The Replica Dataset is a dataset of high quality reconstructions of a variety of indoor spaces. Each reconstruction has clean dense geometry, high resolution and high dynamic range textures, glass and mirror surface information, planar segmentation as well as semantic class and instance segmentation. Source: The Replica Dataset: A Digital Replica of Indoor Spaces

Dynamic Replica is a synthetic dataset of stereo videos featuring humans and animals in virtual environments. It is a benchmark for dynamic disparity/depth estimation and 3D reconstruction consisting of 145,200 stereo frames (524 videos). The dataset contains annotations for left and right views that include: camera intrinsics and extrinsics, image depth, instance segmentation masks, binary foreground / background segmentation masks, optical flow, long-range pixel trajectories

Dataset Overview This dataset contains individual-level data from a randomized controlled trial (RCT) conducted in northern Uganda, along with associated satellite imagery. It is designed to investigate how treatment effects may vary across different geographical and contextual settings by leveraging both tabular and image-based variables. # Motivation and Content - Researchers often wish to explore treatment effect heterogeneity, especially in studies focused on global poverty. Traditional variables—such as age and ethnicity—are typically collected near the time of data gathering and may overlook broader environmental, historical, or neighborhood-specific factors. - Incorporating satellite images into causal inference analyses provides a valuable window into such contextual factors. This dataset exemplifies how researchers can combine tabular data (e.g., demographic variables, outcomes, treatment indicators) with geospatially keyed satellite imagery to model and interpret how treatment effects change across different locations. # Potential Use Cases - Causal Inference Research: Apply image-based methods to detect and explain geographic or contextual heterogeneity in RCT outcomes. - Policy Evaluation: Aid policymakers in identifying areas or populations most likely to benefit from poverty-alleviation interventions. - Methodological Innovations: Serve as a testbed for new models that integrate high-dimensional or unstructured data (images) with standard tabular data in the causal inference setting. # Source Connor T. Jerzak, Fredrik Johansson, Adel Daoud. Image-based Treatment Effect Heterogeneity. Proceedings of the Second Conference on Causal Learning and Reasoning (CLeaR), Proceedings of Machine Learning Research (PMLR), 213: 531-552, 2023.

ReplicaGrasp dataset is created by spawning objects from GRAB into the ReplicaCAD scenes, simulated in random positions and orientations using the Habitat simulator. We capture 4,800 instances, with 50 different objects spawned in one of 48 receptacles in both, upright and randomly fallen orientations. Source: FLEX: Full-Body Grasping Without Full-Body Grasps Image Source: https://arxiv.org/pdf/2211.11903v1.pdf

Transaction fee mechanism (TFM) is an essential component of a blockchain protocol. However, a systematic evaluation of the real-world impact of TFMs is still absent. Using rich data from the Ethereum blockchain, mempool, and exchanges, we study the effect of EIP-1559, one of the first deployed TFMs that depart from the traditional first-price auction paradigm. We conduct a rigorous and comprehensive empirical study to examine its causal effect on blockchain transaction fee dynamics, transaction waiting time and security. Our results show that EIP-1559 improves the user experience by making fee estimation easier, mitigating intra-block difference of gas price paid, and reducing users' waiting times. However, EIP-1559 has only a small effect on gas fee levels and consensus security. In addition, we found that when Ether's price is more volatile, the waiting time is significantly higher. We also verify that a larger block size increases the presence of siblings. These findings suggest new directions for improving TFM. Paper on arXiv: https://arxiv.org/abs/2201.05574 Code and Script on GitHub: https://github.com/SciEcon/EIP1559

This is the replication data for the paper: "Crossing the Linguistic Causeway: A Binational Approach for Translating Soundscape Attributes to Bahasa Melayu". * contains survey responses to a quantitative evaluation survey adopted from Watcharasupat et al., 2022

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset

Code and Data for Replication of "Microsimulation Estimates of Decision Uncertainty and Value of Information Are Biased but Consistent" This is the full data set for replication of all results in the paper along with the R code for doing so.

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset

This tree was generated as indicated above in the methods. The model chosen by the algorithm was LG_F_R10.

This tree was generated as indicated above in the methods. The model chosen by the algorithm was LG+F+R8.

This tree was generated as indicated above in the text for file S3 using model LG+F+G.

