Find research datasets worth reusing

opencc-by-4.0Aug 2020View details →

Manually Annotated Instances of Ich ('I') from the German KoLas Corpus

Dataset used in Andresen/Knorr (2020). The dataset comprises 360 instances of ich ('I') taken from the German learner corpus KoLaS (Andresen/Knorr 2017, see <a href="http://hdl.handle.net/11022/0000-0001-B732-8">http://hdl.handle.net/11022/0000-0001-B732-8</a> for full corpus access) and manually annotated with categories taken from Steinhoff (2007). Column descriptions: <ul> <li>document: name of the document by which it can be found in the KoLaS corpus</li> <li>code_annotator1 - code_annotator4: Annotations by four annotators. Possible values: Verfasser-Ich (author I), Forscher-Ich (researcher I), Erzähler-Ich (narrator I)</li> <li>max_agreement_freq: Highest number of anntators that agreed on one label</li> <li>max_agreement_label: Label on which the highest number of annotators agreed</li> <li>context_before: 150 characters of context before the match</li> <li>match: the match itself (either ich or Ich)</li> <li>context_after: 150 characters of context after the match</li> </ul> References Andresen M, Knorr D. KoLaS – Ein Lernendenkorpus in der Schreibberatungsausbildung einsetzen. Zeitschrift Schreiben. Published online July 5, 2017:10-17. Andresen M, Knorr D. Exploring the Use of the Pronoun I in German Academic Texts with Machine Learning. In: Burghardt M, Müller-Birn C, eds. Methoden und Anwendungen der Computational Humanities. Lecture Notes in Informatics (LNI). Gesellschaft für Informatik; 2020. Steinhoff T. Zum ich-Gebrauch in Wissenschaftstexten. Zeitschrift für germanistische Linguistik. 2007;35(1-2):1–26.

opencc-by-4.0May 2024View details →

Manually Annotated Drone Imagery (RGB) Dataset for automatic coastline delineation of Southern Baltic Sea, Poland with polyline annotations (0.1.1)

Overview: The  Manually Annotated Drone Imagery Dataset (MADRID) consists of hand annotated high resolution RGB images taken in two different types of coasts in Poland, Miedzyzdroje - cliff coast and in Mrzezyno - dune coast in 2022-2023. All images were converted into a uniform format of 1440x2560 pixels, polyline annotated and set into file structure format suited for semantic segmentation tasks (See "Usage" notes below for more details). The raw images of our dataset were captured Zenmuse L1 Sensor (RGB) mounted on a DJI Matrice 300 RTK Drone. Total of 4895 images were captured, however the dataset contains 3876 images with each image annotated with coastline. The dataset only include images with coastlines that are visually identifiable with the human eye. For the annotations of the images, CVAT v2.13 open-source software was utilized. Usage: The compressed RAR file contains two folders train and test. Each folder contains the file that represents the date at which the image was captured in the format of (year, month, day), number of the image and the name of the drone utilized to capture the image. For example, DJI_20220111140051_0051_Zenmuse-L1-mission and DJI_20220111140105_0053_Zenmuse-L1-mission. Additionally, the test folder contains annotations (one per image) which are extracted from the original XML annotation file provided in the CVAT 1.1 image format. Archives were compressed using RAR compression. They can be decompressed in a terminal by opening and extracting Madrid_v0.1_Data.zip. The subset of the data with the name Madrid_subset_data.zip has been added which contains a small portion of train and test images for purpose of inspecting the dataset without downloading the entire dataset. The training images for both training data and testing data are structured as follows. <pre><code>Train/ └── images/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.JPG └── DJI_20220111140105_0053_Zenmuse-L1-mission.JPG └── ... └── masks/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.PNG └── DJI_20220111140105_0053_Zenmuse-L1-mission.PNG └── ... Test/ └── images/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.JPG └── DJI_20220111140105_0053_Zenmuse-L1-mission.JPG └── ... └── masks/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.PNG └── DJI_20220111140105_0053_Zenmuse-L1-mission.PNG └── ...</code></pre>

opencc-by-4.0May 2022View details →

Towards a systematic approach to manual annotation of code smells - C# Dataset of Long Method and Large Class code smells

