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Active-type piezoelectric smart textiles with antifouling performance for pathogenic control

npj Flexible Electronics 2024年第1期8卷 223-232页

作者： Su Eon Lee Hanna Lee Jang Hwan Kim Jae Chul Park Sooah Kyung Hayoung Choi Su Hyun Baek Jun Hyun Park Sohyun Park Jeong-Min Kim Hye-Jun Jo Seung Hyeon Cho Jiwoong Kim Hojun Kim Seung Ho Han Jun Kyun Oh Bong Hoon Kim Department of Robotics and Mechatronics Engineering DGISTDaegu42988Republic of Korea Department of Polymer Science and Engineering Dankook UniversityYongin16890Republic of Korea Department of Materials Science and Engineering Ajou UniversitySuwon16499Republic of Korea Center for Advanced Biomolecular Recognition KISTSeoul02792Republic of Korea Department of Materials Science and Engineering Soongsil UniversitySeoul06978Republic of Korea Division of Emerging Infectious Diseases KDCACheongju28159Republic of Korea Safety Measurement Institute Korea Research Institute of Standards and Science(KRISS)Daejeon34113Republic of Korea Division of Bio-Medical Science and Technology KIST SchoolUST02792 SeoulRepublic of Korea Electronic Convergence Materials&Devices Research Center Korea Electronics Technology Institute(KETI)13509 SeongnamRepublic of Korea

Recently,an investigation into preventive measures for coronavirus disease 2019(COVID-19)has garnered considerable ***,strategies for the proactive prevention of viral pathogens have also attracted significant interest in the field of wearable devices and electronic textiles research,particularly due to their potential applications in personal protective *** this study,we introduce smart textiles designed with optimized piezoelectric devices that exhibit antifouling performance against microorganisms and actively inactivate *** active-type smart textiles,which incorporate advanced lead zirconate titanate(PZT)ceramics,a stretchable interconnector array,and polymeric fabric,demonstrate effective antifouling capabilities,detaching approximately90%of Escherichia coli and75%of ***,they inactivate viruses,releasing~26.8 ng of N protein from ruptured SARS-CoV-2,using ultrasonic waves within the wearable *** results show that piezoelectric smart textiles significantly reduce the spread of COVID-19 by leveraging the electrical and acoustic properties of PZT ceramics.

关键词： textiles smart piezoelectric

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HRI Wasn’t Built In a Day: A Call To Action For Responsible HRI Research

HRI Wasn’t Built In a Day: A Call To Action For Responsible...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Micol Spitale Rebecca Stower Maria Teresa Parreira Elmira Yadollahi Iolanda Leite Hatice Gunes Department of Computer Science and Technology University of Cambridge UK Department of Electronic Information and Bio-Engineering Politecnico di Milano Italy Division of Robotics Perception and Learning KTH Royal Institute of Technology Information Science Cornell University School of Computing and Communications Lancaster University UK

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

In recent years, the awareness of the academy around responsible research has notably increased. For instance, with advances in machine learning and artificial intelligence, recent efforts have been made to promote ethical, fair, and inclusive AI and robotics. To better understand if and to what extent HRI is incentivizing researchers to engage in responsible research, we conducted an exploratory review of the publishing guidelines for the most popular HRI conference venues. We identified 18 conferences which published at least 7 HRI papers in 2022. From these, we discuss four themes relevant to conducting responsible HRI research in line with the Responsible Research and Innovation framework: ethical and human participant considerations, transparency and reproducibility, accessibility and inclusion, and plagiarism and LLM use. We identify several gaps and room for improvement within HRI regarding responsible research. Finally, we establish a call to action to provoke conversations among HRI researchers about the importance of conducting responsible research within emerging fields like HRI.