This tree was generated as indicated above in the methods. The model chosen by the algorithm was LG+R8.

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset

Bitcoin is a peer-to-peer electronic payment system that popularized rapidly in recent years. Usually, we need to query the complete history of bitcoin blockchain data to acquire variables of economic meaning. This becomes increasingly difficult now with over 1.6 billion historical transactions on the Bitcoin blockchain. It is thus important to query Bitcoin transaction data in a way that is more efficient and provides economic insights. We apply cohort analysis that interprets bitcoin blockchain data using methods developed for population data in social science. Specifically, we query and process the Bitcoin transaction input and output data within each daily cohort. With this, we then create datasets and visualizations for some key indicators of bitcoin transactions, including the daily lifespan distributions of accumulated spent transaction output (STXO) and the daily age distributions of accumulated unspent transaction output (UTXO). We provide a computationally feasible approach to characterize bitcoin transactions, which paves the way for future studies of economic behaviors in the emerging market of Bitcoin. Github:https://github.com/SciEcon/UTXO arXiv: https://arxiv.org/abs/2103.0017 Nature Research: https://www.nature.com/articles/s41597-022-01254-0 Nature PDF: https://rdcu.be/cKRkg

Blockchain has empowered computer systems to be more secure using a distributed network. However, the current blockchain design suffers from fairness issues in transaction ordering. Miners are able to reorder transactions to generate profits, the so-called miner extractable value (MEV). Existing research recognizes MEV as a severe security issue and proposes potential solutions, including prominent Flashbots. However, previous studies have mostly analyzed blockchain data, which might not capture the impacts of MEV in a much broader AI society. Thus, in this research, we applied natural language processing (NLP) methods to comprehensively analyze topics in tweets on MEV. We collected more than 20000 tweets with #MEV and #Flashbots hashtags and analyzed their topics. Our results show that the tweets discussed profound topics of ethical concern, including security, equity, emotional sentiments, and the desire for solutions to MEV. We also identify the co-movements of MEV activities on blockchain and social media platforms. Our study contributes to the literature at the interface of blockchain security, MEV solutions, and AI ethics. (2023-07-06)

This dataset contains the data used for all statistical comparisons in our ICSV 2022 submission "Assessment of a Cost-Effective Headphone Calibration Procedure for Soundscape Evaluations", summarised in a single .csv file. <br><br> To obtain the data in this dataset, 17 participants were invited to each rate 27 stimuli twice, once with the stimuli calibrated with a head-and-torso simulator ("HATS method") and once with the stimuli calibrated via an open-circuit voltage method ("OCV method"). This resulted in a total of 17272 = 918 data samples, corresponding to the number of rows in the .csv file. <br><br> For more details on the calibration method and listening test procedure, please refer to our manuscript: <br><br> B. Lam, K. Ooi, K.N. Watcharasupat, Y.-T. Lau, Z.-T. Ong, T. Wong, W.-S. Gan, "Assessment of a Cost-Effective Headphone Calibration Procedure for Soundscape Evaluations", in <i>Proceedings of the 28th International Congress on Sound and Vibration</i>, ICSV28, Singapore, 2022. <br><br> A short explanation of the columns in the .csv file is as follows: <ul> <li><code>calib_method</code>: The method used to calibrate the stimulus for which the present row of responses was obtained (either <code>HATS</code> or <code>OCV</code>)</li> <li><code>participant_idx</code>: The index of the participant who provided the present row of responses. Each participant is assigned a unique index. In addition, indices do not start from 1 because we omitted data from participants who adjusted volume settings when listening to the stimuli (thus rendering the calibration invalid), as well as participants who were unable to provide a complete set of responses for stimuli calibrated using both calibration methods.</li> <li><code>stimulus_idx</code>: The index of the stimulus for which the present row of responses was obtained (integers from 1 to 27, inclusive).</li> <li><code>pleasant</code>: The rating given by the participant for the present stimulus on a scale of 0 to 100 (inclusive) for the attribute "pleasant".</li> <li><code>chaotic</code>: The rating given by the participant for the present stimulus on a scale of 0 to 100 (inclusive) for the attribute "chaotic"</li> <li><code>vibrant</code>: The rating given by the participant for the present stimulus on a scale of 0 to 100 (inclusive) for the attribute "vibrant"</li> <li><code>uneventful</code>: The rating given by the participant for the present stimulus on a scale of 0 to 100 (inclusive) for the attribute "uneventful"</li> <li><code>calm</code>: The rating given by the participant for the present stimulus on a scale of 0 to 100 (inclusive) for the attribute "calm"</li> <li><code>annoying</code>: The rating given by the participant for the present stimulus on a scale of 0 to 100 (inclusive) for the attribute "annoying"</li> <li><code>eventful</code>: The rating given by the participant for the present stimulus on a scale of 0 to 100 (inclusive) for the attribute "eventful"</li> <li><code>monotonous</code>: The rating given by the participant for the present stimulus on a scale of 0 to 100 (inclusive) for the attribute "monotonous"</li> </ul>

