Data Dictionary template for Tempe Open Data.

ACF Agency Wide resource Metadata-only record linking to the original dataset. Open original dataset below.

This template covers section 2.5 Resource Fields: Entity and Attribute Information of the Data Discovery Form cited in the Open Data DC Handbook (2022). It completes documentation elements that are required for publication. Each field column (attribute) in the dataset needs a description clarifying the contents of the column. Data originators are encouraged to enter the code values (domains) of the column to help end-users translate the contents of the column where needed, especially when lookup tables do not exist.

This dataset contains templates of policies and MoU's on data sharing. You can download the Word-templates and adapt the documents to your national context.

Metadata form template for Tempe Open Data.

This repository contains data and code related to experimentation on a comparative evaluation of different template notations for requirements documentation in semi-formal natural language.

Automatically extract critical data from Key Information Documents (DIC) with Koncile's intelligent OCR. Fast structuring, usable formats (Excel, JSON).

Template Manager for Docs Market Outlook

According to our latest research, the global Template Manager for Docs market size in 2024 stands at USD 1.12 billion, with a notable compound annual growth rate (CAGR) of 14.5% projected from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 3.41 billion. This rapid expansion is primarily driven by the increasing demand for automation and standardization in document management processes across various industries, aiming to enhance productivity, ensure compliance, and reduce operational costs. As per our latest research, the Template Manager for Docs market is poised for robust growth, fueled by technological advancements and the rising adoption of digital documentation solutions worldwide.

One of the primary growth drivers for the Template Manager for Docs market is the accelerated digital transformation initiatives across organizations of all sizes. Enterprises are increasingly seeking efficient solutions to manage, standardize, and automate their document creation processes. The proliferation of remote and hybrid work environments has intensified the need for centralized template management systems, ensuring consistency and compliance in documentation regardless of the user’s location. Moreover, as businesses expand globally, the necessity to maintain brand consistency and adhere to regulatory requirements across geographies has become more critical. This has led to a surge in the adoption of sophisticated template management software, which not only streamlines document workflows but also integrates seamlessly with other enterprise systems such as CRM, ERP, and collaboration platforms. The shift towards cloud-based solutions further amplifies this trend, offering scalability, accessibility, and real-time collaboration capabilities, thus fueling market growth.

Another significant factor contributing to the growth of the Template Manager for Docs market is the increasing focus on data security and compliance. With stringent regulations such as GDPR, HIPAA, and others being enforced across various sectors, organizations are under immense pressure to ensure that all documents adhere to prescribed standards and are securely managed. Template managers offer robust permission controls, audit trails, and version management features that help mitigate risks associated with unauthorized access or non-compliance. The BFSI and healthcare sectors, in particular, are witnessing heightened adoption rates due to their sensitive nature of data and the critical need for accurate, compliant documentation. Additionally, the integration of artificial intelligence and machine learning into template management solutions is revolutionizing the way templates are created, updated, and managed, further enhancing efficiency and reducing manual intervention.

Furthermore, the rise of industry-specific use cases is propelling the Template Manager for Docs market forward. In sectors such as education, government, and retail, the volume and diversity of documents generated daily necessitate robust template management systems. Educational institutions are leveraging these solutions to standardize syllabi, certificates, and administrative documents, while government agencies utilize them to ensure uniformity in official communications and policy documents. Retailers, on the other hand, benefit from template managers by maintaining consistency in marketing materials, invoices, and contracts across multiple outlets. The growing trend of integrating template management with business intelligence and analytics platforms is also enabling organizations to gain actionable insights into document usage patterns, further optimizing their operations and driving market growth.

From a regional perspective, North America currently dominates the Template Manager for Docs market, accounting for the largest revenue share in 2024. This leadership is attributed to the region’s advanced IT infrastructure, high adoption rates of cloud-based solutions, and the presence of major industry players. Europe follows closely, driven by stringent regulatory frameworks and a strong focus on digital transformation across industries. The Asia Pacific region is expected to witness the fastest growth during the forecast period, fueled by rapid economic development, increasing internet penetration, and the rising adoption of enterprise software solutions in emerging economies such as China and India. Latin America and the Middle East & Africa are al

Document Template Generation AI Market Outlook

According to our latest research, the global Document Template Generation AI market size reached USD 1.14 billion in 2024, reflecting a robust surge in adoption across diverse industries. The market is expected to expand at a CAGR of 28.6% during the forecast period, reaching an estimated USD 9.82 billion by 2033. This exceptional growth is fueled by increasing digital transformation initiatives, the need for operational efficiency, and the rising demand for automated document workflows across sectors such as legal, healthcare, BFSI, and government.