关键词： Ethics Technological innovation Reviews Rail to rail inputs Human-robot interaction Oral communication Reproducibility of results Stakeholders Robots Guidelines

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Correction to: Prospective minimally invasive pancreatic resections from the IGOMIPS registry: a snapshot of daily practice in Italy on 1191 between 2019 and 2022

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Updates in surgery 2024年第1期76卷 327-328页

作者： Ugo Boggi Greta Donisi Niccolò Napoli Stefano Partelli Alessandro Esposito Giovanni Ferrari Giovanni Butturini Luca Morelli Mohammad Abu Hilal Massimo Viola Fabrizio Di Benedetto Roberto Troisi Marco Vivarelli Elio Jovine Alessandro Ferrero Umberto Bracale Sergio Alfieri Riccardo Casadei Giorgio Ercolani Luca Moraldi Carlo Molino Raffaele Dalla Valle Giuseppe Ettorre Riccardo Memeo Giacomo Zanus Andrea Belli Salvatore Gruttadauria Alberto Brolese Andrea Coratti Gianluca Garulli Renato Romagnoli Marco Massani Felice Borghi Giulio Belli Roberto Coppola Massimo Falconi Roberto Salvia Alessandro Zerbi Division of General and Transplant Surgery University of Pisa Pisa Italy. u.boggi@med.unipi.it. Pancreatic Surgery Unit Department of Biomedical Sciences Humanitas University Pieve Emanuele Italy. IRCCS Humanitas Research Hospital Rozzano Italy. Division of General and Transplant Surgery University of Pisa Pisa Italy. Pancreatic Surgery Unit Pancreas Translational and Clinical Research Center OSR ENETS Center of Excellence IRCCS San Raffaele Scientific Institute Milan Italy. Vita-Salute San Raffaele University Milan Italy. General and Pancreatic Surgery Unit Pancreas Institute University of Verona Verona Italy. Division of Minimally-Invasive Surgical Oncology ASST Grande Ospedale Metropolitano Niguarda Milan Italy. Department of Surgery Pederzoli Hospital Peschiera Italy. General Surgery Department of Translational Research and New Technologies in Medicine and Surgery University of Pisa Pisa Italy. EndoCAS (Center for Computer Assisted Surgery) University of Pisa Pisa Italy. Department of Surgery Poliambulanza Foundation Hospital Brescia Italy. Department of Surgery Ospedale Card. G. Panico Tricase Italy. Hepato-Pancreato-Biliary Surgery and Liver Transplantation Unit University of Modena and Reggio Emilia Modena Italy. Division of HPB Minimally Invasive and Robotic Surgery Department of Clinical Medicine and Surgery Federico II University Hospital Naples Italy. Hepatobiliary and Abdominal Transplantation Surgery Department of Experimental and Clinical Medicine Riuniti Hospital Polytechnic University of Marche Ancona Italy. Department of General Surgery IRCCS Azienda Ospedaliero-Universitaria di Bologna Maggiore Hospital Bologna Italy. Department of General and Oncological Surgery "Umberto I" Mauriziano Hospital Turin Italy. Department Clinical Medicine and Surgery Federico II University of Naples Via Pansini 5 80131 Naples Italy. Digestive Surgery Fondazione Policlinico Universitario A. Gemelli IRCCS Catholic University Rome Italy. Division

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Direct Surface Modification of the Epidermis Using Mussel-Inspired Polydopamine with Multiple Anti-biofouling Functions

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Advanced healthcare materials 2025年 e2500597页

作者： Su Eon Lee Hanna Lee Jang Hwan Kim Sungwook Park Young Taek Oh Hayoung Choi Jeong-Min Kim Hye-Jun Jo Jun Hyun Park Ho Jun Jin Kwan Hyi Lee Seung Ho Han Hojun Kim Jun Kyun Oh Bong Hoon Kim Department of Robotics and Mechatronics Engineering DGIST Daegu 42988 Republic of Korea. Department of Polymer Science and Engineering Dankook University Yongin 16890 Republic of Korea. Department of Materials Science and Engineering Ajou University Suwon 16499 Republic of Korea. Department of Energy Systems Research Ajou University Suwon 16499 Republic of Korea. Center for Advanced Biomolecular Recognition Biomedical Research Division Korea Institute of Science and Technology (KIST) Seoul 02792 Republic of Korea. Department of Materials Science and Engineering Soongsil University Seoul 06978 Republic of Korea. Division of Emerging Infectious Diseases KDCA Cheongju 28159 Republic of Korea. KU-KIST Graduate School of Converging Science and Technology Korea University Seoul 02481 Republic of Korea. Electronic Convergence Materials & Devices Research Center Korea Electronics Technology Institute (KETI) Seongnam 13509 Republic of Korea. Division of Bio-Medical Science and Technology KIST School UST Seoul 02792 Republic of Korea.