Decentralized finance (DeFi) is known for its unique mechanism design, which applies smart contracts to facilitate peer-to-peer transactions. The decentralized bank is a typical DeFi application. Ideally, a decentralized bank should be decentralized in the transaction. However, many recent studies have found that decentralized banks have not achieved a significant degree of decentralization. This research conducts a comparative study among mainstream decentralized banks. We apply core-periphery network features analysis using the transaction data from four decentralized banks, Liquity, Aave, MakerDao, and Compound. We extract six features and compare the banks' levels of decentralization cross-sectionally. According to the analysis results, we find that: 1) MakerDao and Compound are more decentralized in the transactions than Aave and Liquity. 2) Although decentralized banking transactions are supposed to be decentralized, the data show that four banks have primary external transaction core addresses such as Huobi, Coinbase, and Binance, etc. We also discuss four design features that might affect network decentralization. Our research contributes to the literature at the interface of decentralized finance, financial technology (Fintech), and social network analysis and inspires future protocol designs to live up to the promise of decentralized finance for a truly peer-to-peer transaction network. (2023-07-06)

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide:# Can Large Language Models (or Humans) Disentangle Text? ## Abstract We investigate the potential of large language models (LLMs) to disentangle text variables—to remove the textual traces of an undesired forbidden variable in a task sometimes known as text distillation and closely related to the fairness in AI and causal inference literature. We employ a range of various LLM approaches in an attempt to disentangle text by identifying and removing information about a target variable while preserving other relevant signals. We show that in the strong test of removing sentiment, the statistical association between the processed text and sentiment is still detectable to machine learning classifiers post-LLM-disentanglement. Furthermore, we find that human annotators also struggle to disentangle sentiment while preserving other semantic content. This suggests there may be limited separability between concept variables in some text contexts, highlighting limitations of methods relying on text-level transformations and also raising questions about the robustness of disentanglement methods that achieve statistical independence in representation space if this is difficult for human coders operating on raw text to attain. ## Repository Details This repository contains data from human-coded and processed reviews from the main paper results. ## Paper Link Nicolas Audinet de Pieuchon, Adel Daoud, Connor T. Jerzak, Moa Johansson, Richard Johansson. Can Large Language Models (or Humans) Disentangle Text?. Proceedings of the Sixth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS 2024): 57-67, 2024. [PDF] * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset

Audiogram data based on a "gold standard" audiometer and the uHear iOS application of 163 participants

The dataset provides information about 450 HYIPs collected between November 2020 and September 2021. This dataset was analyzed and the results are discussed in the paper.

Replication Data for: On estimating Armington elasticities for Japan's meat imports contains monthly import values and quantities from Jan 1996 to Dec 2020 for all 78 items.

As CryptoPunks pioneers the innovation of non-fungible tokens (NFTs) in AI and art, the valuation mechanics of NFTs has become a trending topic. Earlier research identifies the impact of ethics and society on the price prediction of CryptoPunks. Since the booming year of the NFT market in 2021, the discussion of CryptoPunks has propagated on social media. Still, existing literature hasn't considered the social sentiment factors after the historical turning point on NFT valuation. In this paper, we study how sentiments in social media, together with gender and skin tone, contribute to NFT valuations by an empirical analysis of social media, blockchain, and crypto exchange data. We evidence social sentiments as a significant contributor to the price prediction of CryptoPunks. Furthermore, we document structure changes in the valuation mechanics before and after 2021. Although people's attitudes towards Cryptopunks are primarily positive, our findings reflect imbalances in transaction activities and pricing based on gender and skin tone. Our result is consistent and robust, controlling for the rarity of an NFT based on the set of human-readable attributes, including gender and skin tone. Our research contributes to the interdisciplinary study at the intersection of AI, Ethics, and Society, focusing on the ecosystem of decentralized AI or blockchain. We provide our data and code for replicability as open access on GitHub. (2023-07-06)