One of the primary drivers of the Document Template Generation AI market is the relentless pursuit of efficiency and accuracy in document-centric processes. Organizations are increasingly recognizing the value of AI-driven solutions for automating the creation, customization, and management of business documents. With the exponential growth in the volume of documents generated daily, manual template management has become unsustainable, leading enterprises to adopt AI-powered tools that minimize human error, ensure compliance, and significantly reduce turnaround times. The integration of natural language processing (NLP) and machine learning algorithms enables these platforms to understand context, extract relevant data, and generate highly personalized templates, thereby enhancing productivity and consistency across business operations.

Another significant growth factor is the surge in regulatory compliance requirements and the need for secure, auditable documentation. Industries such as BFSI, healthcare, and legal are subject to stringent regulations that demand meticulous record-keeping and standardized documentation. Document Template Generation AI solutions offer advanced features such as version control, audit trails, and real-time collaboration, which are vital for maintaining compliance and data integrity. Moreover, the ability to rapidly adapt templates to evolving regulatory frameworks gives organizations a competitive edge, ensuring they stay ahead of compliance mandates while maintaining operational agility. As regulatory landscapes become increasingly complex, the demand for intelligent document automation solutions is poised to escalate further.

The proliferation of remote and hybrid work models has also played a pivotal role in market expansion. As businesses transition to digital-first environments, the need for seamless and scalable document management solutions has never been greater. Document Template Generation AI platforms, particularly those deployed via the cloud, enable distributed teams to collaborate effortlessly, access up-to-date templates, and automate repetitive documentation tasks from any location. This not only streamlines workflows but also supports business continuity and resilience in the face of disruptions. The scalability and flexibility offered by AI-powered solutions are particularly attractive to small and medium enterprises (SMEs), which seek to optimize resources and drive innovation without significant upfront investments.

From a regional perspective, North America continues to dominate the Document Template Generation AI market, accounting for the largest revenue share in 2024. The region’s leadership is underpinned by the presence of major technology vendors, early adoption of AI-driven solutions, and a mature digital infrastructure. Europe follows closely, driven by robust regulatory frameworks and increasing investments in digital transformation. Meanwhile, the Asia Pacific region is witnessing the fastest growth, propelled by rapid digitization, expanding IT sectors, and supportive government initiatives. As these regional markets evolve, local nuances such as language diversity, regulatory requirements, and industry-specific needs are shaping the adoption patterns of Document Template Generation AI solutions.

Component Analysis

The Document Template Generation AI market is segmented by component into software and services, each playing a critical role in the adoption and implementation of AI-driven document automation. The software segment currently holds the lion’s share of the market, driven by continuous advancements in AI algorithms, natural language generation, and integration capabilities with existing enterprise systems. Modern software solutions offer intuitive interfaces, robust customization tools, and seamless interoperability with popular productivity suites, making th

This data package contains three templates that can be used for creating README files and Issue Templates, written in the markdown language, that support community-led data reporting formats. We created these templates based on the results of a systematic review (see related references) that explored how groups developing data standard documentation use the Version Control platform GitHub, to collaborate on supporting documents. Based on our review of 32 GitHub repositories, we make recommendations for the content of README Files (e.g., provide a user license, indicate how users can contribute) and so 'README_template.md' includes headings for each section. The two issue templates we include ('issue_template_for_all_other_changes.md' and 'issue_template_for_documentation_change.md') can be used in a GitHub repository to help structure user-submitted issues, or can be modified to suit the needs of data standard developers. We used these templates when establishing ESS-DIVE's community space on GitHub (https://github.com/ess-dive-community) that includes documentation for community-led data reporting formats. We also include file-level metadata 'flmd.csv' that describes the contents of each file within this data package. Lastly, the temporal range that we indicate in our metadata is the time range during which we searched for data standards documented on GitHub.