The surface properties of the epidermis are crucial in pathogen adhesion and proliferation. Moreover, damage to the epidermis caused by various physical and chemical attacks provides a favorable environment for pathogen penetration and proliferation through the exposed internal living tissue. Surface modification of the epidermis to impart anti-biofouling properties can provide effective protection against infections. In this study, a facile method of imparting multiple anti-biofouling functions by directly modifying the epidermal surface of an organism using dopamine, which is a mussel-inspired substance, is introduced. biocompatible polydopamine (PDA) is uniformly applied to organic surfaces with diverse morphological features and surface energies, indicating its versatility. In addition, the reliability of epidermal modification with PDA is confirmed via the PDA-induced prevention of chronic changes in the impedance of the epidermis. Critically, the PDA-modified epidermis exhibited various anti-biofouling functions, including antibacterial and anti-adsorption properties against bacteria and cellular/noncellular microorganisms, respectively. Improved antibacterial properties are successfully realized via integration with tobramycin, which is a representative antibiotic. Direct surface modification using PDA offers an innovative approach to safeguard biological surfaces, particularly the human epidermis, against various pathogens, with potential for application in medical patches and skin-attached devices.

关键词： antibacterial antiviral direct coating polydopamine coatings surface modifications

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WE-173. Combining excitability and connectivity measures from interictal invasive-EEG estimates the epileptogenic zone, even in the absence of frank epileptiform activity

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Clinical Neurophysiology 2022年 141卷 S81-S82页

作者： Billardello, Roberto Ntolkeras, Georgios Pesce, Matthew Harmon, Joseph Hadjinicolaou, Aristides Madsen, Joseph Taffoni, Fabrizio Pearl, Phillip Papadelis, Christos Grant, Ellen Tamilia, Eleonora Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Children’s Hospital Harvard Medical School Boston USA Research Unit of Advanced Robotics and Human-Centred Technologies Department of Engineering University Bio-Medico Campus of Rome Rome Italy Baystate Children's Hospital Springfield USA Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children’s Hospital Harvard Medical School Boston USA Epilepsy Surgery Program Department of Neurosurgery Boston Children’s Hospital Harvard Medical School Boston USA Jane and John Justin Neurosciences Center Cook Children’s Health Care System Fort Worth USA Department of Bioengineering University of Texas at Arlington Arlington USA

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WE-184. From “irritative zone” to “irritative network”: a novel interictal estimate of the epileptogenic zone using invasive EEG

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Clinical Neurophysiology 2022年 141卷 S86-S86页

作者： Pesce, Matthew Billardello, Roberto Pace, Lavinia Madsen, Joseph Pearl, Phillip Papadelis, Christos Grant, Ellen Tamilia, Eleonora Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Children's Hospital Boston USA Research Unit of Advanced Robotics and Human-Centered Technologies Department of Engineering University Bio-Medico Campus of Rome Rome Italy Epilepsy Surgery Program Department of Neurosurgery Boston Children’s Hospital Boston USA Harvard Medical School Boston USA Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children’s Hospital Boston USA Jane and John Justin Neurosciences Center Cook Children’s Health Care System Fort Worth USA Department of Bioengineering University of Texas at Arlington Arlington USA Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Children’s Hospital Boston USA

