edit a hosted feature service or an ArcGIS Server feature service. Creating hosted feature services requires an ArcGIS Online organizational subscription or an ArcGIS Developer account. Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

Field data collection in veterinary and animal behaviour science often faces practical limitations, including time constraints, restricted resources, and difficulties integrating high-quality data capture into real-world clinical workflows. This paper highlights the need for flexible, efficient, and standardised digital solutions that facilitate the collection of multimodal behavioural data in real-world settings. We present a case example using PetsDataLab, a novel cloud-based, “no code” platform designed to enable researchers to create customized apps for efficient and standardised data collection tailored to the behavioural domain, facilitating capture of diverse data types, including video, images, and contextual metadata. We used the platform to develop an app supporting the creation of the Dog Pain Database, a novel comprehensive resource aimed at advancing research on behaviour-based pain indicators in dogs. Using the app, we created a large-scale, structured dataset of dogs with clinically diagnosed conditions expected to be associated with pain and discomfort, including demographic, medical, and pain-related information, alongside high-quality video recordings for future behavioural analyses. To evaluate the app’s usability and its potential for future broader deployment, 14 veterinary professionals tested the app and provided structured feedback via a questionnaire. Results indicated strong usability and clarity, although agreement with using the app in daily clinic life was lower among external testers, pointing to possible barriers to routine integration. This proof-of-concept case study demonstrates the potential of cloud-based platforms like PetsDataLab to bridge research and practice by enabling scalable, standardised, and clinically compatible behavioural data collection. While developed for veterinary pain research, the approach is broadly applicable across behavioural science and supports open science principles through structured, reusable, and interoperable data collection.

This is the data collection Android app for the Improving the Early Diagnosis of Neonatal Sepsis in Malawi study. The app is used to collect vital signs data from neonates at multiple time points. Survey field values can be entered via onscreen keypad and respiratory rate (via tapping while watching breaths) and pulse oximetry (via a connected pulse oximeter) are integrated into the app. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on this page under "collaborate with the pediatric sepsis colab."