Dataset Overview

This dataset consists of 42,052 English words and their corresponding definitions. It is a comprehensive collection of words ranging from common terms to more obscure vocabulary. The dataset is ideal for Natural Language Processing (NLP) tasks, educational tools, and various language-related applications.

Key Features:

• Words: A diverse set of English words, including both rare and frequently used terms.
• Definitions: Each word is accompanied by a detailed definition that explains its meaning and contextual usage.

Total Number of Words: 42,052

Applications

This dataset is well-suited for a range of use cases, including:

• Natural Language Processing (NLP): Enhance text understanding models by providing contextual meaning and word associations.
• Vocabulary Building: Create educational tools or games that help users expand their vocabulary.
• Lexical Studies: Perform academic research on word usage, trends, and lexical semantics.
• Dictionary and Thesaurus Development: Serve as a resource for building dictionary or thesaurus applications, where users can search for words and definitions.

Data Structure

• Word: The column containing the English word.
• Definition: The column providing a comprehensive definition of the word.

Potential Use Cases

• Language Learning: This dataset can be used to develop applications or tools aimed at enhancing vocabulary acquisition for language learners.
• NLP Model Training: Useful for tasks such as word embeddings, definition generation, and contextual learning.
• Research: Analyze word patterns, rare vocabulary, and trends in the English language.

This version focuses on providing essential information while emphasizing the total number of words and potential applications of the dataset. Let me know if you'd like any further adjustments!

Dataset for Beginners to start Data Science process. The subject of data is about simple clinical data for problem definition and solving. range of data science tasks such as classification, clustering, EDA and statistical analysis are using with dataset.

columns in data set are present: Age: Numerical (Age of patient) Sex: Binary (Gender of patient) BP: Nominal (Blood Pressure of patient with values: Low, Normal and High) Cholesterol: Nominal (Cholesterol of patient with values: Normal and High) Na: Numerical (Sodium level of patient experiment) K: Numerical (Potassium level of patient experiment) Drug: Nominal (Type of Drug that prescribed with doctor, with values: A, B, C, X and Y)

Complete dataset of “Film Circulation on the International Film Festival Network and the Impact on Global Film Culture”

A peer-reviewed data paper for this dataset is in review to be published in NECSUS_European Journal of Media Studies - an open access journal aiming at enhancing data transparency and reusability, and will be available from https://necsus-ejms.org/ and https://mediarep.org

Please cite this when using the dataset.

Detailed description of the dataset:

1 Film Dataset: Festival Programs

The Film Dataset consists a data scheme image file, a codebook and two dataset tables in csv format.

The codebook (csv file “1_codebook_film-dataset_festival-program”) offers a detailed description of all variables within the Film Dataset. Along with the definition of variables it lists explanations for the units of measurement, data sources, coding and information on missing data.

The csv file “1_film-dataset_festival-program_long” comprises a dataset of all films and the festivals, festival sections, and the year of the festival edition that they were sampled from. The dataset is structured in the long format, i.e. the same film can appear in several rows when it appeared in more than one sample festival. However, films are identifiable via their unique ID.

The csv file “1_film-dataset_festival-program_wide” consists of the dataset listing only unique films (n=9,348). The dataset is in the wide format, i.e. each row corresponds to a unique film, identifiable via its unique ID. For easy analysis, and since the overlap is only six percent, in this dataset the variable sample festival (fest) corresponds to the first sample festival where the film appeared. For instance, if a film was first shown at Berlinale (in February) and then at Frameline (in June of the same year), the sample festival will list “Berlinale”. This file includes information on unique and IMDb IDs, the film title, production year, length, categorization in length, production countries, regional attribution, director names, genre attribution, the festival, festival section and festival edition the film was sampled from, and information whether there is festival run information available through the IMDb data.

2 Survey Dataset

The Survey Dataset consists of a data scheme image file, a codebook and two dataset tables in csv format.

The codebook “2_codebook_survey-dataset” includes coding information for both survey datasets. It lists the definition of the variables or survey questions (corresponding to Samoilova/Loist 2019), units of measurement, data source, variable type, range and coding, and information on missing data.

The csv file “2_survey-dataset_long-festivals_shared-consent” consists of a subset (n=161) of the original survey dataset (n=454), where respondents provided festival run data for films (n=206) and gave consent to share their data for research purposes. This dataset consists of the festival data in a long format, so that each row corresponds to the festival appearance of a film.

The csv file “2_survey-dataset_wide-no-festivals_shared-consent” consists of a subset (n=372) of the original dataset (n=454) of survey responses corresponding to sample films. It includes data only for those films for which respondents provided consent to share their data for research purposes. This dataset is shown in wide format of the survey data, i.e. information for each response corresponding to a film is listed in one row. This includes data on film IDs, film title, survey questions regarding completeness and availability of provided information, information on number of festival screenings, screening fees, budgets, marketing costs, market screenings, and distribution. As the file name suggests, no data on festival screenings is included in the wide format dataset.

