Data Labeling Software Market Outlook

In 2023, the global market size for data labeling software was valued at approximately USD 1.2 billion and is projected to reach USD 6.5 billion by 2032, with a CAGR of 21% during the forecast period. The primary growth factor driving this market is the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies across various industry verticals, necessitating high-quality labeled data for model training and validation.

The surge in AI and ML applications is a significant growth driver for the data labeling software market. As businesses increasingly harness these advanced technologies to gain insights, optimize operations, and innovate products and services, the demand for accurately labeled data has skyrocketed. This trend is particularly pronounced in sectors such as healthcare, automotive, and finance, where AI and ML applications are critical for advancements like predictive analytics, autonomous driving, and fraud detection. The growing reliance on AI and ML is propelling the market forward, as labeled data forms the backbone of effective AI model development.

Another crucial growth factor is the proliferation of big data. With the explosion of data generated from various sources, including social media, IoT devices, and enterprise systems, organizations are seeking efficient ways to manage and utilize this vast amount of information. Data labeling software enables companies to systematically organize and annotate large datasets, making them usable for AI and ML applications. The ability to handle diverse data types, including text, images, and audio, further amplifies the demand for these solutions, facilitating more comprehensive data analysis and better decision-making.

The increasing emphasis on data privacy and security is also driving the growth of the data labeling software market. With stringent regulations such as GDPR and CCPA coming into play, companies are under pressure to ensure that their data handling practices comply with legal standards. Data labeling software helps in anonymizing and protecting sensitive information during the labeling process, thus providing a layer of security and compliance. This has become particularly important as data breaches and cyber threats continue to rise, making secure data management a top priority for organizations worldwide.

Regionally, North America holds a significant share of the data labeling software market due to early adoption of AI and ML technologies, substantial investments in tech startups, and advanced IT infrastructure. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth is driven by the rapid digital transformation in countries like China and India, increasing investments in AI research, and the expansion of IT services. Europe and Latin America also present substantial growth opportunities, supported by technological advancements and increasing regulatory compliance needs.

Component Analysis

The data labeling software market can be segmented by component into software and services. The software segment encompasses various platforms and tools designed to label data efficiently. These software solutions offer features such as automation, integration with other AI tools, and scalability, which are critical for handling large datasets. The growing demand for automated data labeling solutions is a significant trend in this segment, driven by the need for faster and more accurate data annotation processes.

In contrast, the services segment includes human-in-the-loop solutions, consulting, and managed services. These services are essential for ensuring the quality and accuracy of labeled data, especially for complex tasks that require human judgment. Companies often turn to service providers for their expertise in specific domains, such as healthcare or automotive, where domain knowledge is crucial for effective data labeling. The services segment is also seeing growth due to the increasing need for customized solutions tailored to specific business requirements.

Moreover, hybrid approaches that combine software and human expertise are gaining traction. These solutions leverage the scalability and speed of automated software while incorporating human oversight for quality assurance. This combination is particularly useful in scenarios where data quality is paramount, such as in medical imaging or autonomous vehicle training. The hybrid model is expected to grow as companies seek to balance efficiency with accuracy in their