This dataset includes open-source projects written in C# programing language, annotated for the presence of Long Method and God Class code smells. Each instance was manually annotated by at least two annotators. We explain our motivation and methodology for creating this dataset in our <a href="https://www.techrxiv.org/articles/preprint/Towards_a_systematic_approach_to_manual_annotation_of_code_smells/14159183/1">preprint</a>: Luburić, N., Prokić, S., Grujić, K.G., Slivka, J., Kovačević, A., Sladić, G. and Vidaković, D., 2021. Towards a systematic approach to manual annotation of code smells.  The dataset contains two excel datasheets: <ul> <li>DataSet_Large Class.xlsx – C# classes annotated for the Large Class code smell severity.</li> <li>DataSet_Long Method.xlsx – C# methods annotated for the Long method code smell severity.</li> </ul>  The columns in the datasheet represent: <ul> <li>Code Snippet ID – the full name of the code snippet.  <ul> <li>For classes, this is the package/namespace name followed by the class name. The full name of inner classes also contains the names of any outer classes (e.g., namespace.subnamespace.outerclass.innerclass).</li> <li>For methods, this is the full name of the class and the methods’s signature (e.g., namespace.class.method(param1Type, param2Type) ).</li> </ul> </li> <li>Link – The GitHub link to the code snippet, including the commit and the start and end LOC.</li> <li>Code Smell – code smell for which the code snippet is examined (Large Class or Long Method).</li> <li>Project Link – the link to the version of the code repository that was annotated.</li> <li>Metrics – a list of metrics for the code snippet, calculated by our <a href="https://github.com/Clean-CaDET/platform#readme">platform</a>. Our dataset provides 25 class-level metrics for Large Class detection and 18 method-level metrics for Long Method detection The list of metrics and their definitions is available <a href="https://github.com/Clean-CaDET/platform/blob/c4acff95ec00ff6c25fa62dde4818c1f40e39d39/CodeModel/CaDETModel/CodeItems/CaDETMetrics.cs">here</a>.</li> <li>Final annotation – a single severity score calculated by a majority vote. </li> <li>Annotators – each annotator's (1, 2, or 3) assigned severity score.</li> </ul> To help guide their reasoning for evaluating the presence and the severity of a code smell, three annotators independently annotated whether the considered heuristics apply to an evaluated code snippet. We provide these results in two separate excel datasheets: <ul> <li>LargeClass_Heuristics.xlsx - C# classes annotated for the presence of heuristics relevant for the Large Class code smell.</li> <li>LongMethod_Heuristics.xlsx - C# classes annotated for the presence of heuristics relevant for the Large Class code smell.</li> </ul> The columns of these two datasheets are: <ul> <li>Code Snippet ID - the full name of the code snippet (matching the IDs from DataSet_Large Class.xlsx and DataSet_Long Method.xlsx)</li> <li>Annotators – heuristics labelled by each of the annotators (1, 2, or 3).</li> <li>Heuristics – whether the heuristic is applicable to the examined code snippet or not (Section 1.2.4 lists heuristics relevant for the Large Class detection, and Section 1.2.5 lists the heuristics relevant for the Long Method detection).</li> </ul>