3 IMDb & Scripts

The IMDb dataset consists of a data scheme image file, one codebook and eight datasets, all in csv format. It also includes the R scripts that we used for scraping and matching.

The codebook “3_codebook_imdb-dataset” includes information for all IMDb datasets. This includes ID information and their data source, coding and value ranges, and information on missing data.

The csv file “3_imdb-dataset_aka-titles_long” contains film title data in different languages scraped from IMDb in a long format, i.e. each row corresponds to a title in a given language.

The csv file “3_imdb-dataset_awards_long” contains film award data in a long format, i.e. each row corresponds to an award of a given film.

The csv file “3_imdb-dataset_companies_long” contains data on production and distribution companies of films. The dataset is in a long format, so that each row corresponds to a particular company of a particular film.

The csv file “3_imdb-dataset_crew_long” contains data on names and roles of crew members in a long format, i.e. each row corresponds to each crew member. The file also contains binary gender assigned to directors based on their first names using the GenderizeR application.

The csv file “3_imdb-dataset_festival-runs_long” contains festival run data scraped from IMDb in a long format, i.e. each row corresponds to the festival appearance of a given film. The dataset does not include each film screening, but the first screening of a film at a festival within a given year. The data includes festival runs up to 2019.

The csv file “3_imdb-dataset_general-info_wide” contains general information about films such as genre as defined by IMDb, languages in which a film was shown, ratings, and budget. The dataset is in wide format, so that each row corresponds to a unique film.

The csv file “3_imdb-dataset_release-info_long” contains data about non-festival release (e.g., theatrical, digital, tv, dvd/blueray). The dataset is in a long format, so that each row corresponds to a particular release of a particular film.

The csv file “3_imdb-dataset_websites_long” contains data on available websites (official websites, miscellaneous, photos, video clips). The dataset is in a long format, so that each row corresponds to a website of a particular film.

The dataset includes 8 text files containing the script for webscraping. They were written using the R-3.6.3 version for Windows.

The R script “r_1_unite_data” demonstrates the structure of the dataset, that we use in the following steps to identify, scrape, and match the film data.

The R script “r_2_scrape_matches” reads in the dataset with the film characteristics described in the “r_1_unite_data” and uses various R packages to create a search URL for each film from the core dataset on the IMDb website. The script attempts to match each film from the core dataset to IMDb records by first conducting an advanced search based on the movie title and year, and then potentially using an alternative title and a basic search if no matches are found in the advanced search. The script scrapes the title, release year, directors, running time, genre, and IMDb film URL from the first page of the suggested records from the IMDb website. The script then defines a loop that matches (including matching scores) each film in the core dataset with suggested films on the IMDb search page. Matching was done using data on directors, production year (+/- one year), and title, a fuzzy matching approach with two methods: “cosine” and “osa.” where the cosine similarity is used to match titles with a high degree of similarity, and the OSA algorithm is used to match titles that may have typos or minor variations.

The script “r_3_matching” creates a dataset with the matches for a manual check. Each pair of films (original film from the core dataset and the suggested match from the IMDb website was categorized in the following five categories: a) 100% match: perfect match on title, year, and director; b) likely good match; c) maybe match; d) unlikely match; and e) no match). The script also checks for possible doubles in the dataset and identifies them for a manual check.

The script “r_4_scraping_functions” creates a function for scraping the data from the identified matches (based on the scripts described above and manually checked). These functions are used for scraping the data in the next script.

The script “r_5a_extracting_info_sample” uses the function defined in the “r_4_scraping_functions”, in order to scrape the IMDb data for the identified matches. This script does that for the first 100 films, to check, if everything works. Scraping for the entire dataset took a few hours. Therefore, a test with a subsample of 100 films is advisable.

The script “r_5b_extracting_info_all” extracts the data for the entire dataset of the identified matches.

The script “r_5c_extracting_info_skipped” checks the films with missing data (where data was not scraped) and tried to extract data one more time to make sure that the errors were not caused by disruptions in the internet connection or other technical issues.

The script “r_check_logs” is used for troubleshooting and tracking the progress of all of the R scripts used. It gives information on the amount of missing values and errors.

4 Festival Library Dataset

The Festival Library Dataset consists of a data scheme image file, one codebook and one dataset, all in csv format.

The codebook (csv file “4_codebook_festival-library_dataset”) offers a detailed description of all variables within the Library Dataset. It lists the definition of variables, such as location and festival name, and festival categories, units of measurement, data sources and coding and missing data.

The csv file “4_festival-library_dataset_imdb-and-survey” contains data on all unique festivals collected from both IMDb and survey sources. This dataset appears in wide format, all information for each festival is listed in one row. This

The Dictionary of Algorithms and Data Structures (DADS) is an online, publicly accessible dictionary of generally useful algorithms, data structures, algorithmic techniques, archetypal problems, and related definitions. In addition to brief definitions, some entries have links to related entries, links to implementations, and additional information. DADS is meant to be a resource for the practicing programmer, although students and researchers may find it a useful starting point. DADS has fundamental entries in areas such as theory, cryptography and compression, graphs, trees, and searching, for instance, Ackermann's function, quick sort, traveling salesman, big O notation, merge sort, AVL tree, hash table, and Byzantine generals. DADS also has index pages that list entries by area and by type. Currently DADS does not include algorithms particular to business data processing, communications, operating systems or distributed algorithms, programming languages, AI, graphics, or numerical analysis.