The model forecasts for the sub-seasonal forecasting application considered in the Online Learning under Optimism and Delay paper experiments. This dataset consists of a single ZIP archive (919MB) that contains 1) a "models" folder that contains, for each model the forecasts for the Precip. 3-4w, Precip. 5-6w, Temp. 3-4w, Temp. 5-6w tasks on the western United States geography, and 2) a "data" folder that contains supporting geographic data. The data should be used to reproduce the PoolD experiments in https://github.com/geflaspohler/poold as described in the README. (2021-06-10)

This dataset contains the data used for all statistical analysis in our publication "Singapore Soundscape Site Selection Survey (S5): Identification of Characteristic Soundscapes of Singapore via Weighted k-means Clustering", summarised in a single .csv file. For more details on the study methodology, please refer to our manuscript: Ooi, K.; Lam, B.; Hong, J.; Watcharasupat, K. N.; Ong, Z.-T.; Gan, W.-S. Singapore Soundscape Site Selection Survey (S5): Identification of Characteristic Soundscapes of Singapore via Weighted k-means Clustering. Sustainability, 2022. For our replication code utilising this data, please refer to our Github repository: https://github.com/ntudsp/singapore-soundscape-site-selection-survey A short explanation of the columns in the .csv file is as follows: Full of life & exciting [Latitude]: The latitude, in degrees, of the location chosen by the participant as "Full of life & exciting". Full of life & exciting [Longitude]: The longitude, in degrees, of the location chosen by the participant as "Full of life & exciting". Full of life & exciting [# times visited]: The number of times that the participant had visited the chosen location they considered "Full of life & exciting" before, as reported by the participant. Full of life & exciting [Duration]: The average duration per visit to the chosen location the participant considered "Full of life & exciting", as reported by the participant. Chaotic & restless [Latitude]: The latitude, in degrees, of the location chosen by the participant as "Chaotic & restless". Chaotic & restless [Longitude]: The longitude, in degrees, of the location chosen by the participant as "Chaotic & restless". Chaotic & restless [# times visited]: The number of times that the participant had visited the chosen location they considered "Chaotic & restless" before, as reported by the participant. Chaotic & restless [Duration]: The average duration per visit to the chosen location the participant considered "Chaotic & restless", as reported by the participant. Calm & tranquil [Latitude]: The latitude, in degrees, of the location chosen by the participant as "Calm & tranquil". Calm & tranquil [Longitude]: The longitude, in degrees, of the location chosen by the participant as "Calm & tranquil". Calm & tranquil [# times visited]: The number of times that the participant had visited the chosen location they considered "Calm & tranquil" before, as reported by the participant. Calm & tranquil [Duration]: The average duration per visit to the chosen location the participant considered "Calm & tranquil", as reported by the participant. Boring & lifeless [Latitude]: The latitude, in degrees, of the location chosen by the participant as "Boring & lifeless". Boring & lifeless [Longitude]: The longitude, in degrees, of the location chosen by the participant as "Boring & lifeless". Boring & lifeless [# times visited]: The number of times that the participant had visited the chosen location they considered "Boring & lifeless" before, as reported by the participant. Boring & lifeless [Duration]: The average duration per visit to the chosen location the participant considered "Boring & lifeless", as reported by the participant.

Dataset and Stata codes for replicating Tables 1, 3 and Figures 1-4.

Census statistics play a key role in public policy decisions and social science research. However, given the risk of revealing individual information, many statistical agencies are considering disclosure control methods based on differential privacy, which add noise to tabulated data. Unlike other applications of differential privacy, however, census statistics must be postprocessed after noise injection to be usable. We study the impact of the U.S. Census Bureau’s latest disclosure avoidance system (DAS) on a major application of census statistics, the redrawing of electoral districts. We find that the DAS systematically undercounts the population in mixed-race and mixed-partisan precincts, yielding unpredictable racial and partisan biases. While the DAS leads to a likely violation of the “One Person, One Vote” standard as currently interpreted, it does not prevent accurate predictions of an individual’s race and ethnicity. Our findings underscore the difficulty of balancing accuracy and respondent privacy in the Census.