opencc-by-4.0Jun 2022View details →

An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines

Here we present a benchmarking dataset of fluorescence microscopy images with Hoechst 33342-stained nuclei together with annotations of nuclei, nuclear fragments and micronuclei. Images were randomly selected from an RNA interference screen with a modified U2OS osteosarcoma cell line, acquired on a Thermo Fischer CX7 high-content imaging system at 20x magnification. Labelling was performed by a single annotator and reviewed by a biomedical expert. The dataset contains 50 images showing over 2000 labelled nuclear objects in total, which is sufficiently large to train well-performing neural networks for instance or semantic segmentation. It is pre-split into training, development and test set, each in a zip file. The dataset should be referred to as Aitslab_bioimaging1. A brief article describing the dataset is also available (Arvidsson M, Kazemi Rashed S, Aits S. <a href="https://doi.org/10.1016/j.dib.2022.108769">10.1016/j.dib.2022.108769</a> ) Dataset description: Fluorescence microscopy images: original .C01 files and files converted to 8-bit .png format (Grayscale) Annotations: 24-bit .png format (RGB) Script used to convert C01 to png images: C01_to_png.py file with python code and readme.md file with instructions to run it

opencc-by-4.0Jan 2023View details →

TweetC19SR-Eng - Manually annotated dataset of English language COVID-19 tweets containing self-reports of symptoms

In this work, we release two expert curated, manually annotated datasets of COVID-19 self-reported symptoms. The first dataset contains tweets in English and the second contains tweets in Spanish, both containing around 36,500 tweets in total. These datasets were used for the Sixth and Seventh Workshop on Social Media Mining For Health (2021 and 2022)

opencc-by-4.0Jan 2023View details →

TweetC19SR-Spa - Manually annotated dataset of Spanish language COVID-19 tweets containing self-reports of symptoms

opencc-by-4.0Jan 2024View details →

RafanoSet: Dataset of raw, manual and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation in Heterogenous Agriculture Environment

This dataset is a collection of raw and annotated Multispectral (MS) images acquired in a heterogenous agricultural environment with MicaSense RedEdge-M camera. The spectra particularly Green, Blue, Red, Red Edge and Near Infrared (NIR) were acquired at sub-metre level..  The MS images were labelled manually using VIA and automatically using Grounding DINO in combination with Segment Anything Model. The segmentation masks obtained using these two annotation techniqes over as well as the source code to perform necessary image processing operations are provided in the repository. The images are focussed over Horseradish (Raphanus Raphanistrum) infestations in Triticum Aestivum (wheat) crops. The nomenclature of sequecncing and naming images and annotations has been in this format: IMG_<scene number>_<spectral channel number> _1: Blue _2: Green _3: Red _4: Near Infrared _5: RedEdge Example: An image name  IMG_0200_3 represents the scene number 200 in Red channel This dataset 'RafanoSet'is categorized in 6 directories namely 'Raw Images', 'Manual Annotations', 'Automated Annotations', 'Binary Masks - Manual', 'Binary Masks - Automated' and 'Codes'. The sub-directory 'Raw Images' consists of manually acquired 85 images in .PNG format. over 17 different scenes. The sub-directory 'Manual Annotations' consists of annotation file 'region_data' in COCO segmentation format. The sub-directory 'Automated Annotations' consists of 80 automatically annotated images in .JPG format and 80 .XML files in Pascal VOC annotation format. The scientific framework of image acquisition and annotations are explained in the Data in Brief paper which is the course of peer review. This is just a prerequisite to the data article.  Field experimentation roles: The image acquisition was performed by Mariano Crimaldi, a researcher, on behalf of Department of Agriculture and the hosting institution University of Naples Federico II, Italy. Shubham Rana has been the curator and analyst for the data under the supervision of his PhD supervisor Prof. Salvatore Gerbino. They are affiliated with Department of Engineering, University of Campania 'Luigi Vanvitelli'.  Domenico Barretta, Department of Engineering has been associated in consulting and brainstorming role particularly with data validation, annotation management and litmus testing of the datasets.

opencc-by-4.0Jul 2022View details →

Training data for 'Refining Manual Genome Annotations with Apollo (eukaryotes)' tutorial (Galaxy Training Material)