BackgroundTrauma is a significant public health issue that affects both mental and physical health. Healthcare delivery based on trauma-informed care (TIC) principles is designed to mitigate the risk of re-traumatization in healthcare settings to improve patient outcomes. Chronic pain is a common comorbidity of trauma and a common reason that people seek healthcare, including chiropractic care. The extent to which TIC training is integrated into chiropractic education and Doctor of Chiropractic Programs (DCPs) remains unclear.ObjectiveThis study aims to evaluate the presence of TIC principles in educational curricula documents from accredited DCPs across the United States and Canada to identify potential gaps in trauma-sensitive education within chiropractic training.MethodsA scoping document analysis will be conducted using educational curricula documents (program handbooks, course catalogs, and course syllabi) from DCPs accredited by the Council on Chiropractic Education (CCE-USA). Documents will be evaluated for TIC-related search terms based on established frameworks from the Substance Abuse and Mental Health Services Administration and the Harvard Medical School TIC Core Competencies. The analysis will assess the presence of TIC principles such as safety, trust, empowerment, and cultural sensitivity. A phased approach will be used for data extraction, ensuring a comprehensive review of TIC integration.ResultsThe study will quantify the inclusion of TIC principles in chiropractic education in the United States and Canada and identify trends or gaps related to TIC education.ConclusionOur findings can inform future curriculum review and development, ensuring DCPs integrate TIC effectively to enhance care for trauma-exposed patients.

Template Management Software Market Outlook

As per our latest research, the global Template Management Software market size reached USD 2.34 billion in 2024, reflecting robust adoption across diverse industries. The market is anticipated to expand at a CAGR of 13.7% from 2025 to 2033, with the forecasted market size expected to reach USD 7.37 billion by 2033. This impressive growth trajectory is primarily driven by the escalating need for standardized document processes, enhanced workflow automation, and regulatory compliance across enterprises globally.

The growth of the Template Management Software market is strongly influenced by the increasing digitization of business operations and the growing focus on operational efficiency. Organizations are progressively adopting template management solutions to streamline document creation, ensure brand consistency, and minimize manual errors. The rising complexity of business processes, coupled with the need to adhere to industry-specific compliance standards, has further fueled the demand for these solutions. Additionally, the proliferation of remote and hybrid work models has underscored the importance of centralized and cloud-based template management systems, enabling teams to collaborate seamlessly irrespective of geographical boundaries.

Another significant growth factor for the Template Management Software market is the surge in regulatory and compliance requirements, especially in highly regulated industries such as BFSI, healthcare, and government. Organizations must maintain strict control over document templates to ensure compliance with data privacy laws, industry guidelines, and corporate policies. Template management software not only facilitates this control but also automates audit trails, versioning, and access controls, reducing the risk of non-compliance and associated penalties. The growing emphasis on data security and the need to protect sensitive information are compelling enterprises to invest in advanced template management solutions with robust security features.

The market is also experiencing growth due to the increasing integration of template management software with other enterprise applications such as CRM, ERP, and workflow automation tools. This integration enhances the overall value proposition of template management solutions, allowing organizations to automate end-to-end business processes and improve productivity. Furthermore, advancements in artificial intelligence and machine learning are enabling template management platforms to offer intelligent recommendations, real-time analytics, and automated content generation, further driving their adoption across various industry verticals. The ongoing digital transformation initiatives and the rapid evolution of enterprise IT infrastructure are expected to sustain the momentum of the Template Management Software market over the coming years.

From a regional perspective, North America continues to dominate the Template Management Software market, driven by the presence of leading technology vendors, high IT spending, and early adoption of digital solutions. However, the Asia Pacific region is witnessing the fastest growth, fueled by the rapid digitization of businesses, expanding IT infrastructure, and increasing investments in enterprise software solutions. Europe is also a significant market, characterized by stringent regulatory frameworks and a strong focus on data security and compliance. Latin America and the Middle East & Africa are emerging markets, with growing awareness and adoption of template management solutions among enterprises seeking to enhance operational efficiency and competitiveness.

Component Analysis

The Template Management Software market by component is segmented into software and services, each playing a pivotal role in the overall ecosystem. The software segment constitutes the core of the market, encompassing standalone template management platforms and integrated modules within broader enterprise solutions. These software solutions are designed to facilitate the creation, management, and distribution of document templates, ensuring consistency and compliance across organizations. The increasing demand for user-friendly interfaces, advanced customization capabilities, and seamless integration with other business applications is driving innovation within this segment. Vendors are continuously enhancing their offerings with features such as real-time collaboration, AI-po

This workshop is a continuation of the DDI power point presentation given at the previous year's DLI Training in Kingston. It is intended as a primer for those interested in understanding the basic concepts of the Data Documentation Initiative (DDI) and the Data Type Definition (DTD) statements. This time participants will have the opportunity to take a closer look, examine the tags, determine criteria for selection and create an XML template.