Leaves from genetically unique Juglans regia plants were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA). Soil samples were collected in Fall of 2017 from the riparian oak forest located at the Russell Ranch Sustainable Agricultural Institute at the University of California Davis. The soil was sieved through a 2 mm mesh and was air dried before imaging. A single soil aggregate was scanned at 23 keV using the 10x objective lens with a pixel resolution of 650 nanometers on beamline 8.3.2 at the ALS. Additionally, a drought stressed almond flower bud (Prunus dulcis) from a plant housed at the University of California, Davis, was scanned using a 4x lens with a pixel resolution of 1.72 µm on beamline 8.3.2 at the ALS Raw tomographic image data was reconstructed using TomoPy. Reconstructions were converted to 8-bit tif or png format using ImageJ or the PIL package in Python before further processing. Images were annotated using Intel’s Computer Vision Annotation Tool (CVAT) and ImageJ. Both CVAT and ImageJ are free to use and open source. Leaf images were annotated in following Théroux-Rancourt et al. (2020). Specifically, Hand labeling was done directly in ImageJ by drawing around each tissue; with 5 images annotated per leaf. Care was taken to cover a range of anatomical variation to help improve the generalizability of the models to other leaves. All slices were labeled by Dr. Mina Momayyezi and Fiona Duong.To annotate the flower bud and soil aggregate, images were imported into CVAT. The exterior border of the bud (i.e. bud scales) and flower were annotated in CVAT and exported as masks. Similarly, the exterior of the soil aggregate and particulate organic matter identified by eye were annotated in CVAT and exported as masks. To annotate air spaces in both the bud and soil aggregate, images were imported into ImageJ. A gaussian blur was applied to the image to decrease noise and then the air space was segmented using thresholding. After applying the threshold, the selected air space region was converted to a binary image with white representing the air space and black representing everything else. This binary image was overlaid upon the original image and the air space within the flower bud and aggregate was selected using the “free hand” tool. Air space outside of the region of interest for both image sets was eliminated. The quality of the air space annotation was then visually inspected for accuracy against the underlying original image; incomplete annotations were corrected using the brush or pencil tool to paint missing air space white and incorrectly identified air space black. Once the annotation was satisfactorily corrected, the binary image of the air space was saved. Finally, the annotations of the bud and flower or aggregate and organic matter were opened in ImageJ and the associated air space mask was overlaid on top of them forming a three-layer mask suitable for training the fully convolutional network. All labeling of the soil aggregate and soil aggregate images was done by Dr. Devin Rippner. These images and annotations are for training deep learning models to identify different constituents in leaves, almond buds, and soil aggregates Limitations: For the walnut leaves, some tissues (stomata, etc.) are not labeled and only represent a small portion of a full leaf. Similarly, both the almond bud and the aggregate represent just one single sample of each. The bud tissues are only divided up into buds scales, flower, and air space. Many other tissues remain unlabeled. For the soil aggregate annotated labels are done by eye with no actual chemical information. Therefore particulate organic matter identification may be incorrect. Resources in this dataset:Resource Title: Annotated X-ray CT images and masks of a Forest Soil Aggregate. File Name: forest_soil_images_masks_for_testing_training.zipResource Description: This aggregate was collected from the riparian oak forest at the Russell Ranch Sustainable Agricultural Facility. The aggreagate was scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 0,0,0; pores spaces have a value of 250,250, 250; mineral solids have a value= 128,0,0; and particulate organic matter has a value of = 000,128,000. These files were used for training a model to segment the forest soil aggregate and for testing the accuracy, precision, recall, and f1 score of the model.Resource Title: Annotated X-ray CT images and masks of an Almond bud (P. Dulcis). File Name: Almond_bud_tube_D_P6_training_testing_images_and_masks.zipResource Description: Drought stressed almond flower bud (Prunis dulcis) from a plant housed at the University of California, Davis, was scanned by X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 4x lens with a pixel resolution of 1.72 µm using. For masks, the background has a value of 0,0,0; air spaces have a value of 255,255, 255; bud scales have a value= 128,0,0; and flower tissues have a value of = 000,128,000. These files were used for training a model to segment the almond bud and for testing the accuracy, precision, recall, and f1 score of the model.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads Resource Title: Annotated X-ray CT images and masks of Walnut leaves (J. Regia) . File Name: 6_leaf_training_testing_images_and_masks_for_paper.zipResource Description: Stems were collected from genetically unique J. regia accessions at the 117 USDA-ARS-NCGR in Wolfskill Experimental Orchard, Winters, California USA to use as scion, and were grafted by Sierra Gold Nursery onto a commonly used commercial rootstock, RX1 (J. microcarpa × J. regia). We used a common rootstock to eliminate any own-root effects and to simulate conditions for a commercial walnut orchard setting, where rootstocks are commonly used. The grafted saplings were repotted and transferred to the Armstrong lathe house facility at the University of California, Davis in June 2019, and kept under natural light and temperature. Leaves from each accession and treatment were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 170,170,170; Epidermis value= 85,85,85; Mesophyll value= 0,0,0; Bundle Sheath Extension value= 152,152,152; Vein value= 220,220,220; Air value = 255,255,255.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads

The Q-CAT (Querying-Supported Corpus Annotation Tool) is a tool for manual linguistic annotation of corpora, which also enables advanced queries on top of these annotations. The tool has been used in various annotation campaigns related to the ssj500k reference training corpus of Slovenian (http://hdl.handle.net/11356/1210), such as named entities, dependency syntax, semantic roles and multi-word expressions, but it can also be used for adding new annotation layers of various types to this or other language corpora. Q-CAT is a .NET application, which runs on Windows operating system.

Version 1.1 enables the automatic attribution of token IDs and personalized font adjustments. Version 1.2 supports the CONLL-U format and working with UD POS tags. Version 1.3 supports adding new layers of annotation on top of CONLL-U (and then saving the corpus as XML TEI). Version 1.4 introduces new features in command line mode (filtering by sentence ID, multiple link type visualizations)

Mass spectrometry (MS) is a powerful technology for the structural elucidation of known or unknown small molecules. However, the accuracy of MS-based structure annotation is still limited due to the presence of numerous isomers in complex matrices. There are still challenges in automatically interpreting the fine structure of molecules, such as the types and positions of substituents (substituent modes, SMs) in the structure. In this study, we employed flavones, flavonols, and isoflavones as examples to develop an automated annotation method for identifying the SMs on the parent molecular skeleton based on a characteristic MS/MS fragment ion library. Importantly, user-friendly software AnnoSM was built for the convenience of researchers with limited computational backgrounds. It achieved 76.87% top-1 accuracy on the 148 authentic standards. Among them, 22 sets of flavonoid isomers were successfully differentiated. Moreover, the developed method was successfully applied to complex matrices. One such example is the extract of Ginkgo biloba L. (EGB), in which 331 possible flavonoids with SM candidates were annotated. Among them, 23 flavonoids were verified by authentic standards. The correct SMs of 13 flavonoids were ranked first on the candidate list. In the future, this software can also be extrapolated to other classes of compounds.

