Dataset converter

Author: m | 2025-04-24

Convert Dataset Formats Download datasets; The fiftyone convert command; Convert CIFAR-10 dataset; Convert KITTI dataset; Cleanup; 330 E Liberty St Ann Arbor, MI `df` is a spark dataframe create a converter from `df` it will materialize `df` to cache dir. converter = make_spark_converter (df) make a tensorflow dataset from `converter` with converter. make_tf_dataset as dataset: the `dataset` is `tf.data.Dataset` object dataset transformation can be done if needed dataset = dataset. map

Dataset converter Object Detection Dataset by XMLtoYOLOtxt

QA Dataset ConverterIn this repository, we release code from the paper What do Models Learn from Question Answering Datasets? by Priyanka Sen and Amir Saffari.These scripts convert four popular question answering datasets into a common format based on SQuAD 2.0 to allow for easier probing and experimentation. An example of a question in the SQuAD 2.0 format is shown below:{ "context": "The Normans were the people who in the 10th and 11th centuries..." "qas": [ { "question": "In what country is Normandy located?", "id": "56ddde6b9a695914005b9628", "answers": [ { "text": "France", "answer_start": 159 } ], "is_impossible": false }...In the following sections, we guide you through converting TriviaQA, Natural Question, QuAC, and NewsQA into a SQuAD 2.0 format.TriviaQAStep 1Clone this repo and go into the TriviaQA directory.cd qa-dataset-converter/triviaqaStep 2Download the TriviaQA dataset from This will include a qa directory with question-answer files and an evidence containing the documents for context.Step 3Clone the TriviaQA repo.git clone 4Move our triviaqa_to_squad.py script into the TriviaQA repo.mv triviaqa_to_squad.py triviaqa/Step 5Set --triviaqa_file to a file in your qa directory and --data_dir to the Wikipedia path in your evidence directory. Run:python triviaqa_to_squad.py --triviaqa_file qa/wikipedia-train.json --data_dir evidence/wikipedia/ --output_file triviaqa_train.jsonpython triviaqa_to_squad.py --triviaqa_file qa/wikipedia-dev.json --data_dir evidence/wikipedia/ --output_file triviaqa_dev.jsonThis will return two files triviaqa_train.json and triviaqa_dev.json in the SQuAD 2.0 format.Natural QuestionsStep 1Clone this repo and go into the Natural Questions directory.cd qa-dataset-converter/nqStep 2Download the Natural Questions dataset from This will download train and dev directories of jsonl.gz files.Step 3Set --nq_dir to your Natural Questions train or dev directory. Run:python nq_to_squad.py --nq_dir train/ --output_file nq_train.jsonpython nq_to_squad.py --nq_dir dev/ --output_file nq_dev.jsonThis will return two files nq_train.json and nq_dev.json in the SQuAD 2.0 format.QuACStep 1Clone this repo and go into the QuAC directorycd qa-dataset-converter/quacStep 2Download the QuAC dataset from 3Set --quac_file to the path of your QuAC train or dev file. Run:python quac_to_squad.py --quac_file train_v0.2.json --output_file quac_train.jsonpython quac_to_squad.py --quac_file val_v0.2.json --output_file quac_dev.jsonThis will return two files quac_train.json and quac_dev.json in the SQuAD 2.0 format.NewsQAStep 1Clone this repo and go into the NewsQA directorycd qa-dataset-converter/newsqaStep 2Follow the instructions at to build the NewsQA dataset. This will result in a directory called split_data with train, dev, and test CSVs.Step 3Note: If you used a Python 2.7 conda environment to set up NewsQA, make sure to deactivate your environment before this step.Set --newsqa_file to the path of a NewsQA file in the split_data directory. Run:python newsqa_to_squad.py --newsqa_file split_data/train.csv --output_file newsqa_train.jsonpython newsqa_to_squad.py --newsqa_file split_data/dev.csv --output_file newsqa_dev.jsonAcknowledgementsOur TriviaQA script modifies code released in TrivaiQA repo In particular, we take inspiration from convert_to_squad_format.py for all our scripts.We also use modified code from the Nautral Question browser script to process Natural Questions examples.We are thankful to the authors for making this code available.LicenseThis code is licensed under the Apache License, Version

