Web-based Annotation Tool Research Articles

A Minimal Metadata Schema and Its Tool to Improve the Searchableness of Research Data in Bioinformatics

Bioinformatics develops methods to understand biological data and to explain biological processes. The discipline operates on huge data sets and is computationally intensive. The fast growth of the field and its specialization into many subdisciplines makes it hard to search for, find, and to keep track of scientific results relevant for one’s own research. Being able to get knowledge of prior studies, and the data they rely on, is a prerequisite to further one’s own research and to ensure effective progress of bioinformatics as a field. At the same time, scientists’ own research and reputation benefit from the best possible searchableness of their research data. The FAIR data movement draws from this motivation. Before research data can be accessed and reused, however, it must first be found or discovered. For this purpose, the large space of highly diverse research data must be conquered, it must have a high quality of searchableness. To increase searchableness, we have devised a metadata schema to describe the entire field of bioinformatics by a small set of descriptors. Our metadata schema has been inspired by Dublin Core and aims at replicating its success in the domain of bioinformatics. The schema aims at complementing the many metadata schemes used by bioinformaticians in practice by extracting their common core, yielding to a schema that can be used across bioinformatics subdisciplines. Our minimal schema for bioinformatics metadata is complemented by a Web-based annotation tool where such metadata can be provided in an effective, time-saving, and concise manner.

Comparative analysis of manual and annotations for crowd assessment and classification using artificial intelligence

Funding agencies play a pivotal role in bolstering research endeavors by allocating financial resources for data collection and analysis. However, the lack of detailed information regarding the methods employed for data gathering and analysis can obstruct the replication and utilization of the results, ultimately affecting the study’s transparency and integrity. The task of manually annotating extensive datasets demands considerable labor and financial investment, especially when it entails engaging specialized individuals. In our crowd counting study, we employed the web-based annotation tool SuperAnnotate to streamline the human annotation process for a dataset comprising 3,000 images. By integrating automated annotation tools, we realized substantial time efficiencies, as demonstrated by the remarkable achievement of 858,958 annotations. This underscores the significant contribution of such technologies to the efficiency of the annotation process.

MetaTron: advancing biomedical annotation empowering relation annotation and collaboration

BackgroundThe constant growth of biomedical data is accompanied by the need for new methodologies to effectively and efficiently extract machine-readable knowledge for training and testing purposes. A crucial aspect in this regard is creating large, often manually or semi-manually, annotated corpora vital for developing effective and efficient methods for tasks like relation extraction, topic recognition, and entity linking. However, manual annotation is expensive and time-consuming especially if not assisted by interactive, intuitive, and collaborative computer-aided tools. To support healthcare experts in the annotation process and foster annotated corpora creation, we present MetaTron. MetaTron is an open-source and free-to-use web-based annotation tool to annotate biomedical data interactively and collaboratively; it supports both mention-level and document-level annotations also integrating automatic built-in predictions. Moreover, MetaTron enables relation annotation with the support of ontologies, functionalities often overlooked by off-the-shelf annotation tools.ResultsWe conducted a qualitative analysis to compare MetaTron with a set of manual annotation tools including TeamTat, INCEpTION, LightTag, MedTAG, and brat, on three sets of criteria: technical, data, and functional. A quantitative evaluation allowed us to assess MetaTron performances in terms of time and number of clicks to annotate a set of documents. The results indicated that MetaTron fulfills almost all the selected criteria and achieves the best performances.ConclusionsMetaTron stands out as one of the few annotation tools targeting the biomedical domain supporting the annotation of relations, and fully customizable with documents in several formats—PDF included, as well as abstracts retrieved from PubMed, Semantic Scholar, and OpenAIRE. To meet any user need, we released MetaTron both as an online instance and as a Docker image locally deployable.

