dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , exposure adjusted as needed and cropped tightly around flukes), with IDs assigned to each individual whale. The minimum data required for each encounter were: date, location, and photo-ID image or confirmed individual ID. All encounters of each whale were preserved, and all available supporting attribute data were maintained with the encounter; this could include filename, date, time, location, individual ID from the collaborator's naming/numbering system, observer names, vessel name, observed whale sex, age class, health, behavior, group composition and any further observations. Because the state of every dataset varied at the time of delivery, all data were managed through the following standard series of steps: 1. Image management and matching: Images were quality-controlled through cropping tightly around the flukes and, if necessary, exposure adjustment to facilitate algorithmic ID followed by visual ID confirmation.
All images were quality-scored on a 0-5 scale as described in a previous study  42  , where 0 represented photos in which no photo-ID features were visible, and 1-5 represented very poor to excellent quality photos, respectively. All photo-ID images were matched to a progressively growing set of known whales via an automated image recognition system  42. Every match proposed by the system was manually confirmed by a trained observer. All matches that could be visually confirmed by a trained observer were maintained regardless of image quality. A previous study established that 97-99% of potential matches are found by this method for good-to high-quality images  42   Many-to-one reference catalogs. All images were automatically matched against all individual humpback whales known at the time of each respective dataset integration.
e. , unmatched) individuals were added to the continually growing reference set, with the collaborator ID, if available, or with a newly assigned Happywhale catalog ID. False positives (where two different whales were combined into one individual record) were minimized through trained observer review of every match. Community science data contributions. Opportunistic images submitted through Happywhale were also matched against all known whales, supplementing the research collaboration with community sciencesourced encounter data. The same image and data quality control standards were applied as described above. All community science data contributors implicitly acknowledged their choice of data usage rights during the submission process and had the option of changing usage rights settings among established levels of Creative Commons usage rights (https:// en.
wikip edia. org/ wiki/ Creat ive_ Commo ns_ licen se). Unlike research collaborators participating under the terms of the MOA, public contributors did not have the option of restricting public visibility. Public contributors had access to an encounter comment system whereby suspected data errors and outliers could be brought to the attention of data managers, creating a feedback loop for review and error detection. Information system structure and development. The NPPID data management system integrated a workflow of image processing, individual identification, and recording and curating encounter and individual attribute information. Data were structured through units of contributors (i. e. , "users"), images, encounters, individual humpback whales, and surveys (i. e. , "voyages"), linked by a series of workflow processes (Fig.
1 ). The cloud-based information architecture was composed of a dedicated server for the Java Spring application using a PostgreSQL database populated with Darwin Core compatible fields  44. Submitted binary media were stored in a Simple Storage Service (S3) system for global retrieval. The ID system used a combination of a Node server and a Python Flask app to run the PyTorch-based ID algorithm. During the collaboration, ongoing system development brought enhanced functionality and sophistication to data management processes within the Happywhale. com web platform. In 2021, the automated image recognition system was rebuilt to deliver results in under 0. 1 s per image. This efficiency reduced server load, which has accommodated direct access by collaborators to batch process photo-ID images directly via web and mobile app interfaces in the lab or field.
Near-instantaneous access to image processing was adopted by many collaborators to facilitate more efficient and effective internal data management. NPPID collaborators were invited to directly manage their data import process and ongoing curation, with training, feedback, and quality control oversight by system managers. Some collaborators used the system as a principal repository of their data while others maintained their own separate data management systems during the study. As import and management tools developed in a constantly evolving system, collaborators were increasingly enabled and encouraged to manage their own data. System use, public outreach, and data accessibility. The FAIR Principles (Findable, Accessible, Interoperable, and Reusable) for scientific data  45  guided system design.
Analytics-Documenting detection probability. The 2004-2006 SPLASH project actively developed collaborations and supported field efforts in all known (at the time) North Pacific humpback whale breeding and feeding areas, in pursuit of comprehensively representative sample sizes  8. In contrast, the NPPID project relied on contributions from existing datasets, ongoing field efforts, and community science image contributions. With successive integration of datasets, detection probabilities progressively increased, leading to a predominance of resightings (documenting an individual multiple times) and fewer new whales added to the comprehensive catalog. This caused a shift in methodology from predominantly cataloging new whales to confirming matches Hawai'i of known whales. To understand the proportion of the populations sampled in our growing known dataset, we plotted a discovery curve of new versus total identified individuals (Fig.