amz dataset Object Detection Dataset by Converter

FOR IMMEDIATE RELEASEtetra4D Releases 3D PDF Converter Version 4.1Latest software release contains powerful enhancements for democratizing 3D dataBend, OR, USA – March 31, 2014 – tetra4D, a division of Tech Soft 3D, today announces the latest release of 3D PDF Converter, a powerful solution for creating 3D-based documents to support Model Based Definition (MBD). Customers can immediately take advantage of the most significant upgrade to the industry-leading software for democratizing 3D data yet delivered.This release is the result of many developer-years spent incorporating several hundred changes including new features, customer requests, and updated support for various 3D formats. New support has been added for Autodesk® Inventor® 2014, CATIA V5-6R2013 (R23), Rhino 4.5, Solid Edge ST6, SolidWorks 2014, and NX 9.0, among others. More details can be found in the Release Notes. Click here to download them.Customers on active maintenance and support receive this upgrade for free. Those customers without active maintenance contracts should contact their reseller partner to request upgrade pricing, or contact us at [email protected]“This release of 3D PDF Converter has significant advantages for anyone who needs to adhere to ASME Y14.41 and other PMI standards,” says Bryan R. Fischer, President of Advanced Dimensional Management LLC and MBD360 LLC. “It will now be possible for organizations of any size to leverage 3D PDF for dynamic 3D annotation with visual feedback, without the need for traditional 2D drawings. This significantly reduces costs and complexity, and increases the communication index of the dataset. The dataset provides information in a manner that is an order of magnitude easier to understand.”“We are very pleased to announce this critical milestone for delivering on the commitment we have made to our customers to continue to lead the market with cost-effective solutions for deployment of 3D PDF-enabled workflows,” said Dave Opsahl, tetra4D President and Vice President of Corporate Development for Tech Soft 3D, tetra4D’s parent company. “With the widespread adoption we are now seeing across industries throughout their supply chains, we expect this release will accelerate 3D PDF use and adoption significantly.”FREE TRIAL3D PDF Converter and 3D PDF Converter Suite, which includes Adobe® Acrobat® XI Pro,

Dataset converter Object Detection Dataset (v1,

Zhang, Kang; Goldbaum, Michael (2018), “Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images”, Mendeley Data, V3, doi: 10.17632/rscbjbr9sj.3SourceIf you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:@misc{ visualization-tools-for-zhang-lab-data-oct-dataset, title = { Visualization Tools for ZhangLabData: OCT Dataset }, type = { Computer Vision Tools }, author = { Dataset Ninja }, howpublished = { \url{ } }, url = { }, journal = { Dataset Ninja }, publisher = { Dataset Ninja }, year = { 2025 }, month = { mar }, note = { visited on 2025-03-22 },}Download #Dataset ZhangLabData: OCT can be downloaded in Supervisely format:As an alternative, it can be downloaded with dataset-tools package:pip install --upgrade dataset-tools… using following python code:import dataset_tools as dtoolsdtools.download(dataset='ZhangLabData: OCT', dst_dir='~/dataset-ninja/')Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.The data in original format can be downloaded here.. . .Disclaimer #Our gal from the legal dep told us we need to post this:Dataset Ninja provides visualizations and statistics for some datasets that can be found online and can be downloaded by general audience. Dataset Ninja is not a dataset hosting platform and can only be used for informational purposes. The platform does not claim any rights for the original content, including images, videos, annotations and descriptions. Joint publishing is prohibited.You take full responsibility when you use datasets presented at Dataset Ninja, as well as other information, including visualizations and statistics we provide. You are in charge of compliance with any dataset license and all other permissions. You are required to navigate datasets homepage and make sure that you can use it. In case of any questions, get in touch with us at [email protected].. Convert Dataset Formats Download datasets; The fiftyone convert command; Convert CIFAR-10 dataset; Convert KITTI dataset; Cleanup; 330 E Liberty St Ann Arbor, MI