Utilizing the Autonomous Reef Monitoring Structure (ARMS) to study the temporal variation of benthic community on coral reef ecosystems in Pemuteran, Bali, Indonesia

Tracking changes in community composition in the coral reef ecosystem is essential, mainly to understand the complexities of the ecosystem and its long-term response to environmental and anthropogenic threats. A standardized method sensitive to changes and comparable across sites is needed to provide robust data helpfully. We used a standardized monitoring protocol Autonomous Reef Monitoring Structure (ARMS), to investigate the growth of marine sessile communities and observe variations in motile taxa (decapods) composition through time. Eighteen units of ARMS were deployed at a depth of ∼10–15 m in the coral reef ecosystem in Pemuteran, Bali, Indonesia; three units were then recovered every two months for one year. The sessile community in each ARMS plate was then photographed and annotated using a web-based annotation tool called CoralNet, while the motile decapods were identified using their morphological characteristics. We observed twenty-four benthic categories of sessile communities and three infraorder of decapods across the sampling duration. The sessile community was contributed mainly by Encrusting Sponge, Brown Flesh Algae, and Bryozoan in almost every sampling duration. The ANOVA result shows that the composition is significantly different over time, showing apparent changes in composition. However, the NMDS result indicated similar structure composition during the ARMS’s time retrieval. Future research on temporal changes in benthic diversity can be used to understand the dynamic more about relationship between motile and sessile and how their interaction can affect the coral reef community.

Modelling digital health data: The ExaMode ontology for computational pathology

Computational pathology can significantly benefit from ontologies to standardize the employed nomenclature and help with knowledge extraction processes for high-quality annotated image datasets. The end goal is to reach a shared model for digital pathology to overcome data variability and integration problems. Indeed, data annotation in such a specific domain is still an unsolved challenge and datasets cannot be steadily reused in diverse contexts due to heterogeneity issues of the adopted labels, multilingualism, and different clinical practices. Material and methodsThis paper presents the ExaMode ontology, modeling the histopathology process by considering 3 key cancer diseases (colon, cervical, and lung tumors) and celiac disease. The ExaMode ontology has been designed bottom-up in an iterative fashion with continuous feedback and validation from pathologists and clinicians. The ontology is organized into 5 semantic areas that defines an ontological template to model any disease of interest in histopathology. ResultsThe ExaMode ontology is currently being used as a common semantic layer in: (i) an entity linking tool for the automatic annotation of medical records; (ii) a web-based collaborative annotation tool for histopathology text reports; and (iii) a software platform for building holistic solutions integrating multimodal histopathology data. DiscussionThe ontology ExaMode is a key means to store data in a graph database according to the RDF data model. The creation of an RDF dataset can help develop more accurate algorithms for image analysis, especially in the field of digital pathology. This approach allows for seamless data integration and a unified query access point, from which we can extract relevant clinical insights about the considered diseases using SPARQL queries.

SemClinBr - a multi-institutional and multi-specialty semantically annotated corpus for Portuguese clinical NLP tasks

BackgroundThe high volume of research focusing on extracting patient information from electronic health records (EHRs) has led to an increase in the demand for annotated corpora, which are a precious resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multipurpose clinical corpus outside the scope of the English language, especially in Brazilian Portuguese, is glaring and severely impacts scientific progress in the biomedical NLP field.MethodsIn this study, a semantically annotated corpus was developed using clinical text from multiple medical specialties, document types, and institutions. In addition, we present, (1) a survey listing common aspects, differences, and lessons learned from previous research, (2) a fine-grained annotation schema that can be replicated to guide other annotation initiatives, (3) a web-based annotation tool focusing on an annotation suggestion feature, and (4) both intrinsic and extrinsic evaluation of the annotations.ResultsThis study resulted in SemClinBr, a corpus that has 1000 clinical notes, labeled with 65,117 entities and 11,263 relations. In addition, both negation cues and medical abbreviation dictionaries were generated from the annotations. The average annotator agreement score varied from 0.71 (applying strict match) to 0.92 (considering a relaxed match) while accepting partial overlaps and hierarchically related semantic types. The extrinsic evaluation, when applying the corpus to two downstream NLP tasks, demonstrated the reliability and usefulness of annotations, with the systems achieving results that were consistent with the agreement scores.ConclusionThe SemClinBr corpus and other resources produced in this work can support clinical NLP studies, providing a common development and evaluation resource for the research community, boosting the utilization of EHRs in both clinical practice and biomedical research. To the best of our knowledge, SemClinBr is the first available Portuguese clinical corpus.