Brief Explanation

This dataset is created to develop and evaluate a cataloging system which assigns appropriate metadata to an image record for database management in digital libraries. That is assumed for evaluating a task, in which given an image and assigned tags, an appropriate Wikipedia page is selected for each of the given tags.

A main characteristic of the dataset is including ambiguous tags. Thus, visual contents of images are not unique to their tags. For example, it includes a tag 'mouse' which has double meaning of not a mammal but a computer controller device. The annotations are corresponding Wikipedia articles for tags as correct entities by human judgement.

The dataset offers both data and programs that reproduce experiments of the above-mentioned task. Its data consist of sources of images and annotations. The image sources are URLs of 420 images uploaded to Flickr. The annotations are a total 2,464 relevant Wikipedia pages manually judged for tags of the images. The dataset also provides programs in Jupiter notebook (scripts.ipynb) to conduct a series of experiments running some baseline methods for the designated task and evaluating the results.

Structure of the Dataset

1. data directory 1.1. image_URL.txt This file lists URLs of image files.

   1.2. rels.txt This file lists collect Wikipedia pages for each topic in topics.txt

   1.3. topics.txt This file lists a target pair, which is called a topic in this dataset, of an image and a tag to be disambiguated.

   1.4. enwiki_20171001.xml This file is extracted texts from the title and body parts of English Wikipedia articles as of 1st October 2017. This is a modified data of Wikipedia dump data (https://archive.org/download/enwiki-20171001).

2. img directory This directory is a placeholder directory to fetch image files for downloading.

3. results directory This directory is a placeholder directory to store results files for evaluation. It maintains three results of baseline methods in sub-directories. They contain json files each of which is a result of one topic, and are ready to be evaluated using an evaluation scripts in scripts.ipynb for reference of both usage and performance.

4. scripts.ipynb The scripts for running baseline methods and evaluation are ready in this Jupyter notebook file.

Linguistically annotated Spanish language datasets with headwords, definitions, senses, examples, POS tags, semantic metadata, and usage info. Ideal for dictionary tools, NLP, and TTS model training or fine-tuning.

Our Spanish language datasets are carefully compiled and annotated by language and linguistic experts; you can find them available for licensing:

1. Spanish Monolingual Dictionary Data
2. Spanish Bilingual Dictionary Data
3. Spanish Sentences Data
4. Synonyms and Antonyms Data
5. Audio Data
6. Spanish Word List Data

Key Features (approximate numbers):

1. Spanish Monolingual Dictionary Data

Our Spanish monolingual reliably offers clear definitions and examples, a large volume of headwords, and comprehensive coverage of the Spanish language.

• Words: 73,000
• Senses: 123,000
• Example sentences: 104,000
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. Spanish Bilingual Dictionary Data

The bilingual data provides translations in both directions, from English to Spanish and from Spanish to English. It is annually reviewed and updated by our in-house team of language experts. Offers significant coverage of the language, providing a large volume of translated words of excellent quality.

• Translations: 221,300
• Senses: 103,500
• Example sentences: 74,500
• Example translations: 83,800
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. Spanish Sentences Data

Spanish sentences retrieved from the corpus are ideal for NLP model training, presenting approximately 20 million words. The sentences provide a great coverage of Spanish-speaking countries and are accordingly tagged to a particular country or dialect.

• Sentences volume: 1,840,000
• Format: XML and JSON format
• Delivery: Email (link-based file sharing) and REST API

1. Spanish Synonyms and Antonyms Data

This Spanish language dataset offers a rich collection of synonyms and antonyms, accompanied by detailed definitions and part-of-speech (POS) annotations, making it a comprehensive resource for building linguistically aware AI systems and language technologies.

• Synonyms: 127,700
• Antonyms: 9,500
• Format: XML format
• Delivery: Email (link-based file sharing)
• Updated frequency: annually

1. Spanish Audio Data (word-level)

Curated word-level audio data for the Spanish language, which covers all varieties of world Spanish, providing rich dialectal diversity in the Spanish language.

• Audio files: 20,900
• Format: XLSX (for index), MP3 and WAV (audio files)

1. Spanish Word List Data

This language data contains a carefully curated and comprehensive list of 450,000 Spanish words.

• Wordforms: 450,000
• Format: CSV and TXT formats
• Delivery: Email (link-based file sharing)

Use Cases:

We consistently work with our clients on new use cases as language technology continues to evolve. These include NLP applications, TTS, dictionary display tools, games, translation, word embedding, and word sense disambiguation (WSD).

If you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Oxford.Languages@oup.com to start the conversation.

Pricing:

Oxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs.

Contact our team or email us at Oxford.Languages@oup.com to explore pricing options and discover how our language data can support your goals.