The data provided here are part of a Galaxy Training Network tutorial for manual curation of eukaryotic genome annotation using Apollo.

opencc-by-4.0Aug 2018View details →

Scalasca analysis report of the ASCI Sweep3D benchmark on 294,912 processes in virtual-node mode on IBM Blue Gene/P with manually annotated iterations

A Cube3 performance analysis report written by the Scalasca parallel analyzer of a measurement of the ASCI benchmark Sweep3D, executed in virtual-node mode on 294,912 processes of the IBM Blue Gene/P system JUQUEEN, operated by Forschungszentrum Jülich GmbH. The measurement includes system topology information and manual annotations of the twelve iterations.

opengpl-2.0-or-laterAug 2024View details →

Recordings of zebra finch group behaviors with manually annotated vocal segments

<div> <div>This dataset contains recording files and manually annotated vocal segments of freely behaving zebra finches in the BirdPark. The files contain animal-borne accelerometer, microphone and video channels. Birds are housed in groups of different sizes (2, 4, or 8 birds, (one 7-mins file per group). The dataset contains 112 mins of recordings from 4 different experiments.</div> </div> The dataset is published as an appendix of the following paper: <a href="https://doi.org/10.1101/2022.09.23.509166">Rüttimann, L., Wang, Y., Rychen, J., Tomka, T., Hörster, H., Rocha, M. D., & Hahnloser, R. H. (2024). Multimodal system for recording individual-level behaviors in songbird groups. bioRxiv.</a> See the README.pdf file for a detailed description.

opencc-by-4.0May 2023View details →

childTale-A: A corpus of eighty fairy tales from the 7th edition by the Brothers Grimm, manually annotated for textually encoded emotions

The childTale-A corpus is a collection of eighty fairy tales, a core set of the Grimms’ Children's and Household Tales as introduced in Herrmann & Lüdtke (2023). Within the CHYLSA project, textually encoded emotions were annotated in each sentence in each of the eighty fairy tales. Annotations were collected for the dimensions valence and arousal, as well as for the six basic emotions anger, disgust, fear, joy, sadness, and surprise. Each fairy tale was annotated by two persons (for details see Hermann & Lüdtke, 2023). In detail, this dataset contains: <ul> <li>instructions and texts used for annotation (zip-files): <ul> <li>all N=80 fairy tales as txt-files (with normalised orthography)</li> <li>instructions (in German) for the valence and arousal annotation as well as for the annotation of the six basic emotions</li> </ul> </li> <li>scripts for preparing and analysing annotations for textually encoded emotions: <ul> <li>Python scripts to prepare Excel files for annotation (including a script to separate texts into individual sentences)</li> <li>R-scripts for data preparation, calculation of inter-rater reliability and smoothing of the valence annotations (discrete cosine transformation (DCT) with length normalisation) <ul> </ul> </li> </ul> </li> <li>sentence-level data (for each sentence in each of the eighty fairy tales): <ul> <li>annotations for the dimensions valence and arousal (continuous values)</li> <li>categorisation of each sentence (as negative, neutral or positive) based on the continuous valence annotation</li> <li>annotations on the occurrence of the six basic emotions anger, disgust, fear, joy, sadness, and surprise</li> </ul> </li> <li>transformed and length-normalised valence annotations as basis for the Emotional Arcs (DCT and length normalisation results in one hundred data points for each fairy tale, saved in a separate data file)</li> <li>text-level data (for each of the eighty fairy tales): <ul> <li>general information, for example title in German and English, number of sentences, Kinder- und Hausmärchen-ID (KMH-ID), corpus ID</li> <li>results of the analysis of the annotated data with values for: <ul> <li>Average Valence and Average Arousal</li> <li>inter-rater reliability index (Krippendorff's alpha coefficient) for the valence and arousal annotations</li> <li>proportions of positive, negative and neutral sentences</li> <li>Emotion Potential (percent of both positive and negative sentences)</li> <li>Valence Span, Arousal Span and range of the Emotional Arc</li> <li>Emotion Profile (relative frequency for each of the six basic emotions anger, disgust, fear, joy, sadness, and surprise)</li> <li>inter-rater reliability indices (Krippendorff's alpha coefficient and the percentage of agreement) for each of the six basic emotions</li> </ul> </li> </ul> </li> </ul> Reference:  Herrmann, Berenike & Lüdtke, Jana (2023). A Fairy Tale Gold Standard. Annotation and Analysis of Emotions in the Children's and Household Tales by the Brothers Grimm. Zeitschrift für digitale Geisteswissenschaften (ZfdG). DOI: 10.17175/2023_005.   DFG Schwerpunktprogramm SPP 2207 “Computational Literary Studies“ Online: <ol> <li><a href="https://gepris.dfg.de/gepris/projekt/402743989">https://gepris.dfg.de/gepris/projekt/402743989</a></li> <li><a href="https://dfg-spp-cls.github.io/">https://dfg-spp-cls.github.io/</a></li> </ol> Teilprojekt: &bdquo;CHYLSA - Children’s and Youth Literature Sentiment Analysis“ Online: <ol> <li><a href="https://gepris.dfg.de/gepris/projekt/424250469">https://gepris.dfg.de/gepris/projekt/424250469</a></li> <li><a href="https://dfg-spp-cls.github.io/projects_en/2020/01/24/TP-CHYLSA/">https://dfg-spp-cls.github.io/projects_en/2020/01/24/TP-CHYLSA/</a></li> </ol>