Translation Alignment with Ugarit

Ugarit is a public web-based tool for manual annotation of parallel texts for generating word-level translation alignment. We aimed to develop a user-friendly interactive interface to visualize aligned texts and collect training data in the form of translation pairs to be used later, (i) for training an automatic translation alignment system for historical languages at the word/phrase level, (ii) as a gold standard to evaluate automatic alignment and machine translation systems. Ugarit is now widely used for learning new languages, especially historical languages, and as a reading environment for parallel texts. In the following sections, we present the related works and similar projects; then, we give an overview of the visualization techniques used to present the alignment results. Further, we explain how we could derive the translation graph from the aligned translation pairs. Finally, we discuss the usage limitations of Ugarit, possible improvements, and future development plans.

Towards the creation of a nutrition and food group based image database

Food classification is critical to the analysis of nutrients comprising foods reported in dietary assessment. Advances in mobile and wearable sensors, combined with new image based methods, particularly deep learning based approaches, have shown great promise to improve the accuracy of food classification to assess dietary intake. However, these approaches are data-hungry and their performances are heavily reliant on the quantity and quality of the available datasets for training the food classification model. Existing food image datasets are not suitable for fine-grained food classification and the following nutrition analysis as they lack fine-grained and transparently derived food group based identification which are often provided by trained dietitians with expert domain knowledge. In this paper, we propose a framework to create a nutrition and food group based image database that contains both visual and hierarchical food categorization information to enhance links to the nutrient profile of each food. We design a protocol for linking food group based food codes in the U.S. Department of Agriculture’s (USDA) Food and Nutrient Database for Dietary Studies (FNDDS) to a food image dataset, and implement a web-based annotation tool for efficient deployment of this protocol. Our proposed method is used to build a nutrition and food group based image database including 16,114 food images representing the 74 most frequently consumed What We Eat in America (WWEIA) food sub-categories in the United States with 1,865 USDA food code matched to a nutrient database, the USDA FNDDS nutrient database.

Markup: A Web-Based Annotation Tool Powered by Active Learning.

Across various domains, such as health and social care, law, news, and social media, there are increasing quantities of unstructured texts being produced. These potential data sources often contain rich information that could be used for domain-specific and research purposes. However, the unstructured nature of free-text data poses a significant challenge for its utilisation due to the necessity of substantial manual intervention from domain-experts to label embedded information. Annotation tools can assist with this process by providing functionality that enables the accurate capture and transformation of unstructured texts into structured annotations, which can be used individually, or as part of larger Natural Language Processing (NLP) pipelines. We present Markup (https://www.getmarkup.com/) an open-source, web-based annotation tool that is undergoing continued development for use across all domains. Markup incorporates NLP and Active Learning (AL) technologies to enable rapid and accurate annotation using custom user configurations, predictive annotation suggestions, and automated mapping suggestions to both domain-specific ontologies, such as the Unified Medical Language System (UMLS), and custom, user-defined ontologies. We demonstrate a real-world use case of how Markup has been used in a healthcare setting to annotate structured information from unstructured clinic letters, where captured annotations were used to build and test NLP applications.