API to convert JSON objects for DataSet and DataSet to JSON

Services section navigation .htpasswd and .htaccess generator 3D product box generator Augmented reality pattern marker generator Audio, video, image or data file ID3 file information Bank identification number checker Base64 encoder and decoder Battery charge time calculator BBAN to IBAN converter BIC / SWIFT code finder for SEPA countries Big number bitwise calculation Big number converter Big number equation calculation Blockchain and cryptocurrency tools Business card maker Calendar Character dataset test Check Dutch bank account number or citizen service number with Eleven test Chinese handwriting recognitionChinese HSK vocabulary test --> Compound interest calculator with graph Convert Dutch bank account numbers to IBAN numbers Convert domain name to IP address, find IP address of a domain name Convert IP adddress to different formats Convert ISO Latin 1, UTF-8, UTF-16, UTF-16LE or Base64 text to hex and vice versa Convert Unicode characters to HTML code numbers and vice versa Convert Unicode characters to Unicode escape sequences and vice versa Coordinate converter and show map Create self-signed SSL certificates online Cryptographic Pseudorandom Number Generator CSV to XML converter CVS pserver password decoder and encoder Decode Certificate Signing Request (CSR) Decode SSL certificate Electronic business card vCard generator European clothing standard EN 13402 pictogram generator Favicon generator File checksum calculator Find the BIC numbers for Dutch IBAN numbers Free game sound effects Free game textures Free online practice exams Free online SEPA XML valdation Generate Dutch bank account numbers and Dutch citizen service numbers Google toolbar custom button code generator Google maps (API v2) code generatorGoogle maps (API v3) code generator --> Google map distance calculator Hide email address HTML escape and unescape tool Hieroglyphs generator IBAN checker Icon generator International bra size calculator Javascript and HTML code executor JSON formatter and validator Javascript formatter Learning Mandarin Chinese Long division generator Lorem ipsum generator

MaskTheFace - Convert face dataset to masked dataset - GitHub

ContentsTYGEMTomFoxwqAyaProfessionalAIAlphaGoELF OpenGoFineArtPhoenixGoZenCGIDolBaramDancerLeelaCNCThe 1st World AI Go Open 2017Berry Genomics Cup 2018 World AI Weiqi Competition2018 Tencent World AI WEIQI CompetitionThe 2nd World AI Go Open 2018Berry Genomics Cup 2019 World AI Weiqi Competition2019 China Securities Cup World AI WEIQI OpenThe 11th Computer Go UEC Cup2020 World GO AI ChampionshipCGOSLeelaZeroKGSMinigoNNGSELF OpenGoKataGo1 TYGEM dataset2005.11.02 - 2016.12.31TYGEM "9D vs 9D" dataset (1,516,031 games).1.1 Format(index)idTYGEM iddateYYYY.MM.DD HH:MMwhitechar[16]white englishchar[11]white nation0 Korea1 Japan2 China3 USA4 Chinese Taipei5 Chinablackchar[16]black englishchar[11]black nation0 Korea1 Japan2 China3 USA4 Chinese Taipei5 ChinaresultB+ResignW+ResignB+X.5W+X.5B+TimeW+TimeB+OfflineW+Offlineround60 - 450byoyomi/times minutesdefault:komi6.5white rank9dblack rank9dtyperankedCAUTF-8GM1FF4Due to the 25 MB file size limit, I have to split the ".index" file. You can merge it by yourself.1.2 Format(kifu)id\t;B[coord];W[coord];B[coord];W[coord]......1.3 Convert to SGFtygem_convert.tar.gzThanks for Hiroshi Yamashita writing the converter tool.Converter.pyusage: python Converter.py kifu.index english_user_id kifu_folder save_folderexample: python Converter.py kifu.index Lurk(P) Kifu Save2 TOM dataset2003.09.25 - 2011.12.28TOM "9D vs 9D" dataset (50,956 games).2.1 Format(index)iddatewhiteblackkomiresultroundbyoyomi/times minutesdefault:white rank9dblack rank9dtyperankedCAUTF-8GM1FF42.2 Format(kifu)id\t;B[coord];W[coord];B[coord];W[coord]......2.3 Convert to SGFConverter_Tom.pyusage: python Converter_Tom.py Kifu.index user_id kifu_folder save_folderexample: python Converter_Tom.py Kifu.index 930115 kifu save3 Foxwq dataset3.1 Github18k-9d3.2 9d vs 9d2013.07.09 - 2019.10.17166,184 games4 Aya's selfplay games for training value network19x19, 13x13, 9x9Aya's selfplay games5 Professional1940.01.01 - 2017.01.0973,522 games5.1 Format(txt)SGF\nSGF\nSGF\n...5.2 SGF tagsGM1FF4SZ19APCAUTF-8GNPWWR (option)PBBR (option)KM (option)0 REB+W+B+XW+XB+RW+RB+TW+TB/W/B/W ...6 AI datasetAlphaGoAlphaGo Zero83 gamesAlphaGo KeAlphaGo vs Ke Jie3 games2017.05.23 - 2017.05.273W / 0L / 100%AlphaGo vs China Team1 games2017.05.261W / 0L / 100%AlphaGo + Lian Xiao vs AlphaGo + Gu Li1 games2017.05.26AlphaGo selfplay55 gamesAlphaGo MasterMaster60 games2016.12.29 - 2017.01.0460W / 0L / 100%AlphaGo LeeAlphaGo vs Lee Sedol5 games2016.03.09 - 2016.03.154W / 1L / 80%AlphaGo selfplay3 gamesAlphaGo FanAlphaGo vs Fan Hui5