Artifact for paper (Alpinist: an Annotation-Aware GPU Program Optimizer) submitted to TACAS '22 conference. For a full description on how to use the artifact, please see the README.txt file. The artifact contains the Alpinist tool, all its dependencies and documentation for the VerCors tool.

Abstract of the paper:
GPU programs are widely used in industry. To obtain the best performance, a typical development process involves the manual or semi-automatic application of optimizations prior to compiling the code. To avoid the introduction of errors, we can augment GPU programs with (pre- and postcondition-style) annotations to capture functional properties. However, keeping these annotations correct when optimizing GPU programs is labor-intensive and error-prone.
This paper introduces Alpinist, an annotation-aware GPU program optimizer. It applies frequently-used GPU optimizations, but besides transforming code, it also transforms the annotations. We evaluate Alpinist, in combination with the VerCors program verifier, to automatically optimize a collection of verified programs and reverify them.

Data set of manually annotated chordata-specific proteins as well as those that are widely conserved. The program keeps existing human entries up-to-date and broadens the manual annotation to other vertebrate species, especially model organisms, including great apes, cow, mouse, rat, chicken, zebrafish, as well as Xenopus laevis and Xenopus tropicalis. A draft of the complete human proteome is available in UniProtKB/Swiss-Prot and one of the current priorities of the Chordata protein annotation program is to improve the quality of human sequences provided. To this aim, they are updating sequences which show discrepancies with those predicted from the genome sequence. Dubious isoforms, sequences based on experimental artifacts and protein products derived from erroneous gene model predictions are also revisited. This work is in part done in collaboration with the Hinxton Sequence Forum (HSF), which allows active exchange between UniProt, HAVANA, Ensembl and HGNC groups, as well as with RefSeq database. UniProt is a member of the Consensus CDS project and thye are in the process of reviewing their records to support convergence towards a standard set of protein annotation. They also continuously update human entries with functional annotation, including novel structural, post-translational modification, interaction and enzymatic activity data. In order to identify candidates for re-annotation, they use, among others, information extraction tools such as the STRING database. In addition, they regularly add new sequence variants and maintain disease information. Indeed, this annotation program includes the Variation Annotation Program, the goal of which is to annotate all known human genetic diseases and disease-linked protein variants, as well as neutral polymorphisms.

This file contains the annotations for the ConfLab dataset, including actions (speaking status), pose, and F-formations.

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./actions/speaking_status:

./processed: the processed speaking status files, aggregated into a single data frame per segment. Skipped rows in the raw data (see https://josedvq.github.io/covfee/docs/output for details) have been imputed using the code at: https://github.com/TUDelft-SPC-Lab/conflab/tree/master/preprocessing/speaking_status

The processed annotations consist of:

./speaking: The first row contains person IDs matching the sensor IDs,

The rest of the row contains binary speaking status annotations at 60fps for the corresponding 2 min video segment (7200 frames).

./confidence: Same as above. These annotations reflect the continuous-valued rating of confidence of the annotators in their speaking annotation.

To load these files with pandas: pd.read_csv(p, index_col=False)

./raw.zip: the raw outputs from speaking status annotation for each of the eight annotated 2-min video segments. These were were output by the covfee annotation tool (https://github.com/josedvq/covfee)

Annotations were done at 60 fps.

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./pose:

./coco: the processed pose files in coco JSON format, aggregated into a single data frame per video segment. These files have been generated from the raw files using the code at: https://github.com/TUDelft-SPC-Lab/conflab-keypoints

To load in Python: f = json.load(open('/path/to/cam2_vid3_seg1_coco.json'))

The skeleton structure (limbs) is contained within each file in:

f['categories'][0]['skeleton']

and keypoint names at:

f['categories'][0]['keypoints']

./raw.zip: the raw outputs from continuous pose annotation. These were were output by the covfee annotation tool (https://github.com/josedvq/covfee)

Annotations were done at 60 fps.

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./f_formations:

seg 2: 14:00 onwards, for videos of the form x2xxx.MP4 in /video/raw/ for the relevant cameras (2,4,6,8,10).

seg 3: for videos of the form x3xxx.MP4 in /video/raw/ for the relevant cameras (2,4,6,8,10).