edi40/100

Manual annotation of individual ovenbirds in acoustic recordings

This dataset includes acoustic recordings and annotations of those recordings for individually identified Ovenbird songs (Seiurus aurocapilla) created from annotating randomly selected passive acoustic monitoring recordings, largely collected in Alberta, CA. Every Ovenbird song within each recording is tagged in time and frequency. Each Ovenbird tag is labeled with individual identity a number corresponding to each individual’s order of appearance in the recording. If no Ovenbirds were not detected in a given recording, the recording was labeled at approximately 2 seconds into the recording with a three-second long tag containing species label “NONE.” Recordings contain other species’ songs, but no other species are tagged. This dataset was annotated via the WildTrax interface (https://wildtrax.ca/) and can also be found at https://portal.wildtrax.ca/home/aru-tasks.html?sensorId=ARU&projectId=623. These recordings were intended to be used for studies of individual ID, but can be used for a variety of other applications, including count data, song rate and vocal activity, and migratory phenology. Because each individual Ovenbird is numbered, these data can be also be transformed into count data or used to estimate individual Ovenbirds’ song rates. Data were randomly selected with respect to recording time, recording date, and recording location. Because recording time was randomly selected, the dataset can be used to estimate Ovenbird vocal activity over the course of a day. Because recordings were selected from a date range that includes dates before Ovenbirds are likely to be present in Alberta, the dataset can be used to estimate phenology of Ovenbird migration. This dataset is intended as an example open access labeled dataset to encourage development in the field of acoustic individual identification (Knight et al. In Revision for Trends in Ecology and Evolution).