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MetaSleepLearner: A pilot study on fast adaptation of bio-signals-based sleep stage classifier to new individual subject using Meta-Learning

arXiv

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arXiv 2020年

作者： Banluesombatkul, Nannapas Ouppaphan, Pichayoot Leelaarporn, Pitshaporn Lakhan, Payongkit Chaitusaney, Busarakum Jaimchariyatam, Nattapong Chuangsuwanich, Ekapol Chen, Wei Phan, Huy Dilokthanakul, Nat Wilaiprasitporn, Theerawit Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand Department of Otolaryngology Faculty of Medicine Chulalongkorn University Thailand Excellence Center for Sleep Disorders King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand Division of Pulmonary and Critical Care Medicine Faculty of Medicine Chulalongkorn University Thailand Head of Excellence Center for Sleep Disorders King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand The Computer Engineering Department Chulalongkorn University Bangkok Thailand The Center for Intelligent Medical Electronics Department of Electronic Engineering School of Information Science and Technology Fudan University Shanghai200433 China The School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the automatic sleep stage classification conundrum. However, the difficulties can be posed in replacing the clinicians with the automatic system due to the differences in many aspects found in individual bio-signals, causing the inconsistency in the performance of the model on every incoming individual. Thus, we aim to explore the feasibility of using a novel approach, capable of assisting the clinicians and lessening the workload. We propose the transfer learning framework, entitled MetaSleepLearner, based on Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subjects (source code is available at https://***/IoBTVISTEC/MetaSleepLearner). The framework was demonstrated to require the labelling of only a few sleep epochs by the clinicians and allow the remainder to be handled by the system. Layer-wise Relevance Propagation (LRP) was also applied to understand the learning course of our approach. In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 5.4% to 17.7% improvement with statistical difference in the mean of both approaches. The illustration of the model interpretation after the adaptation to each subject also confirmed that the performance was directed towards reasonable learning. MetaSleepLearner outperformed the conventional approaches as a result from the fine-tuning using the recordings of both healthy subjects and patients. This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording. © 2020, CC BY-N

关键词： Convolutional neural networks

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Author Correction: Rules for the Leg Coordination of Dung Beetle Ball Rolling Behaviour

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Scientific reports 2020年第1期10卷 18182页

作者： Binggwong Leung Nienke Bijma Emily Baird Marie Dacke Stanislav Gorb Poramate Manoonpong Bio-Inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Rayong 21210 Thailand. binggwong.l_s17@vistec.ac.th. Functional Morphology and Biomechanics Zoological Institute Kiel University Kiel 24118 Germany. Division of Functional Morphology Department of Zoology Stockholm University Stockholm SE 10691 Sweden. Department of Biology Lund Vision Group Lund University Sölvegatan 35 223 62 Lund Sweden. Bio-Inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Rayong 21210 Thailand. poramate.m@vistec.ac.th. Embodied AI and Neurorobotics Lab SDU Biorobotics The Mærsk Mc-Kinney Møller Institute The University of Southern Denmark Odense 5230 Denmark. poramate.m@vistec.ac.th.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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bioTIME 2.0: Expanding and Improving a Database of biodiversity Time Series

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Global Ecology and biogeography 2025年第5期34卷

