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Abstract The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data. By using both the work of citizen-science volunteers and machine learning algorithms, the Gravity Spy project enables reliable classification of glitches. Citizen science and machine learning are intrinsically coupled within the Gravity Spy framework, with machine learning classifications providing a rapid first-pass classification of the dataset and enabling tiered volunteer training, and volunteer-based classifications verifying the machine classifications, bolstering the machine learning training set and identifying new morphological classes of glitches. These classifications are now routinely used in studies characterizing the performance of the LIGO gravitational-wave detectors. Providing the volunteers with a training framework that teaches them to classify a wide range of glitches, as well as additional tools to aid their investigations of interesting glitches, empowers them to make discoveries of new classes of glitches. This demonstrates that, when giving suitable support, volunteers can go beyond simple classification tasks to identify new features in data at a level comparable to domain experts. The Gravity Spy project is now providing volunteers with more complicated data that includes auxiliary monitors of the detector to identify the root cause of glitches. 

Abstract Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time–frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of the Gravity Spy analysis of data up to the end of the third observing run of advanced laser interferometric gravitational-wave observatory (LIGO). We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO Livingston into morphological classes. We find that the distribution of glitches differs between the two LIGO sites. This highlights the potential need for studies of data quality to be individually tailored to each gravitational-wave observatory. 

The first successful detection of gravitational waves by ground-based observatories, such as the Laser Interferometer Gravitational-Wave Observatory (LIGO), marked a breakthrough in our comprehension of the Universe. However, due to the unprecedented sensitivity required to make such observations, gravitational-wave detectors also capture disruptive noise sources called glitches, which can potentially be confused for or mask gravitational-wave signals. To address this problem, a community-science project, Gravity Spy, incorporates human insight and machine learning to classify glitches in LIGO data. The machine-learning classifier, integrated into the project since 2017, has evolved over time to accommodate increasing numbers of glitch classes. Despite its success, limitations have arisen in the ongoing LIGO fourth observing run (O4) due to the architecture's simplicity, which led to poor generalization and inability to handle multi-time window inputs effectively. We propose an advanced classifier for O4 glitches. Using data from previous observing runs, we evaluate different fusion strategies for multi-time window inputs, using label smoothing to counter noisy labels, and enhancing interpretability through attention module-generated weights. Our new O4 classifier shows improved performance, and will enhance glitch classification, aiding in the ongoing exploration of gravitational-wave phenomena. 

Socialization is crucial for open collaboration projects as incumbent participants establish and build relationships within their community. However, socialization often excludes engagement in a group's communicative and language practices. This study examines the socialization of newcomers to the communicative and language practices of Gravity Spy. Participants classify image subjects in Gravity Spy to isolate noise signals in gravitational wave detectors. Analyzing the discussion posts of Gravity Spy newcomers, we determine how they assimilate into the group's communicative practices. We find that socialization in a group’s language practices can be a valuable metric for integration. Our results suggest successful socialization is marked by the convergence of newcomers’ language with the community, influencing retention and participation. These findings provide insights into the linguistic dynamics of virtual citizen science communities and propose strategies for enhancing newcomer integration through language adoption. 

Artificial Intelligence (AI) and citizen science (CS) are two approaches to tackling data challenges related to scale and complexity. CS by its very definition relies on the joint effort of typically a distributed group of non-expert people to solve problems in a manner that relies on human intelligence. As AI capabilities increasingly augment or complement human intelligence, if not replicate it, there is a growing effort to understand the role that AI can play in CS and vice versa. With this growing interest as context, this special collection, The Future of AI and Citizen Science, illustrates the many ways that CS practitioners are integrating AI into their efforts, as well as identifies current limitations. In this spirit, our editorial briefly introduces the special collection papers to demonstrate and assess some uses of AI in CS; then, we contextualize these uses in terms of key challenges; and conclude with future directions that use AI with CS in both innovative and ethical ways. 

We explore the bi-directional relationship between human and machine learning in citizen science. Theoretically, the study draws on the zone of proximal development (ZPD) concept, which allows us to describe AI augmentation of human learning, human augmentation of machine learning, and how tasks can be designed to facilitate co-learning. The study takes a design-science approach to explore the design, deployment, and evaluations of the Gravity Spy citizen science project. The findings highlight the challenges and opportunities of co-learning, where both humans and machines contribute to each other’s learning and capabilities. The study takes its point of departure in the literature on co-learning and develops a framework for designing projects where humans and machines mutually enhance each other’s learning. The research contributes to the existing literature by developing a dynamic approach to human-AI augmentation, by emphasizing that the ZPD supports ongoing learning for volunteers and keeps machine learning aligned with evolving data. The approach offers potential benefits for project scalability, participant engagement, and automation considerations while acknowledging the importance of tutorials, community access, and expert involvement in supporting learning. 