Note that camera 10 doesn't include meaningful subject information/body parts that are not already covered in camera 8.

First column: time stamp

Second column: "()" delineates groups, "<>" delineates subjects, cam X indicates the best camera view for which a particular group exists.

phone.csv: time stamp (pertaining to seg3), corresponding group, ID of person using the phone

Vietnamese Multiple Document Summarization Dataset

ViMs Dataset (ViMs folder)

This work was supported by the Ho Chi Minh City Department of Science and Technology, Grant Numbers 15/2016/HÐ-SKHCN

Data construction process: In this work, we aim to have 300 clusters of documents extracted from news. To this end, we made use of the Vietnamese language version of Google News. Due to the copyright issue, we did not collect articles from every source listed on Google News, but limited to some sources that are open for research purposes. The collected articles belong to five genres: world news, domestic news, business, entertainment, and sports. Every cluster contains from four to ten news articles. Each article is represented by the following information: the title, the plain text content, the news source, the date of publication, the author(s), the tag(s) and the headline summary.

After that, two summaries are created for each cluster (produced in the first subtask above) by two distinguished annotators using the MDSWriter system (Meyer, Christian M., et al. "MDSWriter: Annotation tool for creating high-quality multi-document summarization corpora." Proceedings of ACL-2016 System Demonstrations). These annotators are Vietnamese native speakers and they are undergraduate students or graduate students. Most of them know about natural language processing. The full annotation process consists of seven steps that must be done sequentially from the first to the seventh one.

Data information: Original folder: Containing 300 subdirectories which are 300 news clusters. Articles (documents) in each cluster belong to a similar topic and there are from four to ten of them. The number of articles is 1,945.

Summary folder: Contains 300 subdirectories which are 600 final summaries. Every input cluster has two manual abstract summaries from two distinguished annotators. ViMs can be used for both implementing and evaluating supervised machine learning-based systems for Vietnamese abstractive multi-document summarization.

S3_summary folder: Contains 300 subdirectories including 600 ''best sentence selection'' summaries, the result of step 3 -- best sentence selection step. Sentences in a group are separated from others by a blank line. The most important sentence is marked as 1 while 0 is the label for others.

@article{tran2020vims, title={ViMs: a high-quality Vietnamese dataset for abstractive multi-document summarization}, author={Tran, Nhi-Thao and Nghiem, Minh-Quoc and Nguyen, Nhung TH and Nguyen, Ngan Luu-Thuy and Van Chi, Nam and Dinh, Dien}, journal={Language Resources and Evaluation}, volume={54}, number={4}, pages={893--920}, year={2020}, publisher={Springer} }

Vietnamese MDS (clusters folder)

Author: Tran Mai Vu, Vu Trong Hoa, Phi Van Thuy, Le Duc Trong, Ha Quang Thuy Affiliation: Knowledge Technology Laboratory, University of Technology, VNU Hanoi Research Topic: Design and Implementation of a Multi-document Summarization Program for the Vietnamese Language, Funded by the Ministry of Education (Project Code: B2012-01-24)

Data construction process: The data construction process is entirely manual. It consists of two steps:

• Step 1: Data Collection and Clustering - Data is collected from the Baomoi website and organized into clusters, where each cluster contains documents related to a specific topic. The data is collected from various subjects on Baomoi, typically encompassing 8-10 main categories, such as World, Society, Culture, Economics, Science-Technology, Sports, Entertainment, Law, Education, Health, Automobiles, and Real Estate.
• Step 2: Summarization - Two individuals participate in creating reference summaries. The summarization process includes two stages: extracting important sentences and rewriting them into a coherent paragraph.

Data information: Data Volume: 200 clusters

Each cluster corresponds to a folder, and it typically contains 2-5 documents (often 3). The folder's name represents the cluster.

Within each folder:

• .info: Contains cluster ID and cluster label (labels are assigned by the cluster creator based on content).
• .ref1.txt: Reference summary created by summarizer 1.
• .ref1.tok.txt: Tokenized version of reference summary 1, with sentences and words separated.
• .ref2.txt: Reference summary created by summarizer 2.
• .ref2.tok.txt: Tokenized version of reference summary 2, with sentences and words separated.
• .sum.txt: Machine-generated summary.
• .sum.tok.txt: Tokenized version of the machine-generated summary, with sentences and words separated.

All files within the same folder represent documents (online articles) belonging to the cluster:

• .body.txt: Contains the main content of the document.
• .body.tok.txt: Contains the document's content with sentences and words separated.
• .info.txt: Contains other in...

LabelMe database is a large collection of images with ground truth labels for object detection and recognition. The annotations come from two different sources, including the LabelMe online annotation tool.