作者： Maria Dornelas Laura H. Antão Amanda E. Bates Viviana Brambilla Jonathan M. Chase Cher F. Y. Chow Ada Fontrodona-Eslava Anne E. Magurran Inês S. Martins Faye Moyes Alban Sagouis Samuel Adu-Acheampong Daniel Acquah-Lamptey Dušan Adam Penelope A. Ajani Aitor Albaina Pablo Almaraz Jeongseop An Roger Sigismund Anderson Madelaine Jean Robertson Anderson Alexsander Z. Antunes Ivan Arismendi Linda Armbrecht Pedro Aros-Mardones Sreejith Kalpuzha Ashtamoorthy Narayanan Ayyappan Gal Badihi Joseph J. Bailey Andrew H. Baird Mark Edward Baird Sreekumar Vadakkethil Balakrishnan José António L. Barão-Nóbrega Adi Barash Miguel Barbosa Jos Barlow Claus Bässler Matthieu Beaumont Natalie Beenaerts Tiago Octavio Begot Wallace Beiroz Ricardo Beldade David M. Bell Alecia Bellgrove Jonathan Belmaker Lisandro Benedetti-Cecchi Cassandra E. Benkwitt Pamela Medina-van Berkum Brandon T. Bestelmeyer Matthew G. Betts Maxwell Kelvin Billah Anne D. Bjorkman Magdalena Błażewicz Christopher P. Bloch Shane A. Blowes Antonio Bode Juliano A. Bogoni Thomas Bolger Timothy C. Bonebrake Erik Bonsdorff Roberta Bottarin Luke N. Brokensha Rob W. Brooker Andrew J. Brooks Helge Bruelheide Thiago Almeida Bueno Claire Laguionie Mariana Lopes Campagnoli James Cant Erica Pellegrini Caramaschi Alexandre Caron Tadhg Carroll Tancredi Caruso Juan Carvajal-Quintero Giuseppe Castaldelli Edward Castañeda-Moya Pedro V. Castilho Sonia Zanini Cechin Shahar Chaikin Uchangi Manjunatha Chandrashekara Tory J. Chase Chaolun Allen Chen Jorge José Cherem Sei-Woong Choi Erica M. Christensen Alexander V. Christianini Jackson Wing Four Chu Peter Coad Carl Van Colen Lise Comte Elisabeth J. Cooper J. Hans C. Cornelissen Eddy Cosson Unai Cotano Luc Crevecoeur Shannan Kyle Crow Graeme S. Cumming Vanessa S. Daga Gabriella Damasceno Gergana N. Daskalova Claire H. Davies Robert A. Davis Frank P. Day Sussy De-La-Zerda Amy Elizabeth Deacon Indradatta de Castro-Arrazola Steven Degraer Kharran Deonarinesingh Juan C. Diaz-Ricaurte Christopher R. Dickman Tara Dirilgen Ciaran John Dolan J. Emmet Centre for Biological Diversity School of Biology University of St Andrews Fife UK MARE—Centro de Ciências do Mar e do Ambiente Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Research Centre for Ecological Change Organismal and Evolutionary Biology Research Programme Faculty of Biological and Environmental Sciences University of Helsinki Helsinki Finland Department of Biology University of Victoria Victoria British Columbia Canada German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Leipzig Germany Department of Computer Science Martin Luther University Halle-Wittenberg Halle (Saale) Germany Leverhulme Centre for Anthropocene Biodiversity Department of Biology University of York York UK Department of Animal Biology and Conservation Science University of Ghana Accra Ghana Philipps Universität Marburg Marburg Germany Department of Forest Ecology Silva Tarouca Research Institute Brno Czech Republic School of Life Sciences University of Technology Sydney Sydney New South Wales Australia Zoology and Animal Cell Biology Department University of the Basque Country (UPV/EHU) Leioa Spain Grupo de Oceanografía de Ecosistemas Instituto de Ciencias Marinas de Andalucía CSIC Campus Universitario de Puerto Real Puerto Real Spain National Institute of Ecology Seocheon Republic of Korea African Regional Postgraduate Programme in Insect Science University of Ghana Accra Ghana Département de Biologie Université de Sherbrooke Sherbrooke Quebec Canada Núcleo de Conservação da Biodiversidade Instituto de Pesquisas Ambientais Secretaria de Meio Ambiente Infraestrutura e Logística do Estado de São Paulo São Paulo Brazil Department of Fisheries Wildlife and Conservation Sciences Oregon State University Corvallis Oregon USA Institute for Marine and Antarctic Studies Ecology & Biodiversity Centre University of Tasmania Hobart Tasmania Australia Graduate Program in Oceanography Department of Oceanography Faculty of Natural Scienc

Motivation Here, we make available a second version of the bioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (bioTIMEr) to facilitate use of the database. Main Types of Variables Included The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain Sampling locations in bioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain The earliest time series in bioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format csv and. SQL.

关键词： assemblage biodiversity community database ecoinformatics species taxa time

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