This data set contains the individual classifications that the Gravity Spy citizen science volunteers made for glitches through 20 July 2024. Classifications made by science team members or in testing workflows have been removed as have classifications of glitches lacking a Gravity Spy identifier. See Zevin et al. (2017) for an explanation of the citizen science task and classification interface. Data about glitches with machine-learning labels are provided in an earlier data release (Glanzer et al., 2021). Final classifications combining ML and volunteer classifications are provided in Zevin et al. (2022).  22 of the classification labels match the labels used in the earlier data release, namely 1080Lines, 1400Ripples, Air_Compressor, Blip, Chirp, Extremely_Loud, Helix, Koi_Fish, Light_Modulation, Low_Frequency_Burst, Low_Frequency_Lines, No_Glitch, None_of_the_Above, Paired_Doves, Power_Line, Repeating_Blips, Scattered_Light, Scratchy, Tomte, Violin_Mode, Wandering_Line and Whistle. One glitch class that was added to the machine-learning classification has not been added to the Zooniverse project and so does not appear in this file, namely Blip_Low_Frequency. Four classes were added to the citizen science platform but not to the machine learning model and so have only volunteer labels, namely 70HZLINE, HIGHFREQUENCYBURST, LOWFREQUENCYBLIP and PIZZICATO. The glitch class Fast_Scattering added to the machine-learning classification has an equivalent volunteer label CROWN, which is used here (Soni et al. 2021). Glitches are presented to volunteers in a succession of workflows. Workflows include glitches classified by a machine learning classifier as being likely to be in a subset of classes and offer the option to classify only those classes plus None_of_the_Above. Each level includes the classes available in lower levels. The top level does not add new classification options but includes all glitches, including those for which the machine learning model is uncertain of the class. As the classes available to the volunteers change depending on the workflow, a glitch might be classified as None_of_the_Above in a lower workflow and subsequently as a different class in a higher workflow. Workflows and available classes are shown in the table below.  Workflow ID Name Number of glitch classes Glitches added 1610  Level 1 3 Blip, Whistle, None_of_the_Above 1934 Level 2 6 Koi_Fish, Power_Line, Violin_Mode 1935 Level 3 10 Chirp, Low_Frequency_Burst, No_Glitch, Scattered_Light 2360 Original level 4 22 1080Lines, 1400Ripples, Air_Compressor, Extremely_Loud, Helix, Light_Modulation, Low_Frequency_Lines, Paired_Doves, Repeating_Blips, Scratchy, Tomte, Wandering_Line 7765 New level 4 15 1080Lines, Extremely_Loud, Low_Frequency_Lines, Repeating_Blips, Scratchy 2117 Original level 5 22 No new glitch classes 7766 New level 5 27 1400Ripples, Air_Compressor, Paired_Doves, Tomte, Wandering_Line, 70HZLINE, CROWN, HIGHFREQUENCYBURST, LOWFREQUENCYBLIP, PIZZICATO 7767 Level 6 27 No new glitch classes Description of data fields Classification_id: a unique identifier for the classification. A volunteer may choose multiple classes for a glitch when classifying, in which case there will be multiple rows with the same classification_id. Subject_id: a unique identifier for the glitch being classified. This field can be used to join the classification to data about the glitch from the prior data release.  User_hash: an anonymized identifier for the user making the classification or for anonymous users an identifier that can be used to track the user within a session but which may not persist across sessions.  Anonymous_user: True if the classification was made by a non-logged in user.  Workflow: The Gravity Spy workflow in which the classification was made.  Workflow_version: The version of the workflow. Timestamp: Timestamp for the classification.  Classification: Glitch class selected by the volunteer.  Related datasets For machine learning classifications on all glitches in O1, O2, O3a, and O3b, please see Gravity Spy Machine Learning Classifications on Zenodo For classifications of glitches combining machine learning and volunteer classifications, please see Gravity Spy Volunteer Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b. For the training set used in Gravity Spy machine learning algorithms, please see Gravity Spy Training Set on Zenodo. For detailed information on the training set used for the original Gravity Spy machine learning paper, please see Machine learning for Gravity Spy: Glitch classification and dataset on Zenodo. 