Multispectral aerial imagery-based 3D digitisation, segmentation and annotation of large scale urban areas of significant cultural value

• Urban areas 3D digitisation using multispectral aerial imagery • Experimental low-cost multispectral camera for drone development • Supervised machine learning methods for 3D mesh segmentation using multispectral data • Performance evaluation of the proposed methods on a large scale case study • Web-based visualisation and annotation tool for segmentation fine-tuning Disaster risk management of movable and immovable cultural heritage is a highly significant research topic. In this work, we present a pipeline for 3D digitisation, segmentation and annotation of large scale urban areas in order to produce data that can be exploited in disaster management simulators (e.g fire spreading, crowd movement, firefighting training, evacuation planning, etc.). We have selected the old town of Xanthi (Greece) as a challenging case study. We developed a custom multispectral camera to be carried by a commercial drone. Using the structure from motion / multiview stereo (SFM/MVS) approach, we produced a 3D model of the urban area covering 0.5 k m 2 that is followed by a multilayer texture map which carries information from visible and near-infrared regions of the electromagnetic spectrum. We developed a set of machine learning approaches based on logistic regression, support vector machines and artificial neural networks that allow 3D model segmentation by exploiting not only morphological and structural features but also the multispectral behaviour of different material surfaces. We objectively evaluate the performance of the proposed segmentation approaches on six significant material-based classes (cobbled-roads granite kilns, building walls, ceramic roof-tiles, low-vegetation, high-vegetation and metal surfaces) that are used in simulating fire propagation and crowd movement. The experiments revealed that the segmentation accuracy can be enhanced by taking into consideration surface material multispectral properties as well as morphological features. A Web-based multi-user annotation tool complements our proposed pipeline by enabling further 3D model segmentation, fine tuning and semantics annotation (e.g. usage-based building classification and evacuation priorities, escape paths and gathering points).

Markup: A Web-Based Clinical Annotation Tool with Enhanced Ontology Mapping

IntroductionUnstructured free-text clinical notes often contain valuable information relating to patient symptoms, prescriptions and diagnoses. These can assist with better care for patients and novel healthcare research if transformed into accessible, structured clinical text. In particular, Natural Language Processing (NLP) algorithms can produce such structured outputs, but require gold standard data to train and validate their accuracy. While existing tools such as Brat and Webanno provide interfaces to manually annotate text, there is a lack of capability to efficiently annotate complex clinical information.
 Objectives and ApproachWe present Markup, an open-source, web-based annotation tool developed for use within clinical contexts by domain experts to produce gold standard annotations for NLP development. Markup incorporates NLP and Active Learning technologies to enable rapid and accurate annotation of unstructured documents. Markup supports custom user configurations, automated annotation suggestions, and automated mapping to existing clinical ontologies such as the Unified Medical Language System (UMLS), the Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT), or custom, user-defined ontologies.
 ResultsMarkup has been tested on Epilepsy clinic letters, where captured annotations were used to build and test NLP applications. Markup allowed for inter-annotator statistics to be calculated in the case of multiple annotators. Re-annotation, following iterations of annotation definitions, was incorporated for flexibility. UMLS codes, certainty context, and multiple components from complex phrases were all able to be captured and exported in a structured format.
 Conclusions / ImplicationsMarkup allows gold standard annotations to be collected efficiently across unstructured text and is optimized to capture health-specific information. These annotations are important to develop and validate NLP algorithms that automate the capture of important information from clinic letters at scale.

User-centered design of a web-based crowdsourcing-integrated semantic text annotation tool for building a mental health knowledge base.