About the sample:

The samples offer a brief overview of one or two language datasets (monolingual or/and bilingual dictionary data). To help you explore the structure and features of our dataset, we provide a sample in CSV format for preview purposes only.

If you need the complete original sample or more details about any dataset, please contact us (Growth.OL@oup.com) to request access or further information

With recent technological advancements, quantitative analysis has become an increasingly important area within professional sports. However, the manual process of collecting data on relevant match events like passes, goals and tacklings comes with considerable costs and limited consistency across providers, affecting both research and practice. In football, while automatic detection of events from positional data of the players and the ball could alleviate these issues, it is not entirely clear what accuracy current state-of-the-art methods realistically achieve because there is a lack of high-quality validations on realistic and diverse data sets. This paper adds context to existing research by validating a two-step rule-based pass and shot detection algorithm on four different data sets using a comprehensive validation routine that accounts for the temporal, hierarchical and imbalanced nature of the task. Our evaluation shows that pass and shot detection performance is highly dependent on the specifics of the data set. In accordance with previous studies, we achieve F-scores of up to 0.92 for passes, but only when there is an inherent dependency between event and positional data. We find a significantly lower accuracy with F-scores of 0.71 for passes and 0.65 for shots if event and positional data are independent. This result, together with a critical evaluation of existing methodologies, suggests that the accuracy of current football event detection algorithms operating on positional data is currently overestimated. Further analysis reveals that the temporal extraction of passes and shots from positional data poses the main challenge for rule-based approaches. Our results further indicate that the classification of plays into shots and passes is a relatively straightforward task, achieving F-scores between 0.83 to 0.91 ro rule-based classifiers and up to 0.95 for machine learning classifiers. We show that there exist simple classifiers that accurately differentiate shots from passes in different data sets using a low number of human-understandable rules. Operating on basic spatial features, our classifiers provide a simple, objective event definition that can be used as a foundation for more reliable event-based match analysis.

Comprehensive Portuguese language datasets with linguistic annotations, including headwords, definitions, word senses, usage examples, part-of-speech (POS) tags, semantic metadata, and contextual usage details. Perfect for powering dictionary platforms, NLP, AI models, and translation systems.

Our Portuguese language datasets are carefully compiled and annotated by language and linguistic experts. The below datasets in Portuguese are available for license:

1. Portuguese Monolingual Dictionary Data
2. Portuguese Bilingual Dictionary Data

Key Features (approximate numbers):

1. Portuguese Monolingual Dictionary Data

Our Portuguese monolingual covers both EU and LATAM varieties, featuring clear definitions and examples, a large volume of headwords, and comprehensive coverage of the Portuguese language.

• Words:143,600
• Senses: 285,500
• Example sentences: 69,300
• Format: XML format
• Delivery: Email (link-based file sharing)

1. Portuguese Bilingual Dictionary Data

The bilingual data provides translations in both directions, from English to Portuguese and from Portuguese to English. It is annually reviewed and updated by our in-house team of language experts. Offers comprehensive coverage of the language, providing a substantial volume of translated words of excellent quality that span both EU and LATAM Portuguese varieties.

• Translations: 300,000
• Senses: 158,000
• Example translations: 117,800
• Format: XML and JSON format
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

Use Cases:

We consistently work with our clients on new use cases as language technology continues to evolve. These include Natural Language Processing (NLP) applications, TTS, dictionary display tools, games, translations, word embedding, and word sense disambiguation (WSD).

If you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Growth.OL@oup.com to start the conversation.

Pricing:

Oxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs.

Contact our team or email us at Growth.OL@oup.com to explore pricing options and discover how our language data can support your goals.

About the sample:

The samples offer a brief overview of one or two language datasets (monolingual or/and bilingual dictionary data). To help you explore the structure and features of our dataset, we provide a sample in CSV format for preview purposes only.

If you need the complete original sample or more details about any dataset, please contact us (Growth.OL@oup.com) to request access or further information

Comprehensive Chinese language datasets with linguistic annotations, including headwords, definitions, word senses, usage examples, part-of-speech (POS) tags, semantic metadata, and contextual usage details. Covering Simplified and Traditional writing systems.

Our Chinese language datasets are carefully compiled and annotated by language and linguistic experts. The below datasets are available for license:

1. Mandarin Chinese (simplified) Monolingual Dictionary Data
2. Mandarin Chinese (traditional) Monolingual Dictionary Data
3. Mandarin Chinese (simplified) Bilingual Dictionary Data
4. Mandarin Chinese (traditional) Bilingual Dictionary Data
5. Mandarin Chinese (simplified) Synonyms and Antonyms Data

Key Features (approximate numbers):

1. Mandarin Chinese (simplified) Monolingual Dictionary Data

Our Mandarin Chinese (simplified) monolingual features clear definitions, headwords, examples, and comprehensive coverage of the Mandarin Chinese language spoken today.

• Words: 81,300
• Senses: 62,400
• Example sentences: 80,700
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API

1. Mandarin Chinese (traditional) Monolingual Dictionary Data

Our Mandarin Chinese (traditional) monolingual features clear definitions, headwords, examples, and comprehensive coverage of the Mandarin Chinese language spoken today.