openCC (other)May 2024View details →

dryad36/100

Eastern Canada Flocks: Images and manually annotated bird positions

The Eastern Canada (ECA) Flocks data set consists of manually annotated Images from the Common Eider (COEI, Somateria mollissima) Winter Survey and the Greater Snow Geese (GSGO, Anser caerulescens atlanticus) Spring Survey. The images were taken in Eastern Canada using fixed-wing aircraft and manually annotated with ImageJ's Cell counter plugins. We selected and annotated the ECA Flocks images in order to test the precision of the CountEm flock size estimation method. ECA Flocks includes 179 COEI and 99 GSGO single flock images. We cut each image manually to a rectangle that excluded large parts of the image with no birds. Both versions (original and cut) of each image are available in the data set. We manually annotated 637,555 (124,309 COEI and 514,235 GSGO) bird positions in the cut images from both surveys. Each bird has an associated "Type" which refers to species and/or sex. Sex identification was only possible for adult common eiders since females and immature males are brown birds whereas adult males have mainly white plumage. 64,484 male and 58,029 females were identified in the COEI images, as well as 1796 birds of other species. 504,891 Snow Geese and 9344 birds of other species were labeled in the GSGO images. A .csv file including all annotated bird positions and types is available for each image. The COEI and GSGO photos of the ECA Flocks data set were taken in the years 2006 and 2018 and 2016-2018 respectively. We selected these photos in order to include images with different quality and resolution. COEI and GSGO flock sizes range from 6 to 4,154 and from 43 to 36, 241 respectively. There is high variability in light conditions, backgrounds, number and spatial arrangement of birds across the images. The data set is therefore potentially useful to test the precision of methods for analyzing imagery to estimate the abundance of animals by directly detecting, identifying and counting individuals.

opencc-zeroMar 2022View details →

opencc-by-4.0Mar 2024View details →

All Tasks- STROKE - ALAMEDA Bracelet Data with manual Annotations

The tasks_all_total_with_manual_annotations.csv file contains accelerometer data and task labels of the pilot stroke patients that are given in folder datasets/bracelet/stroke. It includes 11 patients that are manually annotated and consists of 7 columns. Those columns are: <ol> <li> x, y, z, which represent the accelerometer values of the bracelets sensors used on either left or right wrist of the patients </li> <li> T and time, which represent the timestamp of the activity (time) and the period (T). </li> <li> Patient ID column, which is the number id of the patients. </li> <li> Task column, which represents the task performed by the patient. </li> </ol> This file contains the tasks that are described below. Inside of each parenthesis, is given the name of each task, based on the annotations that were provided on datasets/annotations/raw_medical_tracking/stroke/ intense-monitoring_with_manual_annotations_v2.xlsx file and on the accelerometer data that were available on stroke pilot folder mentioned above. Those tasks are: <ol> <ol> <li> cane_above_head (Cane above the head) </li> <li> standing_on_forefeet (Standing on the forefeet) </li> <li> lateral_steps (Lateral steps) </li> <li> rotation_cane (Rotations using a cane) </li> <li> cane_to_chest (Cane-to-chest) </li> <li> lateral_movement (Lateral movements with cane) </li> <li> hands_on_cane (Hands on the cane) </li> <li> lifting_knees (Lifting the knees) </li> <li> normal_walk (Normal walking) </li> <li> tandem_walk (Tandem walking) </li> <li> bicycle_walk (Bycicle walking) </li> <li> walk_with_knees_raised (Walking with the knees raised) </li> <li> rowing_movement (Rowing movements) </li> <li> flexion_extension_knees (Flexion and extension of the knees) </li> </ol> </ol>   The features used to recognize activities in stroke patients are x,y,z and Task.

opencc-by-4.0Dec 2021View details →

HT1080WT cells embedded in 3D collagen type I matrices - manual annotations for cell instance segmentation and tracking

Human fibrosarcoma HT1080WT (ATCC) cells at low cell densities embedded in 3D collagen type I matrices [1]. The time-lapse videos were recorded every 2 minutes for 16.7 hours and covered a field of view of 1002 pixels × 1004 pixels with a pixel size of 0.802 μm/pixel The videos were pre-processed to correct frame-to-frame drift artifacts, resulting in a final size of 983 pixels × 985 pixels pixels. Hasini Jayatilaka, Anjil Giri, Michelle Karl, Ivie Aifuwa, Nicholaus J Trenton, Jude M Phillip, Shyam Khatau, and Denis Wirtz. EB1 and cytoplasmic dynein mediate protrusion dynamics for efficient 3-dimensional cell migration. FASEB J., 32(3):1207–1221, 2018. ISSN 0892-6638. doi: 10.1096/fj.201700444RR. Further information about how to use this data is given in <a href="http://github.com/esgomezm/microscopy-dl-suite-tf">https://github.com/esgomezm/microscopy-dl-suite-tf</a> This dataset is provided together with the following preprint and if you use it, we would like to kindly ask you to cite it properly: <a href="https://arxiv.org/abs/2112.08817">Estibaliz Gómez-de-Mariscal, Hasini Jayatilaka, Özgün Çiçek, Thomas Brox, Denis Wirtz, Arrate Muñoz-Barrutia, *Search for temporal cell segmentation robustness in phase-contrast microscopy videos*, arXiv 2021 (arXiv:2112.08817)</a>