Dataset Introduction TFH_Annotated_Dataset is an annotated patent dataset pertaining to thin film head technology in hard-disk. To the best of our knowledge, this is the second labeled patent dataset public available in technology management domain that annotates both entities and the semantic relations between entities, the first one is [1].

The well-crafted information schema used for patent annotation contains 17 types of entities and 15 types of semantic relations as shown below.

Table 1 The specification of entity types

Table 2 The specification of relation types

There are 1010 patent abstracts with 3,986 sentences in this corpus . We use a web-based annotation tool named Brat[2] for data labeling, and the annotated data is saved in '.ann' format. The benefit of 'ann' is that you can display and manipulate the annotated data once the TFH_Annotated_Dataset.zip is unzipped under corresponding repository of Brat.

TFH_Annotated_Dataset contains 22,833 entity mentions and 17,412 semantic relation mentions. With TFH_Annotated_Dataset, we run two tasks of information extraction including named entity recognition with BiLSTM-CRF[3] and semantic relation extractionand with BiGRU-2ATTENTION[4]. For improving semantic representation of patent language, the word embeddings are trained with the abstract of 46,302 patents regarding magnetic head in hard disk drive, which turn out to improve the performance of named entity recognition by 0.3% and semantic relation extraction by about 2% in weighted average F1, compared to GloVe and the patent word embedding provided by Risch et al[5].

For named entity recognition, the weighted-average precision, recall, F1-value of BiLSTM-CRF on entity-level for the test set are 78.5%, 78.0%, and 78.2%, respectively. Although such performance is acceptable, it is still lower than its performance on general-purpose dataset by more than 10% in F1-value. The main reason is the limited amount of labeled dataset.

The precision, recall, and F1-value for each type of entity is shown in Fig. 4. As to relation extraction, the weighted-average precision, recall, F1-value of BiGRU-2ATTENTION for the test set are 89.7%, 87.9%, and 88.6% with no_edge relations, and 32.3%, 41.5%, 36.3% without no_edge relations.

Academic citing Chen, L., Xu, S*., Zhu, L. et al. A deep learning based method for extracting semantic information from patent documents. Scientometrics 125, 289–312 (2020). https://doi.org/10.1007/s11192-020-03634-y

Paper link https://link.springer.com/article/10.1007/s11192-020-03634-y

REFERENCE [1] Pérez-Pérez, M., Pérez-Rodríguez, G., Vazquez, M., Fdez-Riverola, F., Oyarzabal, J., Oyarzabal, J., Valencia,A., Lourenço, A., & Krallinger, M. (2017). Evaluation of chemical and gene/protein entity recognition systems at BioCreative V.5: The CEMP and GPRO patents tracks. In Proceedings of the Bio-Creative V.5 challenge evaluation workshop, pp. 11–18.

[2] Stenetorp, P., Pyysalo, S., Topić, G., Ohta, T., Ananiadou, S., & Tsujii, J. I. (2012). BRAT: a web-based tool for NLP-assisted text annotation. In Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics (pp. 102-107)

[3] Huang, Z., Xu, W., &Yu, K. (2015). Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991

[4] Han,X., Gao,T., Yao,Y., Ye,D., Liu,Z., Sun, M.(2019). OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction. arXiv preprint arXiv: 1301.3781

[5] Risch, J., & Krestel, R. (2019). Domain-specific word embeddings for patent classification. Data Technologies and Applications, 53(1), 108–122.

Background:

Whatizit is a text processing system that allows you to do text-mining tasks on text. It is great at identifying molecular biology terms and linking them to publicly available databases. Identified terms are wrapped with XML tags that carry additional information, such as the primary keys to the databases where all the relevant information is kept. The wrapping XML is translated into HTML hypertext links. This service is highly appreciated by people who are reading literature and need to quickly find more information about a particular term, e.g. its Gene Ontology term.

Whatizit is used in identifying formalized language patterns, specialized, syntactically formalized, technical notation. The annotation speed of a given pipeline is almost independent of the size of the vocabulary behind it and is currently based on pattern matching. In addition, several vocabularies can be integrated in a single pipeline.

Methodology:

The pipeline used is comprised of 175k Gene Ontology terms (preferred labels + synonyms). The annotation on Medline 2015-2019 corpus is done with Gene Ontology (GO) integrated dictionary.

The .zip file contains 10 XML files - each file is for half an year of MEDLINE annotated abstracts. In addition to the abstract, the title is also annotated for further information enrichment.

Respective DOIs, PMIDs are also included in the XML, when applicable.

Further development:

The XML files can be converted into JSON, JSON-LD format.