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: Data and R script for replicating the analyses in Schneider (2018): Two-Step QCA Revisited

This is our replication package for our study on Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud. All scalability experiments are performed with the scalability benchmarking framework Theodolite at Kiel University's Software Performance Engineering Lab (SPEL) or Google Cloud. With this replication package, we provide: * Benchmark execution files in executions, * our benchmark (raw) results in results, * and analysis script for our results in analysis.

This is the replication package for our systematic literature review and can be used for the reproducibility of the individual steps of our search and selection methodology.

This package contains the data and the reported results for the manuscript: Keo B, Li B, Younis W (2025) Measuring trade costs and analyzing the determinants of trade growth between Cambodia and major trading partners: 1993–2019. PLoS ONE 20(1): e0311754. https://doi.org/10.1371/journal.pone.0311754 The data that support the findings of this study can be found in the research data repository: Keo, B., Li, B., & Younis, W. (2025). Replication package for "Measuring trade costs and analyzing the determinants of trade growth between Cambodia and major trading partners: 1993-2019" [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15253951

This repository contains replication data to the paper titled: "Anti-noise window: subjective perception of active noise reduction and effect of informational masking"

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset

Perturbed version of the data used in Van Dijcke, Gunsilius, and Wright (2024). Contains (perturbed) tenure in days and job title of (anonymized) employees at US tech companies.

Perturbed version of the data used in Van Dijcke, Gunsilius, and Wright (2024). Contains (perturbed) tenure in days and job title of (anonymized) employees at US tech companies.

County-level and municipal-level data on private election administration grant receipt, census data, and election outcomes all in tabular form

This is the replication data for the paper: "Crossing the Linguistic Causeway: Ethnonational Differences on Soundscape Attributes in Bahasa Melayu".

Description: Replication data and code for Aref, S., and Neal, Z.P., "Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance" (2021) Scientific Reports. http://dx.doi.org/10.1038/s41598-021-98139-w

Blockchain has empowered computer systems to be more secure using a distributed network. However, the current blockchain design suffers from fairness issues in transaction ordering. Miners are able to reorder transactions to generate profits, the so-called miner extractable value (MEV). Existing research recognizes MEV as a severe security issue and proposes potential solutions, including prominent Flashbots. However, previous studies have mostly analyzed blockchain data, which might not capture the impacts of MEV in a much broader AI society. Thus, in this research, we applied natural language processing (NLP) methods to comprehensively analyze topics in tweets on MEV. We collected more than 20000 tweets with #MEV and #Flashbots hashtags and analyzed their topics. Our results show that the tweets discussed profound topics of ethical concern, including security, equity, emotional sentiments, and the desire for solutions to MEV. We also identify the co-movements of MEV activities on blockchain and social media platforms. Our study contributes to the literature at the interface of blockchain security, MEV solutions, and AI ethics. (2023-07-06) Subject

As CryptoPunks pioneers the innovation of non-fungible tokens (NFTs) in AI and art, the valuation mechanics of NFTs has become a trending topic. Earlier research identifies the impact of ethics and society on the price prediction of CryptoPunks. Since the booming year of the NFT market in 2021, the discussion of CryptoPunks has propagated on social media. Still, existing literature hasn't considered the social sentiment factors after the historical turning point on NFT valuation. In this paper, we study how sentiments in social media, together with gender and skin tone, contribute to NFT valuations by an empirical analysis of social media, blockchain, and crypto exchange data. We evidence social sentiments as a significant contributor to the price prediction of CryptoPunks. Furthermore, we document structure changes in the valuation mechanics before and after 2021. Although people's attitudes towards Cryptopunks are primarily positive, our findings reflect imbalances in transaction activities and pricing based on gender and skin tone. Our result is consistent and robust, controlling for the rarity of an NFT based on the set of human-readable attributes, including gender and skin tone. Our research contributes to the interdisciplinary study at the intersection of AI, Ethics, and Society, focusing on the ecosystem of decentralized AI or blockchain. We provide our data and code for replicability as open access on GitHub.