1.0 Introduction

Deep Shape From Template Dataset(DSfTD) a multimodal database(depth, registration and rgb data) of recordings synthetically created, monitoring in frontal position, objects being deformed, and it was designed to fulfil the following objetives:

• Allow evaluation and fine tuning of DNN systems in registration, reconstruction and SfT task.
• Provide quality data to the research community in registration, reconstruction and SfT task.

The reconstruction and registration task can also be extended to practical applications such as augmented reality, retail or non invasive surgery.

To give you an idea on what to expect, you can have a look at the following video we prepared from similar data(https://www.youtube.com/watch?v=VvYj-FnuVp0).

2.0 Database Info

FI3S is composed from sequences comprising a broad variety of conditions:

• Thin shell and volumetric templates
• High variety of deformations
• Different level of rotation, translation and light changes over the database objects.

The RGB info is stored in 8 bit images(.png) with each pixel between 0-255 value.

The depth and warps(registration) information is stored in general 16 bit images(.png), with each pixel normalized with three different normalizations, that are provided in the image code example of the database.

File naming conventions:

To ease adapting the experimental setup for specific tasks, we have designed a (verbose) naming conven- tion for the file names and folders.

• The depth images are saved in the folder depth, as images with the name imageX.png
• The RGB images are saved in the folder input, as images with the name imageX_texturaY.png
• The registration images are saved in the folders warpu and warpv, as images with the name imageX.png

Filename extensions: The distributed filenames have an extension of PNG images(.png), to provide an extended and generic use filetipe.

Depht Camera Specifications:

1. The first camera used in our emulations is a Kinect 2 for device, with the following intrinsic parameters:

   cx_K = 947.64 / 4;
   cy_K = 530.38 / 4;
   fy_K = 1064 / 4;
   fx_K = 1057.8 / 4;
   

All the images of the database are resized to 270x480, which imply a resize of the intrinsic parameters too, dividing by a factor of 4.

If you make use of this databases and/or its related documentation, you are kindly requested to cite the paper:

Deep Shape-from-Template: Wide-Baseline, Dense and Fast Registration and Deformable Reconstruction from a Single Image, David Fuentes-Jimenez, David Casillas-Perez, Daniel Pizarro, Toby Collins, Adrien Bartoli, 2018, (https://arxiv.org/abs/1811.07791).

Bibtext: @misc{fuentesjimenez2018deep, title={Deep Shape-from-Template: Wide-Baseline, Dense and Fast Registration and Deformable Reconstruction from a Single Image}, author={David Fuentes-Jimenez and David Casillas-Perez and Daniel Pizarro and Toby Collins and Adrien Bartoli}, year={2018}, eprint={1811.07791}, archivePrefix={arXiv}, primaryClass={cs.CV} }

The dataset consists of 10000 jpg images with white backgrounds, 10000 jpg images with colored backgrounds (the same colors used in the paper) as well as 3x10000 json annotation files. The images are generated from 50 different templates. For each template, 200 images were generated. We provide annotations in three formats: our own original format, the COCO format and a format compatible with HuggingFace Transformers. Background color varies across templates but not across instances from the same template.

In terms of objects, the dataset contains 24 different classes. The classes vary considerably in their numbers of occurrences and thus, the dataset is somewhat imbalanced.

The annotations contain bounding box coordinates, bounding box text and object classes.

We propose two methods for training and evaluating models. The models were trained until convergence ie until the model reaches optimal performance on the validation split and started overfitting. The model version used for evaluation is the one with the best validation performance.

First Evaluation strategy:
For each template, the generated images are randomly split into 3 subsets: training, validation and testing.
In this scenario, the model trains on all templates and is thus tested on new images rather than new layouts.

Second Evaluation strategy:
The real templates are randomly split into a training set, and a common set of templates for validation and testing. All the variants created from the training templates are used as training dataset. The same is done to form the validation and testing datasets. The validation and testing sets are made up of the same templates but of different images.
This approach tests the models' performance on different unseen templates/layouts, rather than the same templates with different content.