Citizen scientists make valuable contributions to science but need to learn about the data they are working with to be able to perform more advanced tasks. We present a set of design principles for identifying the kinds of background knowledge that are important to support learning at different stages of engagement, drawn from a study of how free/libre open source software developers are guided to create and use documents. Specifically, we suggest that newcomers require help understanding the purpose, form and content of the documents they engage with, while more advanced developers add understanding of information provenance and the boundaries, relevant participants and work processes. We apply those principles in two separate but related studies. In study 1, we analyze the background knowledge presented to volunteers in the Gravity Spy citizen-science project, mapping the resources to the framework and identifying kinds of knowledge that were not initially provided. In study 2, we use the principles proactively to develop design suggestions for Gravity Spy 2.0, which will involve volunteers in analyzing more diverse sources of data. This new project extends the application of the principles by seeking to use them to support understanding of the relationships between documents, not just the documents individually. We conclude by discussing future work, including a planned evaluation of Gravity Spy 2.0 that will provide a further test of the design principles. 

{"Abstract":["This dataset contains machine learning and volunteer classifications from the Gravity Spy project. It includes glitches from observing runs O1, O2, O3a and O3b that received at least one classification from a registered volunteer in the project. It also indicates glitches that are nominally retired from the project using our default set of retirement parameters, which are described below. See more details in the Gravity Spy Methods paper. <\/p>\n\nWhen a particular subject in a citizen science project (in this case, glitches from the LIGO datastream) is deemed to be classified sufficiently it is "retired" from the project. For the Gravity Spy project, retirement depends on a combination of both volunteer and machine learning classifications, and a number of parameterizations affect how quickly glitches get retired. For this dataset, we use a default set of retirement parameters, the most important of which are: <\/p>\n\nA glitches must be classified by at least 2 registered volunteers<\/li>Based on both the initial machine learning classification and volunteer classifications, the glitch has more than a 90% probability of residing in a particular class<\/li>Each volunteer classification (weighted by that volunteer's confusion matrix) contains a weight equal to the initial machine learning score when determining the final probability<\/li><\/ol>\n\nThe choice of these and other parameterization will affect the accuracy of the retired dataset as well as the number of glitches that are retired, and will be explored in detail in an upcoming publication (Zevin et al. in prep). <\/p>\n\nThe dataset can be read in using e.g. Pandas: \n```\nimport pandas as pd\ndataset = pd.read_hdf('retired_fulldata_min2_max50_ret0p9.hdf5', key='image_db')\n```\nEach row in the dataframe contains information about a particular glitch in the Gravity Spy dataset. <\/p>\n\nDescription of series in dataframe<\/strong><\/p>\n\n['1080Lines', '1400Ripples', 'Air_Compressor', 'Blip', 'Chirp', 'Extremely_Loud', 'Helix', 'Koi_Fish', 'Light_Modulation', 'Low_Frequency_Burst', 'Low_Frequency_Lines', 'No_Glitch', 'None_of_the_Above', 'Paired_Doves', 'Power_Line', 'Repeating_Blips', 'Scattered_Light', 'Scratchy', 'Tomte', 'Violin_Mode', 'Wandering_Line', 'Whistle']\n\tMachine learning scores for each glitch class in the trained model, which for a particular glitch will sum to unity<\/li><\/ul>\n\t<\/li>['ml_confidence', 'ml_label']\n\tHighest machine learning confidence score across all classes for a particular glitch, and the class associated with this score<\/li><\/ul>\n\t<\/li>['gravityspy_id', 'id']\n\tUnique identified for each glitch on the Zooniverse platform ('gravityspy_id') and in the Gravity Spy project ('id'), which can be used to link a particular glitch to the full Gravity Spy dataset (which contains GPS times among many other descriptors)<\/li><\/ul>\n\t<\/li>['retired']\n\tMarks whether the glitch is retired using our default set of retirement parameters (1=retired, 0=not retired)<\/li><\/ul>\n\t<\/li>['Nclassifications']\n\tThe total number of classifications performed by registered volunteers on this glitch<\/li><\/ul>\n\t<\/li>['final_score', 'final_label']\n\tThe final score (weighted combination of machine learning and volunteer classifications) and the most probable type of glitch<\/li><\/ul>\n\t<\/li>['tracks']\n\tArray of classification weights that were added to each glitch category due to each volunteer's classification<\/li><\/ul>\n\t<\/li><\/ul>\n\n <\/p>\n\n```\nFor machine learning classifications on all glitches in O1, O2, O3a, and O3b, please see Gravity Spy Machine Learning Classifications on Zenodo<\/p>\n\nFor the most recently uploaded training set used in Gravity Spy machine learning algorithms, please see Gravity Spy Training Set on Zenodo.<\/p>\n\nFor detailed information on the training set used for the original Gravity Spy machine learning paper, please see Machine learning for Gravity Spy: Glitch classification and dataset on Zenodo. <\/p>"]}