The technological advances of cutting-edge high-resolution mass spectrometry (HRMS) has set the stage for a new paradigm for exposure assessment. However, it is critical to ensure that bioinformatics software developed to process raw HRMS data can disentangle low-abundant xenobiotics from the noise. It is also essential to provide tools to speed up the annotation process. In this study, we optimized and compared the efficiency of open source (e.g. XCMS and MzMine2) and vendor software (e.g. MarkerViewTM and Progenesis QI) to detect low-abundant xenobiotics in human plasma and serum. We show that the rates of false negative can be decreased to below 5% when critical parameters are identified and tuned. Even though similar performances can be achieved using the best tuning for all software, the best detection rate was observed for MzMine2 (ADAP pipeline). We then developed an automatized suspect screening workflow (XenoScreener) based on the mass, three Rt prediction models and isotopic pattern. Indicators were developed to provide intermediate annotation scores for each predictor as well as a global score. With XenoScreener, we show that it is possible to efficiently pre-annotate with a high level of confidence a mix of xenobiotics spiked at real-life concentrations in human plasma and serum samples in a very short time (i.e. less than 3 hours after acquisition). We also demonstrate XenoScreener’s high efficiency for the rapid annotation of various xenobiotics (pharmaceuticals, lifestyle markers, plasticizers, flame-retardant, antifungals) in human plasma and serum using a library of about 2200 xenobiotics (annotation confirmed with MS/MS data).

The data published here are a supplementary for a paper to be published in Metaphor and Social Words (under revision).

Two debates organised and published by TVP and TVN were transcribed and annotated with Metaphor Identification Method. We have used eMargin software (a collaborative textual annotation tool, (Kehoe and Gee 2013) and a slightly modified version of MIP (Pragglejaz 2007). Each lexical unit in the transcript was labelled as a metaphor related word (MRW) if its “contextual meaning was related to the more basic meaning by some form of similarity” (Steen 2007). The meanings were established with the Wielki Słownik Języka Polskiego (Great Dictionary of Polish, ed. (Żmigrodzki 2019). In addition to MRW, lexemes which create a metaphorical expression together with MRW were tagged as metaphor expression word (MEW). At least two words are needed to identify the actual metaphorical expression, since MRW cannot appear without MEW. Grammatical construction of the metaphor (Sullivan 2009) is asymmetrical: one word is conceptually autonomous and the other is conceptually dependent on the first. Within construction grammar terms (Langacker 2008), metaphor related word is elaborated with/by metaphorical expression word, because the basic meaning of MRW is elaborated and extended to more figurative meaning only if it is used jointly with MEW. Moreover, the meaning of the MEW is rather basic, concrete, as it remains unchanged in connection with the MRW. This can be clearly seen in the expression often used in our data: “Służba zdrowia jest w zapaści” (“Health service suffers from a collapse.”) where the word “zapaść” (“collapse”) is an example of MRW and words “służba zdrowia” (“health service”) are labeled as MEW. The English translation of this expression needs a different verb, instead of “jest w zapaści” (“is in collapse”) the English unmarked collocation is “suffers from a collapse”, therefore words “suffers from a collapse” are labeled as MRW. The “collapse” could be caused by heart failure, such as cardiac arrest or any other life-threatening medical condition and “health service” is portrayed as if it could literally suffer from such a condition – a collapse.

The data are in csv tables exported from xml files downloaded from eMargin site. Prior to annotation transcripts were divided to 40 parts, each for one annotator. MRW words are marked as MLN, MEW are marked as MLP and functional words within metaphorical expression are marked MLI, other words are marked just noana, which means no annotation needed.

A total of eight deployments of an autonomous baited camera lander were conducted at the Cabo Verde Abyssal Plain (tropical East Atlantic, Lat. 14.72, Lon. -25.19, Water depth ~4200 m) using either Atlantic mackerel (Scomber scombrus, n=4) or Patagonian squid (Doryteuthis gahi, n=4) bait, to photograph organisms attracted to the bait over roughly 24 hours. The deployments took place during the iMirabilis2 campaign in August 2021 from the research vessel Sarmiento de Gamboa. A deep-sea time lapse camera system with an oblique view of the bait plate (12 cm x 45 cm) and surroundings took a picture every 150 seconds. The bar attached to the bait plate is 6 cm wide. The camera was located about 120 cm above the seafloor with an oblique view of 40 degrees (assuming straight down in 0 degrees). Annotations were performed in BIIGLE software (Langenkämper et al. 2017) on every second photograph, providing the morphospecies group label (or 'No ID' if to morphospecies level was not possible) and the taxonomic hierarchy to a level of best confidence for each annotation. Annotations were rectangular in shape, enclosing each individual so that the centre of the annotation was roughly the centre of mass, and the points of each rectangle corner are provided in pixels (x,y) where the lower left corner of the picture is 0,0. Images were 6000 pixels in width and 4000 pixels in height.

