A Resource for Evaluating Creative Search on the Infinite Index

This dataset supplements our paper A Resource for Evaluating Creative Search on the Infinite Index (currently under review). It introduces Cranfield-style topics that are designed to evaluate iterative generative systems, which are systems that allow for iterative refinement of generated content through user feedback. One example for such a system would be "prompt engineering on Stable Diffusion". The reproduction instructions and application examples can be found in our code repository on Github: https://github.com/niklasdeckers/evaluating-creative-search-infinite-index File Structure To simply access the textual image descriptions (to form interactive topics), see the [reddit|pexels|lexica]/doccano_*_single_image.jsonl files. ├── reddit/ # Data based on Reddit r/captionthis │ ├── text_embeddings.pkl # CLIP embeddings of all texts │ ├── faiss_text_index.index # FAISS index to find nearest neighbors in the text embedding space │ ├── image_embeddings.pkl # CLIP embeddings of all images │ ├── faiss_image_index.index # FAISS index to find nearest neighbors in the image embedding space │ ├── doccano_reddit_single_image.jsonl # Contains entries for all image-text pairs in a Doccano-compatible format │ ├── doccano_reddit_closest_clip_match_by_comment.jsonl # Contains entries with two candidate images for each text │ ├── groundtruth_reddit_closest_clip_match_by_comment.json # Groundtruth data for the order chosen for the two candidate images above │ ├── doccano_reddit_closest_clip_match_by_image.jsonl # Contains entries with two candidate images for each text │ ├── groundtruth_reddit_closest_clip_match_by_image.json # Groundtruth data for the order chosen for the two candidate images above │ └── output/ # Artifacts from building the dataset │ ├── meta/ # Contains cleaned top-level threads (including the URLs of submitted images) │ ├── comments/ # Contains cleaned comments under the corresponding threads │ ├── images/ # Used for storing the images after crawling │ ├── failed_images.jsonl # Image metadata in case of crawling issues │ ├── removed_by_blocklist/ # Posts moved from the meta and comments dir when excluded by the blocklist │ ├── duplicates/ # Posts moved from the meta and comments dir in case of duplicates │ └── removed_404/ # A single remaining instance formed by a Imgur 404 error image ├── pexels/ # Data based on Pexels (stock images) │ ├── text_embeddings.pkl # CLIP embeddings of all texts │ ├── faiss_text_index.index # FAISS index to find nearest neighbors in the text embedding space │ ├── image_embeddings.pkl # CLIP embeddings of all images │ ├── faiss_image_index.index # FAISS index to find nearest neighbors in the image embedding space │ ├── doccano_pexels_single_image.jsonl # Contains entries for all image-text pairs in a Doccano-compatible format │ ├── doccano_pexels_closest_clip_match_by_comment.jsonl # Contains entries with two candidate images for each text │ ├── groundtruth_pexels_closest_clip_match_by_comment.json # Groundtruth data for the order chosen for the two candidate images above │ ├── doccano_pexels_closest_clip_match_by_image.jsonl # Contains entries with two candidate images for each text │ └── groundtruth_pexels_closest_clip_match_by_image.json # Groundtruth data for the order chosen for the two candidate images above ├── lexica/ # Data based on Lexica (prompts for image generation) │ ├── text_embeddings.pkl # CLIP embeddings of all texts │ ├── faiss_text_index.index # FAISS index to find nearest neighbors in the text embedding space │ ├── image_embeddings.pkl # CLIP embeddings of all images │ ├── faiss_image_index.index # FAISS index to find nearest neighbors in the image embedding space │ ├── doccano_lexica_single_image.jsonl # Contains entries for all image-text pairs in a Doccano-compatible format │ ├── doccano_lexica_closest_clip_match_by_comment.jsonl # Contains entries with two candidate images for each text │ ├── groundtruth_lexica_closest_clip_match_by_comment.json # Groundtruth data for the order chosen for the two candidate images above │ ├── doccano_lexica_closest_clip_match_by_image.jsonl # Contains entries with two candidate images for each text │ └── groundtruth_lexica_closest_clip_match_by_image.json # Groundtruth data for the order chosen for the two candidate images above └── annotation/ # Results of our annotation experiment for validating topics ├── agreement_gallery.html # Overview of annotators' decisions and agreement ├── reddit/ # Doccano exports based on doccano_reddit_closest_clip_match_by_comment.jsonl ├── pexels/ # Doccano exports based on doccano_pexels_closest_clip_match_by_comment.jsonl └── lexica/ # Doccano exports based on doccano_lexica_closest_clip_match_by_comment.jsonl Datasheet The following information is structured according to the Datasheets for Datasets paper. Motivation For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description. This dataset supplements our paper A Resource for Evaluating Creative Search on the Infinite Index (currently under review). It introduces Cranfield-style topics that are designed to evaluate iterative generative systems, which are systems that allow for iterative refinement of generated content through user feedback. One example for such a system would be "prompt engineering on Stable Diffusion". Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? The dataset was created by Niklas Deckers, Aleyna Albayrak, Klara M. Gutekunst, Paul Hahn, Henry Plutz, Moritz Wirth and Martin Potthast at the University of Kassel. Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number. N/A Any other comments? None. Composition What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description. Instances represent short description texts for images, coming from different sources (Reddit, Pexels, Lexica). How many instances are there in total (of each type, if appropriate)? 3721 from Reddit, 109990 from Pexels, 49173 from Lexica Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable). The Reddit data comes from the Pushshift dataset with a cutoff date of December 2022. It is probably not representative for later posts. For images with multiple candidate descriptions, the one with the highest Reddit upvote score was used. What data does each instance consist of? “Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description. The text is available as raw text (escaped for JSON); however, we also provide embeddings. Is there a label or target associated with each instance? If so, please provide a description. N/A Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text. The original images associated with the texts are not part of the released dataset. Are relationships between individual instances made explicit (e.g., users’ movie ratings, social network links)? If so, please describe how these relationships are made explicit. For the compilation of the *_match_by_*.jsonl files, nearest neighbors are formed. Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them. N/A Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description. Not to the knowledge of the authors. Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a dataset consumer? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate. Links to the original images are provided; however, the core contribution is formed by the texts directly given in the dataset. Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor–patient confidentiality, data that includes the content of individuals’ nonpublic communications)? If so, please provide a description. All data was previously made available publicly online. Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why. The data from the Reddit r/captionthis dataset may be considered offensive. Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset. N/A Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how. The original source data is available publicly on Reddit and might allow to identify who posted the data via the original platform. Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals race or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)? If so, please provide a description. Not to the knowledge of the authors. Any other comments? None. Collection Process How was the data associated with each instance acquired? Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (e.g., part-of-speech tags, model-based guesses for age or language)? If the data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how. Data was acquired from Reddit r/captionthis via the Pushshift dataset, the Pexels dataset and Lexica via Huggingface. What mechanisms or procedures were used to collect the data (e.g., hardware apparatuses or sensors, manual human curation, software programs, software APIs)? How were these mechanisms or procedures validated? The software is provided in our Github repository. If the dataset is a sample from a larger set, what was the sampling strategy (e.g., deterministic, probabilistic with specific sampling probabilities)? N/A Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)? For the experiment in annotation/, members of our lab did the rating. Otherwise, N/A. Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)? If not, please describe the timeframe in which the data associated with the instances was created. The Reddit data comes from the Pushshift dataset with a cutoff date of December 2022. Were any ethical review processes conducted (e.g., by an institutional review board)? If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation. N/A Did you collect the data from the individuals in question directly, or obtain it via third parties or other sources (e.g., websites)? See above. Were the individuals in question notified about the data collection? If so, please describe (or show with screenshots or other information) how notice was provided, and provide a link or other access point to, or otherwise reproduce, the exact language of the notification itself. N/A (dataset comes from publicly available sources) Did the individuals in question consent to the collection and use of their data? If so, please describe (or show with screenshots or other information) how consent was requested and provided, and provide a link or other access point to, or otherwise reproduce, the exact language to which the individuals consented. N/A If consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future or for certain uses? If so, please provide a description, as well as a link or other access point to the mechanism (if appropriate). N/A (publicly available sources) Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., a data protection impact analysis) been conducted? If so, please provide a description of this analysis, including the outcomes, as well as a link or other access point to any supporting documentation. None. Any other comments? None. Preprocessing/cleaning/labeling Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? If so, please provide a description. If not, you may skip the remaining questions in this section. Preprocessing for the Reddit dataset involved the removal of unobtainable/404 data, duplicates and matches with a blocklist (to exclude bot comments). Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)? If so, please provide a link or other access point to the “raw” data. See above for the raw data sources. Is the software that was used to preprocess/clean/label the data available? If so, please provide a link or other access point. The software is provided in our Github repository. Any other comments? None. Uses Has the dataset been used for any tasks already? If so, please provide a description. The dataset has been used for annotation experiments for validating the topics and experiments for evaluating generative text-to-image models (as described in our Github repository). Is there a repository that links to any or all papers or systems that use the dataset? If so, please provide a link or other access point. Not yet. What (other) tasks could the dataset be used for? Other generative text-to-image models could be evaluated. Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? For example, is there anything that a dataset consumer might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other risks or harms (e.g., legal risks, financial harms)? If so, please provide a description. Is there anything a dataset consumer could do to mitigate these risks or harms? Some of the captions from the Reddit dataset may be considered offensive by some readers, which should be respected when selecting topics or assessors for the experiments. Are there tasks for which the dataset should not be used? If so, please provide a description. Not to the knowledge of the authors. Any other comments? None. Distribution Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? If so, please provide a description. The dataset has been publicly released. How will the dataset will be distributed (e.g., tarball on website, API, GitHub)? Does the dataset have a digital object identifier (DOI)? The dataset is available via Zenodo: https://doi.org/10.5281/zenodo.20574977 When will the dataset be distributed? The initial release was on 7 June 2026. Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? If so, please describe this license and/or ToU, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions. The dataset has been released under the Creative Commons Attribution 4.0 International license. Copyright issues are mitigated by not redistributing the Reddit images directly and by using only short phrases of user-generated content. Have any third parties imposed IP-based or other restrictions on the data associated with the instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms, as well as any fees associated with these restrictions. N/A Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation. N/A Any other comments? None. Maintainance Who will be supporting/hosting/maintaining the dataset? The dataset is available via Zenodo: https://doi.org/10.5281/zenodo.20574977 How can the owner/curator/manager of the dataset be contacted (e.g., email address)? Niklas Deckers can be contacted via the email address given on his website. Is there an erratum? If so, please provide a link or other access point. Not yet. Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? If so, please describe how often, by whom, and how updates will be communicated to dataset consumers (e.g., mailing list, GitHub)? The dataset might be updated irregularly via Zenodo. If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were the individuals in question told that their data would be retained for a fixed period of time and then deleted)? If so, please describe these limits and explain how they will be enforced. N/A Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to dataset consumers. The older versions will be available via Zenodo. If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? If so, please provide a description. Will these contributions be validated/verified? If so, please describe how. If not, why not? Is there a process for communicating/distributing these contributions to dataset consumers? If so, please provide a description. N/A Any other comments? None.