nuPlan converter - Dataset Converter - CommonRoad

Issues, unlock the dataset(s) by entering the passphrase/key to allow datasets to mount and apps to start.You can create required datasets before or after launching the installation wizard.The install wizard includes the Create Dataset option for host path storage volumes, but if you are organizing required datasets under a parent you must create that dataset before launching the app installation wizard.Go to Datasets and select the pool or dataset where you want to place the dataset(s) for the app.For example, /tank/apps/appName.Nextcloud requires three datasets: html for app data, data for user data, and postgres_data for the database data storage volume.Earlier versions of the Nextcloud app relied on four datasets.If upgrading with an existing deployment of this application, the installation wizard offers an option to migrate these to the new configuration.Creating Datasets for AppsWhen creating datasets for apps follow these steps:Go to Datasets, select the location for the parent dataset if organizing required datasets under a parent dataset, then click Add Dataset.For example, select the root dataset of the pool, and click Add Dataset to create a new parent called apps or appName*, where appName is the name of the app.Do not create the app datasets under the ix-applications or ix-apps dataset.Enter the name of the dataset, then select Apps as the Dataset Preset.Creating the parent dataset with the preset set to Generic causes permissions issues when you try to create the datasets the app requires with the preset set to Apps.Click Save.Return to dataset creation when prompted rather than configuring ACL permissions.You can set up permissions (ACLs) for a dataset after adding it by selecting Go to ACL Manager to open the Edit ACL screen, or wait and use the app Install wizard ACL settings to add permissions.You can also edit permissions after installing the app using either method.Select the parent dataset and then click Create Dataset to open the Add Dataset screen again.Enter the name of a dataset required for the app, such as config, select Apps as the Dataset Preset, and then click Save.When prompted, return to creating datasets rather than setting up ACL permissions.Repeat for remaining datasets required for the app.You can modify dataset ACLs at the time of creation, or later in the app.Adding ACL permissions in the installation wizard is the recommended method.When storage volumes include a postgres dataset, do not select Enable ACL to configure permissions.Instead, proceed with entering or browsing to select the dataset and populate the Host Path field, then select the Automatic Permissions option.This configures permissions for the postgres dataset and, if configured, the parent dataset used to organize required datasets for the app.As with other host path storage volumes, you can create a dataset when entering the host path.You can use Enable ACL to manually configure ACL permissions for the postgres dataset and a parent dataset, but the process is involved and you receive errors after clicking Install on the application installation wizard. Additionally, the Automatic Permissions option does not show on the wizard screen.You can reverse setting the host