The dataset includes taxon counts of the observed background community at the stations prior to the baited lander deployments as well as area calculations per image. Framegrab images from the OFOS videos were extracted in VLC media player (3.0.11 Vetinari) using the snapshot-tool. Framegrabs were taken every 1.5 minute and additionally as close to the seafloor as possible to have the highest possible resolution. Because of variable image quality, the images were classified into good, medium, and poor quality. In each collected image, organisms were identified to the highest possible taxonomic unit and counted with the Multi-Point tool in ImageJ (ImageJ 1.53g). The annotated area in each image was calculated by setting the scale in ImageJ and then calculating the area with the measure-tool. The total area surveyed was calculated by the sum of the analysed images (total = 8-53 m2 per study region).

Annotating product ion peaks in tandem mass spectra is essential for evaluating spectral quality and validating peptide identification. This task is more complex for glycopeptides and is crucial for the confident determination of glycosylation sites in glycoproteins. MS_Piano (Mass Spectrum Peptide Annotation) software was developed for reliable annotation of peaks in collision induced dissociation (CID) tandem mass spectra of peptides or N-glycopeptides for given peptide sequences, charge states, and optional modifications. The program annotates each peak in high or low resolution spectra with possible product ion(s) and the mass difference between the measured and theoretical m/z values. Spectral quality is measured by two major parameters: the ratio between the sum of unannotated vs all peak intensities in the top 20 peaks, and the intensity of the highest unannotated peak. The product ions of peptides, glycans, and glycopeptides in spectra are labeled in different class-type colors to facilitate interpretation. MS_Piano assists validating peptide and N-glycopeptide identification from database and library searches and provides quality control and optimizes search reliability in custom developed peptide mass spectral libraries. The software is freely available in .exe and .dll formats for the Windows operating system.

This is an Annotation for Transparent Inquiry (ATI) data project. The annotated article can be viewed on the publisher's website. We have concentrated on a number of key stages of sociolinguistic research, with specific reference to collection, processing and statistical analysis of data. Recordings of speech data: we are linguists working on speech data, yet we rely on written data to convey the core materials we work with. We thus include examples of actual speech recordings to provide concrete support for our claim that the data we are working with diverges significantly from mainstream norms. Data preparation and coding Transcription – example of protocol in action: the transcription of speech data must satisfy two, often competing, criteria: it has to be 1) an accurate reflection of what was actually said and 2) transparent and accessible for analysis. How this is achieved is no easy feat, thus we include the full transcription protocol here in order to highlight the complexities in representing speech data in written format: what changes, what does not, and why. Coding and annotation – from sound file to transcript to coded data: this phase of the research is often relegated to one or two lines in a journal article. This is highlighted by our own paper which states that ‘we extracted approx. 100 tokens per speaker per insider/outsider interview’. In this annotation we show how this is actually done, demonstrating how we isolate the linguistic variable in the original text to sound-aligned transcribed data, and how this annotation prepares for eventual extraction of the variable context under analysis. Coding schema: the coding schema arises from two different sources: 1) what has been found in previous research; 2) observation of the current data. As such, there are multiple possibilities for what governs the observed variability. The initial coding schema sets out to test these multiple possibilities. Occam’s Razor is then applied to these multiple categories in sifting the data for the best fit, resulting in a leaner, more interpretable coda schema as presented in the final article. We have included in this annotation the original more elaborated categories to highlight the behind the scenes work that takes place in making sense of the data. We also include sound files of the actual variants used. This allows the user to hear the different environments set out in the final coding schema as used in the object of study: spontaneous speech data. Statistical analysis – the program used: a challenge of statistical analysis is that field constantly evolves. This annotation is a case in point where the version of the program we used is now deprecated and no longer supported. The new version is more than a superficial change to the graphical interface and represents a completely different approach in the way the models are built (stepping-up based on p-values as opposed to stepping-down from fully saturated models). The wider implication is that this can mean that analyses are not fully replicable, particularly as the software becomes obsolete, thus we provide further information on the program used to highlight this potential problem. Statistical analysis – procedure: the description of the statistical analysis which appears in the final journal article is usually a ‘final model’ outlined in a linear fashion but the reality is a model that results from many different iterations where many different models are run and cross-referenced. The final model is a pay off between accuracy and elegance; we are aiming for the ‘best-fit’ but also the simplest or most straightforward computation. As we outline, in this case we decided to model each generation separately as this provided a clearer route to answer our research questions. However, other analysts may argue that a fully saturated model which represents all the interactions together is more accurate. Including this annotation provides further rationale for the model(s) we eventually used in the article.

Dataset Overview databricks-dolly-15k is a corpus of more than 15,000 records generated by thousands of Databricks employees to enable large language models to exhibit the magical interactivity of ChatGPT. Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories, including the seven outlined in the InstructGPT paper, as well as an open-ended free-form category. The contributors were instructed to avoid using information from any source on the web with the exception of Wikipedia (for particular subsets of instruction categories), and explicitly instructed to avoid using generative AI in formulating instructions or responses. Examples of each behavior were provided to motivate the types of questions and instructions appropriate to each category.