One in five U.S. adults lives with some kind of mental health condition and 4.6% of all U.S. adults have a serious mental illness. The Internet has become the first place for these people to seek online mental health information for help. However, online mental health information is not well-organized and often of low quality. There have been efforts in building evidence-based mental health knowledgebases curated with information manually extracted from the high-quality scientific literature. Manual extraction is inefficient. Crowdsourcing can potentially be a low-cost mechanism to collect labeled data from non-expert laypeople. However, there is not an existing annotation tool integrated with popular crowdsourcing platforms to perform the information extraction tasks. In our previous work, we prototyped a Semantic Text Annotation Tool (STAT) to address this gap. We aimed to refine the STAT prototype (1) to improve its usability and (2) to enhance the crowdsourcing workflow efficiency to facilitate the construction of evidence-based mental health knowledgebase, following a user-centered design (UCD) approach. Following UCD principles, we conducted four design iterations to improve the initial STAT prototype. In the first two iterations, usability testing focus groups were conducted internally with 8 participants recruited from a convenient sample, and the usability was evaluated with a modified System Usability Scale (SUS). In the following two iterations, usability testing was conducted externally using the Amazon Mechanical Turk (MTurk) platform. In each iteration, we summarized the usability testing results through thematic analysis, identified usability issues, and conducted a heuristic evaluation to map identified usability issues to Jakob Nielsen's usability heuristics. We collected suggested improvements in the usability testing sessions and enhanced STAT accordingly in the next UCD iteration. After four UCD iterations, we conducted a case study of the system on MTurk using mental health related scientific literature. We compared the performance of crowdsourcing workers with two expert annotators from two aspects: efficiency and quality. The SUS score increased from 70.3±12.5 to 81.1±9.8 after the two internal UCD iterations as we improved STAT's functionality based on the suggested improvements. We then evaluated STAT externally through MTurk in the following two iterations. The SUS score decreased to 55.7±20.1 in the third iteration, probably because of the complexity of the tasks. After further simplification of STAT and the annotation tasks with an improved annotation guideline, the SUS score increased to 73.8±13.8 in the fourth iteration of UCD. In the evaluation case study, on average, the workers spent 125.5±69.2s on the onboarding tutorial and the crowdsourcing workers spent significantly less time on the annotation tasks compared to the two experts. In terms of annotation quality, the workers' annotation results achieved average F1-scores ranged from 0.62 to 0.84 for the different sentences. We successfully developed a web-based semantic text annotation tool, STAT, to facilitate the curation of semantic web knowledgebases through four UCD iterations. The lessons learned from the UCD process could serve as a guide to further enhance STAT and the development and design of other crowdsourcing-based semantic text annotation tasks. Our study also showed that a well-organized, informative annotation guideline is as important as the annotation tool itself. Further, we learned that a crowdsourcing task should consist of multiple simple microtasks rather than a complicated task.

SNPnexus: a web server for functional annotation of human genome sequence variation (2020 update).

SNPnexus is a web-based annotation tool for the analysis and interpretation of both known and novel sequencing variations. Since its last release, SNPnexus has received continual updates to expand the range and depth of annotations provided. SNPnexus has undergone a complete overhaul of the underlying infrastructure to accommodate faster computational times. The scope for data annotation has been substantially expanded to enhance biological interpretations of queried variants. This includes the addition of pathway analysis for the identification of enriched biological pathways and molecular processes. We have further expanded the range of user directed annotation fields available for the study of cancer sequencing data. These new additions facilitate investigations into cancer driver variants and targetable molecular alterations within input datasets. New user directed filtering options have been coupled with the addition of interactive graphical and visualization tools. These improvements streamline the analysis of variants derived from large sequencing datasets for the identification of biologically and clinically significant subsets in the data. SNPnexus is the most comprehensible web-based application currently available and these new set of updates ensures that it remains a state-of-the-art tool for researchers. SNPnexus is freely available at https://www.snp-nexus.org.

Eras: Improving the quality control in the annotation process for Natural Language Processing tasks

The increasing amount of valuable, unstructured textual information poses a major challenge to extract value from those texts. We need to use NLP (Natural Language Processing) techniques, most of which rely on manually annotating a large corpus of text for its development and evaluation. Creating a large annotated corpus is laborious and requires suitable computational support. There are many annotation tools available, but their main weaknesses are the absence of data management features for quality control and the need for a commercial license. As the quality of the data used to train an NLP model directly affects the quality of the results, the quality control of the annotations is essential. In this paper, we introduce ERAS, a novel web-based text annotation tool developed to facilitate and manage the process of text annotation. ERAS includes not only the key features of current mainstream annotation systems but also other features necessary to improve the curation process, such as the inter-annotator agreement, self-agreement and annotation log visualization, for annotation quality control. ERAS also implements a series of features to improve the customization of the user’s annotation workflow, such as: random document selection, re-annotation stages, and warm-up annotations. We conducted two empirical studies to evaluate the tool’s support to text annotation, and the results suggest that the tool not only meets the basic needs of the annotation task but also has some important advantages over the other tools evaluated in the studies. ERAS is freely available at https://github.com/grosmanjs/eras.