• Words: 60,100
• Senses: 144,700
• Example sentences: 29,900
• Format: XML format
• Delivery: Email (link-based file sharing)

1. Mandarin Chinese (simplified) Bilingual Dictionary Data

The bilingual data provides translations in both directions, from English to Mandarin Chinese (simplified) and from Mandarin Chinese (simplified) to English. It is annually reviewed and updated by our in-house team of language experts. Offers comprehensive coverage of the language, providing a substantial volume of translated words of excellent quality.

• Translations: 367,600
• Senses: 204,500
• Example translations: 150,900
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. Mandarin Chinese (traditional) Bilingual Dictionary Data

The bilingual data provides translations in both directions, from English to Mandarin Chinese (traditional) and from Mandarin Chinese (traditional) to English. It is annually reviewed and updated by our in-house team of language experts. Offers comprehensive coverage of the language, providing a substantial volume of translated words of excellent quality.

• Translations: 215,600
• Senses: 202,800
• Example sentences: 149,700
• Format: XML format
• Delivery: Email (link-based file sharing)

1. Mandarin Chinese (simplified) Synonyms and Antonyms Data

The Mandarin Chinese (simplified) Synonyms and Antonyms Dataset is a leading resource offering comprehensive, up-to-date coverage of word relationships in contemporary Mandarin Chinese. It includes rich linguistic detail such as precise definitions and part-of-speech (POS) tags, making it an essential asset for developing AI systems and language technologies that require deep semantic understanding.

• Synonyms: 3,800
• Antonyms: 3,180
• Format: XML format
• Delivery: Email (link-based file sharing)

Use Cases:

We consistently work with our clients on new use cases as language technology continues to evolve. These include NLP applications, TTS, dictionary display tools, games, translation, word embedding, and word sense disambiguation (WSD).

If you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Growth.OL@oup.com to start the conversation.

Pricing:

Oxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs.

Contact our team or email us at Growth.OL@oup.com to explore pricing options and discover how our language data can support your goals.

Please note that some datasets may have rights restrictions. Contact us for more information.

About the sample:

The samples offer a brief overview of one or two language datasets (monolingual or/and bilingual dictionary data). To help you explore the structure and features of our dataset, we provide a sample in CSV format for preview purposes only.

If you need the complete original sample or more details about any dataset, please contact us (Growth.OL@oup.com) to request access or further information.

General

For more details and the most up-to-date information please consult our project page: https://kainmueller-lab.github.io/fisbe.

Summary

A new dataset for neuron instance segmentation in 3d multicolor light microscopy data of fruit fly brains

30 completely labeled (segmented) images

71 partly labeled images

altogether comprising ∼600 expert-labeled neuron instances (labeling a single neuron takes between 30-60 min on average, yet a difficult one can take up to 4 hours)

To the best of our knowledge, the first real-world benchmark dataset for instance segmentation of long thin filamentous objects

A set of metrics and a novel ranking score for respective meaningful method benchmarking

An evaluation of three baseline methods in terms of the above metrics and score

Abstract

Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging, thin filamentous and widely branching morphologies, multiple neurons are tightly inter-weaved, and partial volume effects, uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research, namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing, to date methods are typically benchmarked on synthetic datasets. To address this gap, we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset, the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition, we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly, we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies, and facilitate scientific discovery in basic neuroscience.

Dataset documentation:

We provide a detailed documentation of our dataset, following the Datasheet for Datasets questionnaire:

FISBe Datasheet

Our dataset originates from the FlyLight project, where the authors released a large image collection of nervous systems of ~74,000 flies, available for download under CC BY 4.0 license.

Files

fisbe_v1.0_{completely,partly}.zip

contains the image and ground truth segmentation data; there is one zarr file per sample, see below for more information on how to access zarr files.

fisbe_v1.0_mips.zip

maximum intensity projections of all samples, for convenience.

sample_list_per_split.txt

a simple list of all samples and the subset they are in, for convenience.

view_data.py

a simple python script to visualize samples, see below for more information on how to use it.

dim_neurons_val_and_test_sets.json

a list of instance ids per sample that are considered to be of low intensity/dim; can be used for extended evaluation.

Readme.md

general information

How to work with the image files

Each sample consists of a single 3d MCFO image of neurons of the fruit fly.For each image, we provide a pixel-wise instance segmentation for all separable neurons.Each sample is stored as a separate zarr file (zarr is a file storage format for chunked, compressed, N-dimensional arrays based on an open-source specification.").The image data ("raw") and the segmentation ("gt_instances") are stored as two arrays within a single zarr file.The segmentation mask for each neuron is stored in a separate channel.The order of dimensions is CZYX.