We provide the data splits we used for every evaluation scenario. We also provide the background colors we used as augmentation for each template.

This document is a template for states to use when developing the terms and conditions of their title IV-E child welfare waiver demonstration projects.

Metadata-only record linking to the original dataset. Open original dataset below.

Abstract: Numerous business workflows involve printed forms, such as invoices or receipts, which are often manually digitalized to persistently search or store the data. As hardware scanners are costly and inflexible, smartphones are increasingly used for digitalization. Here, processing algorithms need to deal with prevailing environmental factors, such as shadows or crumples. Current state-of-the-art approaches learn supervised image dewarping models based on pairs of raw images and rectification meshes. The available results show promising predictive accuracies for dewarping, but generated errors still lead to sub-optimal information retrieval. In this paper, we explore the potential of improving dewarping models using additional, structured information in the form of invoice templates. We provide two core contributions: (1) a novel dataset, referred to as Inv3D, comprising synthetic and real-world high-resolution invoice images with structural templates, rectification meshes, and a multiplicity of per-pixel supervision signals and (2) a novel image dewarping algorithm, which extends the state-of-the-art approach GeoTr to leverage structural templates using attention. Our extensive evaluation includes an implementation of DewarpNet and shows that exploiting structured templates can improve the performance for image dewarping. We report superior performance for the proposed algorithm on our new benchmark for all metrics, including an improved local distortion of 26.1 %. We made our new dataset and all code publicly available at https://felixhertlein.github.io/inv3d. TechnicalRemarks: Each sample contains the following files: "flat_document.png" (2200x1700x3, uint8, 0-255), showcasing a document in perfect condition. "flat_information_delta.png" displays all texts which represent invoice data (2200x1700x3, uint8, 0-255). "flat_template.png" is an empty invoice template (2200x1700x3, uint8, 0-255). "flat_text_mask.png" visually presents all texts shown in the given document (2200x1700x3, uint8, 0-255). "warped_angle.png" shows warping-induced x- and y-axis angle (1600x1600x2, float32, -Pi to Pi). "warped_albedo.png" is an albedo map (1600x1600x3, uint8, 0-255). "warped_BM.npz" stores backward mapping, i. e. the realtive pixel shift from warped to normalized image for each pixel shifts (1600x1600x2, float32, 0-1). "warped_curvature.npz" has pixel-wise curvature of the warped document (1600x1600x1, float32, 0-inf). "warped_depth.npz" holds per-pixel depth between camera and document (1600x1600x3, float32, 0-inf). "warped_document.png" displays the warped document (1600x1600x3, uint8, 0-255). "warped_normal.npz" contains warped document normals (1600x1600x3, float32, -inf to inf). "warped_recon.png" features a chess-textured warped document (1600x1600x3, uint8, 0-255). "warped_text_mask.npz" is a boolean text pixel mask (1600x1600x1, bool8, True/False). "warped_UV.npz" stores warped texture coordinates (1600x1600x3, float32, 0-1). "warped_WC.npz" includes document coordinates in the 3D space (1600x1600x3, float32, -inf to inf). For more details see https://github.com/FelixHertlein/inv3d-generator. Released under CC BY-NC-SA 4.0. Excluded files are listed in 'restricted-license-files.txt' (located in record with DOI 10.35097/1730, "Inv3D: a high-resolution 3D invoice dataset for template-driven Single-Image Document Unwarping - Metadata"). These are for academic use only.

This is the reporting template for SDG indicator 11.3.2 which UN-Habitat sends to countries on an annual basis to submit the most recent data at the city and national levels. Please click on the [DOWNLOAD] button to get the .xlsx template.Last updated: November 2024

The "cookiecutter-nist-python" template includes all NIST specific branding for creating a pythonpackage. Features include: Testing with pytest. Isolated testing, documentation building, etc, with nox. Linting with pre-commit.Documentation with Sphinx, MyST, using either the furo or sphinx-book-theme theme.Simple commands to upload package to pypi, or a personal conda channel. Simple commands to release documentation to nist-pages.Works with both conda and virtualenv based environments. Handle creation of "requirments.txt" and "environment.yaml" files with pyproject2conda.

A well structured and professional Material Safety Data Sheet Template or MSDS for short, which can be used to store details about specific hazardous checmicals and materials. These sheets are critical for safety across all industries, including construction, cleaning, facilities management and more.