TeamTat: a collaborative text annotation tool.

Manually annotated data is key to developing text-mining and information-extraction algorithms. However, human annotation requires considerable time, effort and expertise. Given the rapid growth of biomedical literature, it is paramount to build tools that facilitate speed and maintain expert quality. While existing text annotation tools may provide user-friendly interfaces to domain experts, limited support is available for figure display, project management, and multi-user team annotation. In response, we developed TeamTat (https://www.teamtat.org), a web-based annotation tool (local setup available), equipped to manage team annotation projects engagingly and efficiently. TeamTat is a novel tool for managing multi-user, multi-label document annotation, reflecting the entire production life cycle. Project managers can specify annotation schema for entities and relations and select annotator(s) and distribute documents anonymously to prevent bias. Document input format can be plain text, PDF or BioC(uploaded locally or automatically retrieved from PubMed/PMC), and output format is BioC with inline annotations. TeamTat displays figures from the full text for the annotator's convenience. Multiple users can work on the same document independently in their workspaces, and the team manager can track task completion. TeamTat provides corpus quality assessment via inter-annotator agreement statistics, and a user-friendly interface convenient for annotation review and inter-annotator disagreement resolution to improve corpus quality.

CPVA: a web-based metabolomic tool for chromatographic peak visualization and annotation.

Liquid chromatography-mass spectrometry-based non-targeted metabolomics is routinely performed to qualitatively and quantitatively analyze a tremendous amount of metabolite signals in complex biological samples. However, false-positive peaks in the datasets are commonly detected as metabolite signals by using many popular software, resulting in non-reliable measurement. To reduce false-positive calling, we developed an interactive web tool, termed CPVA, for visualization and accurate annotation of the detected peaks in non-targeted metabolomics data. We used a chromatogram-centric strategy to unfold the characteristics of chromatographic peaks through visualization of peak morphology metrics, with additional functions to annotate adducts, isotopes and contaminants. CPVA is a free, user-friendly tool to help users to identify peak background noises and contaminants, resulting in decrease of false-positive or redundant peak calling, thereby improving the data quality of non-targeted metabolomics studies. The CPVA is freely available at http://cpva.eastus.cloudapp.azure.com. Source code and installation instructions are available on GitHub: https://github.com/13479776/cpva. Supplementary data are available at Bioinformatics online.

An integrated analysis of public genomic data unveils a possible functional mechanism of psoriasis risk via a long-range ERRFI1 enhancer

BackgroundPsoriasis is a chronic inflammatory skin disease, for which genome-wide association studies (GWAS) have identified many genetic variants as risk markers. However, the details of underlying molecular mechanisms, especially which variants are functional, are poorly understood.MethodsWe utilized a computational approach to survey psoriasis-associated functional variants that might affect protein functions or gene expression levels. We developed a pipeline by integrating publicly available datasets provided by GWAS Catalog, FANTOM5, GTEx, SNP2TFBS, and DeepBlue. To identify functional variants on exons or splice sites, we used a web-based annotation tool in the Ensembl database. To search for noncoding functional variants within promoters or enhancers, we used eQTL data calculated by GTEx. The data of variants lying on transcription factor binding sites provided by SNP2TFBS were used to predict detailed functions of the variants.ResultsWe discovered 22 functional variant candidates, of which 8 were in noncoding regions. We focused on the enhancer variant rs72635708 (T > C) in the 1p36.23 region; this variant is within the enhancer region of the ERRFI1 gene, which regulates lipid metabolism in the liver and skin morphogenesis via EGF signaling. Further analysis showed that the ERRFI1 promoter spatially contacts with the enhancer, despite the 170 kb distance between them. We found that this variant lies on the AP-1 complex binding motif and may modulate binding levels.ConclusionsThe minor allele rs72635708 (rs72635708-C) might affect the ERRFI1 promoter activity, which results in unstable expression of ERRFI1, enhancing the risk of psoriasis via disruption of lipid metabolism and skin cell proliferation. Our study represents a successful example of predicting molecular pathogenesis by integration and reanalysis of public data.