Results The NPPID collaboration involved 43 research organizations and included data from all nations around the North Pacific rim where humpback whales are known to regularly occur (Tables  1  and 2 , Fig. 5 ). The complete NPPID collaboration ocean-basin dataset totaled 30,100 individual whales (February 1977 through August 2022 encompassing all available data). A total of 27,956 unique individuals were documented in 157,350 encounters during the 2001-2021 study period (Table  2 , Fig. 2 ). Effort was variable over time: it was much higher in some areas relative to others, and skewed to the central and eastern North Pacific. However, data collection occurred   An annual average of 87% of individuals (84-92%) were documented in more than one season (Table  3 , by region Fig. 5 ), averaging 5.
The range of false negatives across collaborator catalogs of greater than 100 individuals was 0. 1-11%. In the SPLASH dataset of 7971 total individuals, 331 (4%) previously undetected false negatives were found. False positive errors, where two or more whales were confused as one individual, were far less likely than false negatives, prevented by manual review of each proposed match. False positives error rates were estimated to be below 0. 1%. Over 5700 encounter comments were received through Happywhale's online comment fields from researchers and community scientists, in many cases alerting data managers to potential errors in date, location and/or whale identities.
Discussion The NPPID collaboration established a comprehensive, broad-scale, and rich dataset made possible by a rapid and rewarding feedback process connecting collaborator and community science data around the North Pacific Ocean basin. The NPPID collaboration is the first of its kind to develop a long-term individual ID database on this scale. This effort established a unique dataset foundation well-suited for humpback whale population modeling, as well as for any research benefitting from individual identification, such as longitudinal studies of individual health. This study began during the development of fast and accurate automated image recognition for humpback whale flukes and demonstrated the scalability for the algorithm used. We could not initially predict how comprehensively we might document the populations of humpback whales across the NPPID study area.
However, in a relatively short period the results exceeded expectations. As of August 2022, 56 months after the creation of this study, 30,100 individual North Pacific humpback whales had been documented. Some regions are now extremely well sampled. For example, in Southeast Alaska and northern British Columbia for 2011-2019, fewer than 6% of individuals encountered each year were unique (encountered in only one season) (Table  3 , Fig. 5 ). The annual set of newly documented individuals includes recruitment of calves and juveniles, and a progressively smaller proportion of previously undocumented adults. Data gaps exist, particularly in the western North Pacific, in remote feeding areas such as the Aleutian Islands, and in the Mexican offshore breeding area of the Revillagigedo Islands, where effort was far less than in most breeding, feeding and migratory corridor areas of the central and eastern Pacific.
In the Northwestern Hawaiian Islands archipelago, recent acoustic-based surveys including those using wave-glider technology have revealed substantial singing and thus humpback whale abundance with relatively little fluke ID effort  [46] [47] [48]. It remains to be determined if the majority of these whales use this region as a terminal breeding ground, or whether they mix during a breeding season with those whales in the main Hawaiian Islands. However, even in these least-sampled regions, over 50% of individuals were encountered in more than one season, in the same or in different regions. Thus, we believe that the great majority of individuals in all the North Pacific, including the less sampled regions, are documented in the NPPID dataset. By extensively resampling populations in breeding grounds, migratory corridors, and feeding areas, the impact of effort bias on population models can be reduced  21.
We believe applies to the NPPID dataset. Accessibility and user agreements. Data collection should not be an end unto itself, and sharing is a core tenet of good data management  49. The Happywhale web platform was developed to make data accessible by design, aiming for a user experience that is both easy and rewarding. Users were motivated to contribute more and higher-quality data by a simple user interface to upload images, which then rewarded them with rapid results of information about "their" individual whales. Accessibility creates a public good as a resource for research, education, resource management, and science communication. In the existing NPPID dataset, 66% of all North Pacific humpback whale encounter data are publicly visible. Researchers and community scientists can explore migratory connections across the North Pacific via the web platform (Fig.
2 ). For research collaborators, this has inspired studies that would not have been possible without the large collective investment in building a platform and populating it with a comprehensive and contemporary dataset  50, 51. As of December 2022, the NPPID had contributed data to seven other collaborative peer-reviewed publications  13, 37, 38, [52] [53] [54] [55]. Accessible information about North Pacific humpback whale individuals has also proven very useful for resource managers, for example in tracking fishing gear entanglement cases, and individual identification and past sighting histories of dead or stranded whales  56. We recognize that including many actors and an open-science stance can add complexity to a collaboration 57 with concerns for misuse of shared or public data  58.