We recommend to work in a virtual environment, e.g., by using conda:

conda create -y -n flylight-env -c conda-forge python=3.9conda activate flylight-env

How to open zarr files

Install the python zarr package:

pip install zarr

Opened a zarr file with:

import zarrraw = zarr.open(, mode='r', path="volumes/raw")seg = zarr.open(, mode='r', path="volumes/gt_instances")

optional:import numpy as npraw_np = np.array(raw)

Zarr arrays are read lazily on-demand.Many functions that expect numpy arrays also work with zarr arrays.Optionally, the arrays can also explicitly be converted to numpy arrays.

How to view zarr image files

We recommend to use napari to view the image data.

Install napari:

pip install "napari[all]"

Save the following Python script:

import zarr, sys, napari

raw = zarr.load(sys.argv[1], mode='r', path="volumes/raw")gts = zarr.load(sys.argv[1], mode='r', path="volumes/gt_instances")

viewer = napari.Viewer(ndisplay=3)for idx, gt in enumerate(gts): viewer.add_labels( gt, rendering='translucent', blending='additive', name=f'gt_{idx}')viewer.add_image(raw[0], colormap="red", name='raw_r', blending='additive')viewer.add_image(raw[1], colormap="green", name='raw_g', blending='additive')viewer.add_image(raw[2], colormap="blue", name='raw_b', blending='additive')napari.run()

Execute:

python view_data.py /R9F03-20181030_62_B5.zarr

Metrics

S: Average of avF1 and C

avF1: Average F1 Score

C: Average ground truth coverage

clDice_TP: Average true positives clDice

FS: Number of false splits

FM: Number of false merges

tp: Relative number of true positives

For more information on our selected metrics and formal definitions please see our paper.

Baseline

To showcase the FISBe dataset together with our selection of metrics, we provide evaluation results for three baseline methods, namely PatchPerPix (ppp), Flood Filling Networks (FFN) and a non-learnt application-specific color clustering from Duan et al..For detailed information on the methods and the quantitative results please see our paper.

License

The FlyLight Instance Segmentation Benchmark (FISBe) dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Citation

If you use FISBe in your research, please use the following BibTeX entry:

@misc{mais2024fisbe, title = {FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures}, author = {Lisa Mais and Peter Hirsch and Claire Managan and Ramya Kandarpa and Josef Lorenz Rumberger and Annika Reinke and Lena Maier-Hein and Gudrun Ihrke and Dagmar Kainmueller}, year = 2024, eprint = {2404.00130}, archivePrefix ={arXiv}, primaryClass = {cs.CV} }

Acknowledgments

We thank Aljoscha Nern for providing unpublished MCFO images as well as Geoffrey W. Meissner and the entire FlyLight Project Team for valuablediscussions.P.H., L.M. and D.K. were supported by the HHMI Janelia Visiting Scientist Program.This work was co-funded by Helmholtz Imaging.

Changelog

There have been no changes to the dataset so far.All future change will be listed on the changelog page.

Contributing

If you would like to contribute, have encountered any issues or have any suggestions, please open an issue for the FISBe dataset in the accompanying github repository.

All contributions are welcome!

This dataset is about books. It has 1 row and is filtered where the book is Dictionary of herpetology : a brief and meaningful definition of words and terms used in herpetology. It features 7 columns including author, publication date, language, and book publisher.

Derived from over 150 years of lexical research, these comprehensive textual and audio data, focused on British English, provide linguistically annotated data. Ideal for NLP applications, Machine Learning (ML), LLM training and/or fine-tuning, as well as educational and game apps.

Our British English language datasets are meticulously curated and annotated by experienced linguists and language experts, ensuring exceptional accuracy, consistency, and linguistic depth. The below datasets in British English are available for license:

1. British English Monolingual Dictionary Data
2. British English Synonyms and Antonyms Data
3. British English Pronunciations with Audio

Key Features (approximate numbers):

1. British English Monolingual Dictionary Data

Our British English monolingual dataset delivers clear, reliable definitions and authentic usage examples, featuring a high volume of headwords and in-depth coverage of the British English variant of English. As one of the world’s most authoritative lexical resources, it’s trusted by leading academic, AI, and language technology organizations.

• Headwords: 146,000
• Senses: 230,000
• Sentence examples: 149,000
• Format: XML and JSON format
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: twice a year

1. British English Synonyms and Antonyms Data

This British English language dataset offers a rich collection of synonyms and antonyms, accompanied by detailed definitions and part-of-speech (POS) annotations, making it a comprehensive resource for NLP tasks such as semantic search, word sense disambiguation, and language generation.

• Synonyms: 600,000
• Antonyms: 22,000
• Usage Examples: 39,000
• Format: XML and JSON format
• Delivery: Email (link-based file sharing)
• Updated frequency: annually

1. British English Pronunciations with audio (word-level)

This dataset provides IPA transcriptions and clean audio data in contemporary British English. It includes syllabified transcriptions, variant spellings, POS tags, and pronunciation group identifiers. The audio files are supplied separately and linked where available for seamless integration - perfect for teams building TTS systems, ASR models, and pronunciation engines.

• Transcriptions (IPA): 250,000
• Audio files: 180,000
• Format: XLSX (for transcriptions), MP3 and WAV (audio files)
• Updated frequency: annually

Use Cases:

We consistently work with our clients on new use cases as language technology continues to evolve. These include NLP applications, TTS, dictionary display tools, games, translation machine, AI training and fine-tuning, word embedding, and word sense disambiguation (WSD).  