Halfway through the data generation process, contributors were given the option of answering questions posed by other contributors. They were asked to rephrase the original question and only select questions they could be reasonably expected to answer correctly.

For certain categories contributors were asked to provide reference texts copied from Wikipedia. Reference text (indicated by the context field in the actual dataset) may contain bracketed Wikipedia citation numbers (e.g. [42]) which we recommend users remove for downstream applications.

Intended Uses While immediately valuable for instruction fine tuning large language models, as a corpus of human-generated instruction prompts, this dataset also presents a valuable opportunity for synthetic data generation in the methods outlined in the Self-Instruct paper. For example, contributor--generated prompts could be submitted as few-shot examples to a large open language model to generate a corpus of millions of examples of instructions in each of the respective InstructGPT categories.

Likewise, both the instructions and responses present fertile ground for data augmentation. A paraphrasing model might be used to restate each prompt or short responses, with the resulting text associated to the respective ground-truth sample. Such an approach might provide a form of regularization on the dataset that could allow for more robust instruction-following behavior in models derived from these synthetic datasets.

Dataset Purpose of Collection As part of our continuing commitment to open source, Databricks developed what is, to the best of our knowledge, the first open source, human-generated instruction corpus specifically designed to enable large language models to exhibit the magical interactivity of ChatGPT. Unlike other datasets that are limited to non-commercial use, this dataset can be used, modified, and extended for any purpose, including academic or commercial applications.

Sources Human-generated data: Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories. Wikipedia: For instruction categories that require an annotator to consult a reference text (information extraction, closed QA, summarization) contributors selected passages from Wikipedia for particular subsets of instruction categories. No guidance was given to annotators as to how to select the target passages. Annotator Guidelines To create a record, employees were given a brief description of the annotation task as well as examples of the types of prompts typical of each annotation task. Guidelines were succinct by design so as to encourage a high task completion rate, possibly at the cost of rigorous compliance to an annotation rubric that concretely and reliably operationalizes the specific task. Caveat emptor.

The annotation guidelines for each of the categories are as follows:

Creative Writing: Write a question or instruction that requires a creative, open-ended written response. The instruction should be reasonable to ask of a person with general world knowledge and should not require searching. In this task, your prompt should give very specific instructions to follow. Constraints, instructions, guidelines, or requirements all work, and the more of them the better. Closed QA: Write a question or instruction that requires factually correct response based on a passage of text from Wikipedia. The question can be complex and can involve human-level reasoning capabilities, but should not require special knowledge. To create a question for this task include both the text of the question as well as the reference text in the form. Open QA: Write a question that can be answered using general world knowledge or at most a single search. This task asks for opinions and facts about the world at large and does not provide any reference text for consultation. Summarization: Give a summary of a paragraph from Wikipedia. Please don't ask questions that will require more than 3-5 minutes to answer. To create a question for this task include both the text of the question as well as the reference text in the form. Information Extraction: T

B. subtilis and E. coli cell segmentation dataset consisting of

test data annotated by three experts (test),

data annotated manually by a single microbeSEG user within 30 minutes (30min-man),

data annotated manually by a single microbeSEG user within 30 minutes and data annotated with microbeSEG pre-labeling with 15 minutes manual correction time (30min-man_15min-pre, includes the 30min-man dataset).

Images, instance segmentation masks and image-segmentation overlays are provided. All images are crops of size 320px x 320px. Annotations were made with ObiWan-Microbi.

Data acquisition

The phase contrast images of growing B. subtilis and E. coli colonies were acquired with a fully automated time-lapse microscope setup (TI Eclipse, Nikon, Germany) using a 100x oil immersion objective (Plan Apochromat λ Oil, N.A. 1.45, WD 170 µm, Nikon Microscopy). Time-lapse images were taken every 15 minutes for B. subtilis and every 20 minutes for E. coli. Cultivation took place inside a special microfluidic cultivation device. Resolution: 0.07μm/px for B. subtilis und 0.09μm/px for E. coli.

microbeSEG import

For the use with microbeSEG, create or select a new training set within the software and use the training data import functionality. Best import train data with the "train" checkbox checked, validation data with the "val" checkbox checked, and test data with the "test" checkbox checked. Since the images are already normalized, the "keep normalization" functionality can be used.

HAMAP stands for High-quality Automated and Manual Annotation of Proteins. HAMAP profiles are manually created by expert curators. They identify proteins that are part of well-conserved protein families or subfamilies. HAMAP is based at the SIB Swiss Institute of Bioinformatics, Geneva, Switzerland.