Successful aspects of this collaboration bring opportunities   but also pose two challenges that the collaboration must address: (1) How do we encourage contributing researchers to allow public visibility of data to allow the widest possible benefit, while ensuring data are used correctly in context, with proper credit preserved? (2) How do we simplify and clarify co-authorship policies to be effective, meaningful, and not so complex as to hinder publication? An ideal collaboration builds datasets that directly answer present biological and management questions, and simultaneously creates data-sharing readiness. Data readiness for study of ecological change depends on both standardized repositories and aligned research interests  13, 59, 60. The NPPID dataset has been successfully applied in this context, contributing to challenging management issues such as the US West Coast Dungeness crab fishery.
Here, researchers can readily determine the proportion of whales in the Endangered Central American DPS  51, [61] [62] [63]. The NPPID collaboration began with a MOA, offering co-authorship to contributors in a series of publications investigating humpback whale migratory patterns and population status in the North Pacific. Collaborators wishing to address additional research questions must seek permission from all relevant data contributors. While the communication required is a cost imposed on prospective studies, community is built around mutually beneficial collaboration. The MOA created an effective working group and context for this study through the completion of the specified series of publications. Future success will require clear use, sharing, and management policy, with oversight and funding maintained into the future.
Data quality improved by accessibility. Accessibility adds value as part of the FAIR Principles for scientific data  45  that guided this study design. Accessibility also serves the immediate practical value of improving data quality, consistency, and repeatability. Active collaboration and public access to data make knowledge gaps more visible and encourages effort to fill them  64. With many eyes reviewing the dataset, from curious public enthusiasts exploring encounters of "their" whales or an area of their personal interest to research collaborators pursuing diverse lines of inquiry, an ongoing collaborative quality control process frequently detects data discrepancies. Happywhale user comments-over 5,700 as of August 2022-alerted NPPID data managers to enough errors that public accessibility to review might be considered as a systematic method of quality control, worthy of attention for its own value and efficiency.
All datasets will contain errors; more accurate image recognition, repeatedly applied, and review of data by diverse users will continually detect some, but not all errors. The SPLASH study estimated a 9-10% rate of missed matches using trained human matchers, the largest model error correction factor in the associated mark-recapture population estimate  21. This kind of accuracy assessment rarely appears in photo-ID based markrecapture studies, yet missed matches were detected in every dataset larger than 100 individuals involved in this study. Our finding of 331 false negatives in 7971 (4%) total individuals in the SPLASH study, when added to algorithm error rates for good-to-high quality images of 1-3%  42  , suggests the 9-10% error estimation was high by 3-4%. In our most accurately matched large dataset, the 2004-2020 whales of Glacier Bay National Park and Preserve, Alaska, missed matches accounted for only 0.
15% (1 of 633 individuals, a first-summer calf to adult match with substantial fluke pigment change). All other datasets of more than 100 individuals showed from 2 to 11% detectable false negative missed match rates. Considering this range and other sources of error and bias, it is important to understand and account for limitations in any dataset, including ours. Effort bias and appropriate use. Ideally, a dataset should be created with its specific use in mind a priori, following a good data management plan  49  with an optimized data workflow  65. However, because we built a dataset gathered from post-SPLASH photo-ID archives and opportunistic efforts, standardization had to stand in for a priori data management plans. The effort was geographically and temporally heterogeneous, and any study design or interpretation of data must account for this to ensure appropriate use.
It would be easy, for example, to falsely interpret the lower effort in the western North Pacific as evidence of smaller whale populations. Datasets cannot be assumed to provide an error-free documentation of humpback whale presence in the study area (i. e. , devoid of effort bias); no clear rule can be set a priori to identify the appropriate application of an evolving dataset of this nature. It is therefore imperative that any potential data user actively engage directly with collaborating researchers to understand data limitations and potential. Data contributors can also be the primary data users, a group that will benefit from increased knowledge of and aptitude with the data management system built through Happywhale. Because there could not be a comprehensive data collection plan across this large scale of a study area and time period, the full dataset might be considered opportunistic, a sum of collected efforts of dedicated research, research from platforms of opportunity, and community science contributions.