If you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Growth.OL@oup.com to start the conversation.

Pricing:  

Oxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs. 

Contact our team or email us at Growth.OL@oup.com to explore pricing options and discover how our language data can support your goals. 

Please note that some datasets may have rights restrictions. Contact us for more information. 

About the sample: 

To help you explore the structure and features of our dataset on this platform, we provide a sample in CSV and/or JSON formats for one of the presented datasets, for preview purposes only, as shown on this page. This sample offers a quick and accessible overview of the data's contents and organization.  

Our full datasets are available in various formats, depending on the language and type of data you require. These may include XML, JSON, TXT, XLSX, CSV, WAV, MP3, and other file types. Please contact us (Growth.OL@oup.com) if you would like to receive the original sample with full details. 

Comprehensive German language datasets with linguistic annotations, including headwords, definitions, word senses, usage examples, part-of-speech (POS) tags, semantic metadata, and contextual usage details.

Our German language datasets are carefully compiled and annotated by language and linguistic experts. The below datasets in German are available for license:

1. German Monolingual Dictionary Data
2. German Bilingual Dictionary Data
3. German Word List Data

Key Features (approximate numbers):

1. German Monolingual Dictionary Data

Our German monolingual features clear definitions, headwords, examples, and comprehensive coverage of the German language spoken today.

• Words: 85,500
• Senses: 78,000
• Example sentences: 55,000
• Format: XML format
• Delivery: Email (link-based file sharing)

1. German Bilingual Dictionary Data

The bilingual data provides translations in both directions, from English to German and from German to English. It is annually reviewed and updated by our in-house team of language experts. Offers comprehensive coverage of the language, providing a substantial volume of translated words of excellent quality.

• Translations: 393,000
• Senses: 207,500
• Example translations: 129,500
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. German Word List Data

This language data contains a carefully curated and comprehensive list of 338,000 German words.

• Wordforms: 338,000
• Format: CSV and TXT formats
• Delivery: Email (link-based file sharing)

Use Cases:

We consistently work with our clients on new use cases as language technology continues to evolve. These include Natural Language Processing (NLP) applications, TTS, dictionary display tools, games, translations, word embedding, and word sense disambiguation (WSD).

If you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Growth.OL@oup.com to start the conversation.

Pricing:

Oxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs.

Contact our team or email us at Growth.OL@oup.com to explore pricing options and discover how our language data can support your goals.

About the sample:

The samples offer a brief overview of one or two language datasets (monolingual or/and bilingual dictionary data). To help you explore the structure and features of our dataset, we provide a sample in CSV format for preview purposes only.

If you need the complete original sample or more details about any dataset, please contact us (Growth.OL@oup.com) to request access or further information.

A medical lexicon of 14013 pairs of technical word forms and the corresponding simplified synonym or definition. It is aimed at automatic text simplification in Spanish. A subset of the lexicon (4642 term entries) was also normalized to Unified Medical Language System (UMLS) concept unique identifiers (CUIs). Note that the number of inflected forms was reduced after revision by experts, with regard to the version used in the published article (Campillos-Llanos et al. 2024). This dataset was collected in the CLARA-MeD project, with the goal of simplifying medical texts in the Spanish language and reduce the language barrier to patient's informed decision making. In particular, the project aims at developing linguistic resources for automatic medical term simplification in Spanish; and conducting experiments in automatic text simplification.

The canton within the meaning of INSEE is an administrative subdivision of a department. The canton is a territorial subdivision of the borough. It is the electoral district in which a general councillor is elected. The cantons, like the departments, were created by the law of 22 December 1789. In most cases, the cantons comprise several municipalities. But the cantons do not always respect the municipal boundaries: the most populated municipalities belong to several cantons. A township belongs to one and only one arrondissement.

Layer different from that of pseudo-cantons of the BD Carto layer #58 N_CANTON_BDC_ddd) because the latter contains the canton of attachment of the municipalities (finer definition to be provided)

Dataset contains basic descriptive data on territorial elements and units of territorial registration, in which at least one attribute in selected day have changed. Dataset contains no spatial location (polygons, definition lines and centroids of RÚIAN elements). The file contains following elements (in case they have changed): state, cohesion region, higher territorial self-governing entity (VÚSC), municipality with extended competence (ORP), authorized municipal office (POU), regions (old ones – defined in 1960), county, municipality, municipality part, town district (MOMC), Prague city district (MOP), town district of Prague (SOP), cadastral units and basic urban units (ZSJ), streets, building objects and address points. Dataset is provided as Open Data (licence CC-BY 4.0). Data is based on RÚIAN (Register of Territorial Identification, Addresses and Real Estates). Data is created every day (in case any change occurred) in RÚIAN exchange format (VFR), which is based on XML language and fulfils the GML 3.2.1 standard (according to ISO 19136:2007). Dataset is compressed (ZIP) for downloading. More in the Act No. 111/2009 Coll., on the Basic Registers, in Decree No. 359/2011 Coll., on the Basic Register of Territorial Identification, Addresses and Real Estates.