opencc-by-4.0May 2024View details →

Manually annotated RNA-focused scientific articles -- Experimental dataset

In this repository, we store 100 <a href="../records/11393776/files/annotations.json?download=1">paragraphs sourced from RNA-focused scientific articles that have been manually annotated</a> (both for entities and relations) according to a <a href="../records/11393776/files/RNA-KG.yaml?download=1">LinkML template</a> that represent associations among genes, proteins, RNAs, chemicals, variants (SNPs), GO terms, and diseases. The template reflects a <a href="../records/11393776/files/meta-graph.pdf?download=1">subportion of RNA-KG's meta-graph</a>. <a href="https://doi.org/10.48550/arXiv.2312.00183">RNA-KG</a> data are available at: <a href="../records/10078876">https://zenodo.org/records/10078876</a>.

opencc-by-4.0Jul 2018View details →

Manual Dependency Annotation of Three German Text Extracts from the Project hermA (Gold Standard Data)

This dataset was created in the digital humanities project hermA (www.herma.uni-hamburg.de) and comprises annotated extracts of the following three texts: <ul> <li>Modern literature (Lit2009): novel Corpus Delicti: Ein Prozess by German author Juli Zeh, published in Frankfurt/Main in 2009.</li> <li>Non-contemporary literature (Lit1850): Eine Frauenfahrt um die Welt ('A woman’s journey around the world') by Austrian author Ida Pfeiffer (1850). Full text available at Deutsches Textarchiv: http://www.deutschestextarchiv.de/pfeiffer_frauenfahrt01_1850/6.</li> <li>Modern academic writing (Aca2009): Stand, Möglichkeiten und Grenzen der Telemedizin in Deutschland ('Telemedicine in Germany: status, chances and limits') by Rüdiger Klar and Ernst Pelikan, published in Bundesgesundheitsblatt ('Federal Health Gazette') in 2009. DOI 10.1007/s00103-009-0787-7.</li> </ul> The texts are annotated for part-of-speech and dependency syntax and are made available in CoNLL file format. We describe the annotation process and report inter-annotator agreements in: Adelmann, Benedikt, Melanie Andresen, Wolfgang Menzel & Heike Zinsmeister. 2018. Evaluation of Out-Of Domain Dependency Parsing for its Application in a Digital Humanities Project. Proceedings of the 14th Conference on Natural Language Processing (KONVENS 2018). Vienna, Austria.

opencc-by-4.0Jun 2021View details →

Dump truck object detection with manual annotations

Doing manual annotations can sometimes be resource heavy, depending on the amount of data. This dataset was designed to created to use in conjunction with a semi-automatic annotation method based on linear interpolation. The dataset contains 799 images, where 679 lies in the trainingset, and the rest lies in the validationset. The images are taken from 6 different video streams, where a remote controlled wheel loader approaches a miniature dump truck at different angles. 4 of the videos are used in the trainingset. The labels can contain up to 5 classes which are: 0 - front wheel   1 - middle wheel 2 - back wheel 3 - tipping body 4 - cap This dataset was used to train a YOLOv3 model, hence the labels will be written in the YOLO labeling format.