4 ), though we believe sampling will recover and continue to increase. Building a successful collaboration. The NPPID study benefitted from the largely successful precedent of the SPLASH study both in providing a foundation of data (Fig. 4 ) and as a collaborative framework. The current study began at a time when new methods were needed to efficiently manage large volumes of post-SPLASH data, where separate research efforts were constrained by time-intensive visual matching of photo-ID datasets. Although the SPLASH study produced notable insight and remains frequently cited, and the catalog was made available online, the study was not intended to continue beyond 2006, and the online dataset was not built to facilitate photo-ID matching. The role of the NPPID collaboration agreement was to establish clear expectations and create an environment of openness, trust, transparency, and consistency.
This context was necessary for research collaborators to feel comfortable sharing images and data that were products of many thousands of person-hours and costs in the field. Positive and useful feedback delivered by rapid results from image recognition efforts was also necessary. Researchers were enticed to join the collection in part by the instant gratification when most of their flukes immediately matched to known individuals; this was a welcome change from years of toil over visually matching isolated photo-ID datasets. Success was crafted by a combination of a high-quality product supported by solid guiding principles of Transparency, Responsibility, User focus, Sustainability and Technology (TRUST), to promote digital repository trustworthiness  66. The idea behind these principles is that as a data repository, we must earn the trust of the community we serve and demonstrate that we are reliable and capable of appropriately managing the data we curate.
Empowerment comes through this intentional framework, with a feeling of collective ownership rather than isolated possession. This then supports sustainable collaboration by creating active participation of research users. As an ongoing, living dataset, the NPPID developed active, increasingly decentralized participation in ongoing data management with an intent to serve diverse needs in the research community. System development remains ongoing, with a focus on providing research collaborators with tools to become more directly involved with data management. This development reduces centralized data management costs, serves the real-time needs of collaborators, and benefits the dataset with local expertise, potentially detecting data issues that would not be recognized by remote data managers.
Conclusion: sustainability and maximizing future value The NPPID effort has established a single unified repository. This has been accomplished by reconciling all available research catalogs and ID nomenclature, and by aggregating all individual identities and encounter data into a state of data readiness unprecedented on a long-term and ocean-basin-scale. The first benefits are cost savings and organizational effectiveness. Particularly in well-sampled areas, data processing is revolutionized by immediate access to a fast and reliable photo-ID system. Collaborators reported that this "saves countless hours of manual visual matching, allowing us to get our data out in products, papers, and outreach more quickly" (JN) and "reduces lab time by 90%" (AS). However, collaborators face the challenge of how to maximize the present and future value of the NPPID dataset.
A primary outstanding need is to create clarity for how researchers efficiently access, establish permission, and create sub-collaborations to develop further studies beyond the term of the NPPID collaboration. System functionality was developed in a constant feedback cycle to accommodate progressively larger datasets through the study. This dataset appears to document most living humpback whales across the North Pacific Ocean basin, creating an abundance of data and inspiring an ambition to monitor populations in near-real time. With heterogeneous sampling effort over the study area, critical data gaps can be identified for understanding abundance and population structure. In addition, minimum sample sizes for reliable, robust population models can be established. Given the low cost of data storage, and if the incremental cost of each additional data point is driven to near zero, there is very little cost to overshooting a threshold of "enough" data.
Having now acquired sufficient baseline data for North Pacific populations in the face of a changing ocean, we aim for data readiness to understand the implications of ecosystem events on a timescale that benefits resource management. This study concentrates on humpback whales of the North Pacific, but the concept and methods can be extended to many species. Baleen whales are recognized to influence marine ecosystems on a massive scale  67. In recognition of the concept of essential biological variables  68, 69  , there is a need for marine observation and data at an ocean-basin-wide scale  [70] [71] [72]. This dataset, the collaboration agreement, and the system established to create and maintain it can contribute to our understanding of essential ocean variables. This study established an extremely cost effective and utilitarian information architecture, delivering an essential service for ongoing studies.
If investment in collaborator engagement, upkeep, development, and data management continue, the future of this collaborative system promises great contributions to the understanding of North Pacific humpback whale populations. Sustainability will require a transition from the centralized efforts of a multi-year study to an established project at a stable institution with community ownership, oversight, and funding. We see this effort not as collecting and possessing a dataset, but as curating a public good for the betterment of science, education, and marine conservation. The FAIR and TRUST principles are central to guiding development, recognizing that accessibility requires more than just a data search feature via a web browser. To truly achieve full potential will require decentralizing data management to research collaborators, a shift that requires further system development, funding, user training, and commitment.