This table contains data on the percent of population age 25 and up with a four-year college degree or higher for California, its regions, counties, county subdivisions, cities, towns, and census tracts. Greater educational attainment has been associated with health-promoting behaviors including consumption of fruits and vegetables and other aspects of healthy eating, engaging in regular physical activity, and refraining from excessive consumption of alcohol and from smoking. Completion of formal education (e.g., high school) is a key pathway to employment and access to healthier and higher paying jobs that can provide food, housing, transportation, health insurance, and other basic necessities for a healthy life. Education is linked with social and psychological factors, including sense of control, social standing and social support. These factors can improve health through reducing stress, influencing health-related behaviors and providing practical and emotional support. More information on the data table and a data dictionary can be found in the Data and Resources section. The educational attainment table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf The format of the educational attainment table is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

This linguistically rich French dataset offers comprehensive annotations including headwords, definitions, senses, real-world examples, POS tags, semantic metadata, and usage info. Optimized for use in NLP, dictionary tools, and language model fine-tuning.

Our French language datasets are meticulously curated and annotated by experienced linguistics and language experts, ensuring exceptional accuracy, consistency, and linguistic depth. The below datasets in French are available for license:

1. French Monolingual Dictionary Data
2. French Bilingual Dictionary Data

Key Features (approximate numbers):

1. French Monolingual Dictionary Data

Our French monolingual dataset delivers clear, reliable definitions and authentic usage examples, featuring a high volume of headwords and in-depth coverage.

• Words: 42,000
• Senses: 56,000
• Example sentences: 43,000
• Format: XML format
• Delivery: Email (link-based file sharing)
• Updated frequency: annually

1. French Bilingual Dictionary Data

The bilingual data provides translations in both directions, from English to French and from French to English. It is annually reviewed and updated by our in-house team of language experts. Offers comprehensive coverage of the language, providing a substantial volume of translated words of excellent quality that span European, African and Canadian French varieties.

• Translations: 380,000
• Senses: 199,000
• Example translations: 146,000
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

Use Cases:

We consistently work with our clients on new use cases as language technology continues to evolve. These include Natural Language Processing (NLP) applications, TTS, dictionary display tools, games, translations, word embedding, and word sense disambiguation (WSD).

If you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Growth.OL@oup.com to start the conversation.

Pricing:

Oxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs.

Contact our team or email us at Growth.OL@oup.com to explore pricing options and discover how our language data can support your goals.

About the sample:

The samples offer a brief overview of one or two language datasets (monolingual or/and bilingual dictionary data). To help you explore the structure and features of our dataset, we provide a sample in CSV format for preview purposes only.

If you need the complete original sample or more details about any dataset, please contact us (Growth.OL@oup.com) to request access or further information.

Bangladesh number dataset provides contact information from trusted sources. We only collect phone numbers from reliable sources and define this information. To ensure transparency, we also provide the source URL to show where the information was collected from. In addition, we offer 24/7 support. If you have a question or need help, we’re always here. However, we care about accuracy, so we carefully collect the Bangladesh number dataset from trusted sources. You may rely on this data for business or personal use. With customer support, you’ll never have to wait when you need help or more information. We use opt-in data to respect privacy. This way, we contact only people who want to hear from you. Bangladesh phone data gives you access to contacts in Bangladesh. Here you can filter information by gender, age, and relationship status. This makes it easy to find exactly the people you want to connect with. We define this data by ensuring it follows all GDPR rules to keep it safe and legal. Our system works hard to remove any invalid data so you get only accurate and valid numbers. List to Data is a helpful website for finding important phone numbers quickly. Also, our Bangladesh phone data is suitable for doing business targeting specific groups. You can easily filter your list to focus on specific types of customers. Since we remove invalid data regularly, you don’t have to deal with old or useless numbers. We assure you that all data follows strict GDPR rules, so you can use it without any problems. Bangladesh phone number list is a collection of phone numbers from people in Bangladesh. We define this list by providing 100% correct and valid phone numbers that are ready to use. Also, we offer a replacement guarantee if you ever receive an invalid number. This means you will always have accurate data. We collect phone numbers that we provide based on customer’s permission. Moreover, we work hard to provide the best Bangladesh phone number list for businesses and personal use. We gather data correctly, so you won’t have to worry about getting outdated or incorrect information. Our replacement guarantee means you’ll always have valid numbers, so you can relax and feel confident.

The canton is a territorial subdivision of the borough. It is the electoral district within which a general councillor is elected.The cantons were created, like the departments, by the Act of 22 December 1789. In most cases, the cantons comprise several municipalities. But the cantons do not always respect the municipal boundaries: the most populated municipalities belong to several cantons. A township belongs to one and a single borough.A layer different from that of the pseudo-cantons of the BD Carto layer #58 N_CANTON_BDC_ddd) because the latter contains the canton of attachment of the municipalities (finer definition to be provided)