Writing a dataset converter - GitHub

In Social Media. IEEE Trans. Comput. Soc. Syst. 2024, 11, 1979–1990. [Google Scholar] [CrossRef]Tavchioski, I.; Robnik-Šikonja, M.; Pollak, S. Detection of depression on social networks using transformers and ensembles. arXiv 2023, arXiv:2305.05325. [Google Scholar] [CrossRef]Go, A.; Bhayani, R.; Huang, L. Twitter Sentiment Classification Using Distant Supervision. 2009. Available online: (accessed on 29 May 2024).Yoon, S.; Dang, V.; Mertz, J.; Rottenberg, J. Are attitudes towards emotions associated with depression? A Conceptual and meta-analytic review. J. Affect. Disord. 2018, 18, 329–340. [Google Scholar] [CrossRef] [PubMed]Yavuzer, Y.; Karatas, Z. Investigating the Relationship between Depression, Negative Automatic Thoughts, Life Satisfaction and Symptom Interpretation in Turkish Young Adults. In Depression; IntechOpen: London, UK, 2017. [Google Scholar] [CrossRef]Marshall, A.D.; Sippel, L.M.; Belleau, E.L. Negatively Biased Emotion Perception in Depression as a Contributing Factor to Psychological Aggression Perpetration: A Preliminary Study. In Emotions and Their Influence on Our Personal, Interpersonal and Social Experiences; Routledge: London, UK, 2011. [Google Scholar] [CrossRef]Komati, N. Suicide and Depression Detection Dataset, Kaggle. 2021. Available online: (accessed on 20 September 2024).Bayes Theorem—An Overview|ScienceDirect Topics. Available online: (accessed on 20 May 2024). Figure 1. Worldwide active users of select social media sites [6]. Figure 1. Worldwide active users of select social media sites [6]. Figure 2. Overview of our methodological approach. Figure 2. Overview of our methodological approach. Figure 3. RoBERTa loss curve of the Sentiment140 dataset. Figure 3. RoBERTa loss curve of the Sentiment140 dataset. Figure 4. RoBERTa accuracy curve of the Sentiment140 dataset. Figure 4. RoBERTa accuracy curve of the Sentiment140 dataset. Figure 5. DeBERTa loss curve. Figure 5. DeBERTa loss curve. Figure 6. DeBERTa accuracy curve. Figure 6. DeBERTa accuracy curve. Figure 7. DistilBERT loss curve of the Sentiment140 dataset. Figure 7. DistilBERT loss curve of the Sentiment140 dataset. Figure 8. DistilBERT accuracy curve of the Sentiment140 dataset. Figure 8. DistilBERT accuracy curve of the Sentiment140 dataset. Figure 9. SqueezeBERT loss curve of the Sentiment140 dataset. Figure 9. SqueezeBERT loss curve of the Sentiment140 dataset. Figure 10. SqueezeBERT accuracy curve of the Sentiment140 dataset. Figure 10. SqueezeBERT accuracy curve of the Sentiment140 dataset. Figure 11. RoBERTa loss curve of the Suicide-Watch dataset. Figure 11. RoBERTa loss curve of the Suicide-Watch dataset. Figure 12. RoBERTa accuracy curve of the Suicide-Watch dataset. Figure 12. RoBERTa accuracy curve of the Suicide-Watch dataset. Figure 13. DeBERTa loss curve of the Suicide-Watch dataset. Figure 13. DeBERTa loss curve of the Suicide-Watch dataset. Figure 14. DeBERTa accuracy curve of the Suicide-Watch dataset. Figure 14. DeBERTa accuracy curve of the Suicide-Watch dataset. Figure 15. DistilBERT loss curve of the Suicide-Watch dataset. Figure 15. DistilBERT loss curve of the Suicide-Watch dataset. Figure 16. DistilBERT accuracy curve of the Suicide-Watch dataset. Figure 16. DistilBERT accuracy curve of the Suicide-Watch dataset. Figure 17. SqueezeBERT loss curve of the Suicide-Watch dataset. Figure 17. SqueezeBERT loss curve of the Suicide-Watch dataset. Figure 18. SqueezeBERT accuracy curve of the Suicide-Watch dataset. Figure 18. SqueezeBERT accuracy curve of the Suicide-Watch dataset. Figure 19. Logistic regression Confusion Matrix for the Suicide-Watch dataset. Figure 19. Logistic regression. Convert Dataset Formats Download datasets; The fiftyone convert command; Convert CIFAR-10 dataset; Convert KITTI dataset; Cleanup; 330 E Liberty St Ann Arbor, MI