Accurator: Nichesourcing for Cultural Heritage

With the increase of cultural heritage data published online, the usefulness of data in this open context hinges on the quality and diversity of descriptions of collection objects. In many cases, existing descriptions are not sufficient for retrieval and research tasks, resulting in the need for more specific annotations. However, eliciting such annotations is a challenge since it often requires domain-specific knowledge. Where crowdsourcing can be successfully used to execute simple annotation tasks, identifying people with the required expertise might prove troublesome for more complex and domain-specific tasks. Nichesourcing addresses this problem, by tapping into the expert knowledge available in niche communities. This paper presents Accurator, a methodology for conducting nichesourcing campaigns for cultural heritage institutions, by addressing communities, organizing events and tailoring a web-based annotation tool to a domain of choice. The contribution of this paper is fourfold: 1) a nichesourcing methodology, 2) an annotation tool for experts, 3) validation of the methodology in three case studies and 4) a dataset including the obtained annotations. The three domains of the case studies are birds on art, bible prints and fashion images. We compare the quality and quantity of obtained annotations in the three case studies, showing that the nichesourcing methodology in combination with the image annotation tool can be used to collect high-quality annotations in a variety of domains. A user evaluation indicates the tool is suited and usable for domain-specific annotation tasks.

Accurator: Nichesourcing for Cultural Heritage

With the increase of cultural heritage data published online, the usefulness of data in this open context hinges on the quality and diversity of descriptions of collection objects. In many cases, existing descriptions are not sufficient for retrieval and research tasks, resulting in the need for more specific annotations. However, eliciting such annotations is a challenge since it often requires domain-specific knowledge. Where crowdsourcing can be successfully used to execute simple annotation tasks, identifying people with the required expertise might prove troublesome for more complex and domain-specific tasks. Nichesourcing addresses this problem, by tapping into the expert knowledge available in niche communities. This paper presents Accurator, a methodology for conducting nichesourcing campaigns for cultural heritage institutions, by addressing communities, organizing events and tailoring a web-based annotation tool to a domain of choice. The contribution of this paper is fourfold: 1) a nichesourcing methodology, 2) an annotation tool for experts, 3) validation of the methodology in three case studies and 4) a dataset including the obtained annotations. The three domains of the case studies are birds on art, bible prints and fashion images. We compare the quality and quantity of obtained annotations in the three case studies, showing that the nichesourcing methodology in combination with the image annotation tool can be used to collect high-quality annotations in a variety of domains. A user evaluation indicates the tool is suited and usable for domain-specific annotation tasks.

STAT: A Web-based Semantic Text Annotation Tool to Assist Building Mental Health Knowledge Base.

Mental health problems are serious among American adults and many of them are turning to the Internet for help. However, online mental health information is not well-organized and in low quality. We are building a mental health knowledge base (MHKB) with evidence-based information extracted from scientific literature manually, but lacking efficiency. We envision to leverage collective wisdoms through crowdsourcing to speed up the curation of MHKB. In order to integrate with crowdsourcing platforms, we designed and prototyped a web-based annotation tool, STAT (Semantic Text Annotation Tool), with real-time annotation recommendation and annotation quality analysis, to facilitate management of laypeople annotators recruited through crowdsourcing to complete the necessary annotation tasks.