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Confusion Matrix for the Suicide-Watch dataset. Figure 20. Bernoulli Naive Bayes Confusion Matrix for the Suicide-Watch dataset. Figure 20. Bernoulli Naive Bayes Confusion Matrix for the Suicide-Watch dataset. Figure 21. Random Forest Confusion Matrix for Suicide-Watch dataset. Figure 21. Random Forest Confusion Matrix for Suicide-Watch dataset. Figure 22. SqueezeBert Confusion Matrix for Suicide-Watch dataset. Figure 22. SqueezeBert Confusion Matrix for Suicide-Watch dataset. Figure 23. RoBerta Confusion Matrix for Suicide-Watch dataset. Figure 23. RoBerta Confusion Matrix for Suicide-Watch dataset. Figure 24. DeBerta Confusion Matrix for Suicide-Watch dataset. Figure 24. DeBerta Confusion Matrix for Suicide-Watch dataset. Figure 25. DistilBert Confusion Matrix for Suicide-Watch dataset. Figure 25. DistilBert Confusion Matrix for Suicide-Watch dataset. Table 1. Sentiment140 dataset description table. Table 1. Sentiment140 dataset description table. CharacteristicValueTotal Tweets632,528Depressive Tweets316,264Non-Depressive Tweets316,264Collection PeriodApril–June 2009 Table 2. Suicide-Watch dataset description table. Table 2. Suicide-Watch dataset description table. CharacteristicValueTotal Reddits/Subreddits232,074Suicide Reddits/Subreddits116,037Non-Depressive Reddits/Subreddits116,037Collection PeriodDecember 2008–January 2021 Table 3. Overviewof the model’s performance on the Sentiment 140 Dataset. Table 3. Overviewof the model’s performance on the Sentiment 140 Dataset. ModelAccuracyPrecisionRecallF1-ScoreRandom Forest94.9%96.4%93.3%95.0%Bernoulli Naive Bayes90.1%90.1%90.0%90.1%Logistic Regression97.0%97.2%96.7%97.0%RoBERTa98.0%98.0%99.0%98.0%DeBERTa98.0%98.0%98.0%98.0%DistilBERT97.0%98.0%98.0%97.0%SquuezeBERT95.0%97.0%97.0%96.0% Table 4. Overviewof the model’s performance on the Suicide-Watch Dataset. Table 4. Overviewof the model’s performance on the Suicide-Watch Dataset. ModelAccuracyPrecisionRecallF1-ScoreRandom Forest90.8%90.8%90.8%90.8%Bernoulli Naive Bayes78.2%80.1%78.2%77.9%Logistic Regression93.5%93.5%93.5%93.5%RoBERTa87.0%87.2%87.0%87.0.0%DeBERTa88.0%88.5%88.0%87.9%DistilBERT88.0%88.0%88.0%88.0%SquuezeBERT87.5%87.8%87.5%87.4% Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (

Dataset Converter script for ADE20k dataset Issue 2025 - GitHub

The recommended option, set Type to Host Path (Path that already exists on the system).If the install wizard shows a Mount Path, either accept the default value or enter the correct mount path. For example, if the dataset name is data, enter /data as the mount path.To create a dataset while in the app installation wizard, with Type set to the host path option, go to the Host Path field, click into the pool or a dataset in the pool to activate the Create Dataset option. Click on Create Dataset to open the dialog.Enter the name for the dataset, then click Create. TrueNAS creates the dataset in the location selected.Select Enable ACL to define ACL permissions and to populate the Host Path field by either entering or browsing to and selecting the location of the dataset.Populating the Host Path with the dataset location and then selecting Enable ACL clears the values, so we recommend selecting Enable ACL before entering the host path.Repeat the above for each required dataset.Nextcloud needs three datasets for host path storage volume configurations:html to use as the AppData storage volume.data to use as the User Data storage volume.postgres_data to use as the Postgres Data storage volume.If you nest these datasets under a parent dataset named nextcloud, you can create this nextcloud dataset with the Dataset Preset set to Generic or Apps.You can configure the ACL for this dataset from the Permissions widget on the Datasets screen.If the app has postgres storage volumes, the process is easier and less prone to permissions errors if you use the Automatic Permissions option in the postgres storage volume section of the install Wizard.Earlier Nextcloud Deployment DatasetsEarlier deployments of the Nextcloud app use five datasets, the parent dataset for the application (nextcloud) and the four child datasets:appdata that contains HTML, apps, custom_themes, config, etc.userdata that contains the actual files uploaded by the userpgdata that contains the database files.pgbackup that contains the database backupsUpgrading to 24.10 migrates earlier Nextcloud Kubernetes app deployments to the current Docker Compose configuration.Setting Dataset ACL PermissionsYou can configure ACL permissions for the required dataset in the Install Nextcloud wizard, or from the Datasets screen any time after adding the datasets.Creating App DatasetsTo create the Nextcloud app datasets, go to Datasets, select the dataset you want to use as the parent dataset, then click Add Dataset to add a dataset.In this example, we create the Nextcloud datasets under the root parent dataset tank.Enter nextcloud in Name, and select Apps as the Dataset Preset.Click Advanced Options if you want to make any other setting changes. Click Save.When prompted, select Return to Pool List to configure permissions later after adding the other three datasets, or open the ACL editor to edit ACL permissions immediately after adding the dataset.Next, select the nextcloud dataset, and click Add Dataset to add the first child dataset.Enter html in Name and select Apps as the Dataset Preset.Click Advanced Options if you want to make any other setting changes. Click Save.Repeat this two more times to. Convert Dataset Formats Download datasets; The fiftyone convert command; Convert CIFAR-10 dataset; Convert KITTI dataset; Cleanup; 330 E Liberty St Ann Arbor, MI `df` is a spark dataframe create a converter from `df` it will materialize `df` to cache dir. converter = make_spark_converter (df) make a tensorflow dataset from `converter` with converter. make_tf_dataset as dataset: the `dataset` is `tf.data.Dataset` object dataset transformation can be done if needed dataset = dataset. map

Datasets - Wildlife DataSets, Animal Population DataSets and

To get an understanding of how your customers feel about a certain service or product. Get dataset Promote your brand Find the right people to promote your brand using a Facebook dataset. The dataset can be analyzed based on the number of followers and post engagement, brand affinity, topics, location, and other factors. Get dataset Competitor analysis Evaluate what type of posts and content work for your competitors by analyzing a Facebook dataset. Identify areas of opportunity, based on your competitors’ Facebook presence, look at their posts, images, user engagement, and more. Get dataset Facebook Dataset FAQsWhat data is included in the Facebook dataset? The Facebook dataset includes different data points that fit your needs. Some of the data points include: URL, post ID, user URL, username, content, date posted, hashtag, number of comments, number of shares, and a lot more.Can I get updates for my purchased Facebook dataset? Yes, you can get updates to your Facebook dataset on a daily, weekly, monthly, or custom basis.Can I purchase a subset of the Facebook dataset? Yes, you can purchase a Facebook subset that will include only the data points you need. By purchasing a subset, cost is reduced substantially.In what format will I receive the Facebook dataset? Dataset formats are JSON, NDJSON, JSON Lines, CSV, or Parquet. Optionally, files can be compressed to .gz.Can I get a data sample? Yes, you can request sample data to evaluate the quality and relevance of the information provided. This is a great way to