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Supplementary material 3 from: Osman OA, Andersson J, Martin-Sanchez PM, Eiler A (2022) National eDNA-based monitoring of Batrachochytrium dendrobatidis and amphibian species in Norway. Metabarcoding and Metagenomics 6: e85199. https://doi.org/10.3897/mbmg.6.85199
Table S3, Figures S1, S2
Non-invasive monitoring of multiple wildlife health factors by fecal microbiome analysis
<p>Fecal microbial biomarkers represent a less invasive alternative for acquiring information on wildlife populations than many traditional sampling methodologies. Our goal was to evaluate linkages between fecal microbiome communities in Rocky Mountain elk (<i>Cervus canadensis</i>) and four host factors including sex, age, population, and physical condition (body-fat). We paired a feature-selection algorithm with an LDA-classifier trained on elk differential bacterial abundance (16S-rRNA amplicon survey) to predict host health factors from 104 elk microbiomes across four elk populations. We validated the accuracy of the various classifier predictions with leave-one-out cross-validation using known measurements. We demonstrate that the elk fecal microbiome can predict the four host factors tested. Our results show that elk microbiomes respond to both the strong extrinsic factor of biogeography and simultaneously occurring, but more subtle, intrinsic forces of individual body-fat, sex, and age class. Thus, we have developed and described herein a generalizable approach to disentangle microbiome responses attributed to multiple host factors of varying strength from the same bacterial sequence data set. Wildlife conservation and management presents many challenges, but we demonstrate that non-invasive microbiome surveys from scat samples can provide alternative options for wildlife population monitoring. We believe that, with further validation, this method could be broadly applicable in other species and potentially predict other measurements. Our study can help guide the future development of microbiome-based monitoring of wildlife populations and supports hypothetical expectations found in host-microbiome theory.</p>
Raw Data for the article: Local diagnostic reference levels for pediatric retrograde wedge portography interventional procedures using a dose monitoring software at a transplantation institute
<p>The aim of this work was to establish local diagnostic reference levels (DRLs) for retrograde wedge portography (RWP) performed on pediatric patients assessing the usefulness of radiation dose monitoring software in the establishing process. Between September 2016 and April 2020, 66 consecutive RWP were performed at a transplantation institute and were included in our study. Patients were divided in three groups according to age: n = 25 infants, n = 20 middle childhood and n = 21 early adolescence. The third quartile of both Air Kerma at the reference point (Ka,r) and air kerma-area product (PKA) were evaluated to establish local DRLs (lDRLs). In addition, to control high Ka,r levels during procedures, the software notified to operators if Ka,r exceeded the dose 'alert' threshold set at 2 Gy. lDRLs were established for all three groups using PKA and Ka,r: infant group: 5.6 Gy.cm 2 and 0.034 Gy; middle childhood: 6.4 Gy.cm2 and 0.018 Gy and early adolescence: 12.8 Gy.cm2 and 0.059 Gy. The dose threshold 'alert' was never encountered (alert quota: 0%). The dose monitoring system supports the feasibility of accurate and easier lDRLs' establishment.</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2020/2021 – Greece
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection: Ministry of Rural Development and Food</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2020/2021 – Czechia
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods, as well as the voluntary reporting of and Whole Genome Sequencing (WGS) data are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection: State Veterinary Administration</p>
Assessment of acoustic indices for monitoring phylogenetic and temporal patterns of biodiversity in tropical forests
<b>Description: </b><p>Acostic recordings of bird calls at SAFE in 2014. Four solar-powered Wildlife Song Meter SM3 bioacoustic recorders (Wildlife Acoustics Inc., Concord,<br>MA, USA) were deployed with omni-directional microphones (sensitivity: 20Hz - 20kHz). No exact locations recorded.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/175"><b>Continuous bio-acoustic monitoring (2020 extension)</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=7740620">here</a></p><p><b>Files: </b>This dataset consists of 2 files: Trigg_safedata.xlsx, CLIPS.zip</p><p><b>Trigg_safedata.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Data</b> (described in worksheet Data)</p><p>Description: excel sheet with clips listed and birds identified</p><p>Number of fields: 7</p><p>Number of data rows: 837</p><p>Fields: </p><ul><li><b>FileNumber</b>: The audio files, which are labelled 1 to 120 (Field type: id)</li><li><b>StartTime</b>: approximate time when the species call began. (Field type: numeric)</li><li><b>EndTime</b>: approximate end time of the call. (Field type: numeric)</li><li><b>original_taxon_names</b>: species that can be heard in the recording. Original version as entered in to worksheet (Field type: comments)</li><li><b>Species</b>: species that can be heard in the recording. Corrected name, spelling mistakes and excess spaces removed etc. (Field type: taxa)</li><li><b>CallType</b>: Type of sound being identified (Field type: categorical)</li><li><b>IdCertainty</b>: Degree of certainty (Field type: categorical)</li></ul></li></ol><p><b>CLIPS.zip</b></p><p>Description: Zip file containing original audio clips used to generate these data</p><p><b>Date range: </b>2015-04-01 to 2015-06-30</p><p><b>Latitudinal extent: </b>4.6000 to 4.8000</p><p><b>Longitudinal extent: </b>117.5000 to 117.7000</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Primates <br> -  -  -  -  -  Hylobatidae <br> -  -  -  -  -  -  <i>Hylobates</i> <br> -  -  -  -  -  -  -  <i>Hylobates funereus</i> <br> -  -  -  Aves <br> -  -  -  -  Galliformes <br> -  -  -  -  -  Phasianidae <br> -  -  -  -  -  -  <i>Argusianus</i> <br> -  -  -  -  -  -  -  <i>Argusianus argus</i> <br> -  -  -  -  Bucerotiformes <br> -  -  -  -  -  Bucerotidae <br> -  -  -  -  -  -  <i>Anorrhinus</i> <br> -  -  -  -  -  -  -  <i>Anorrhinus galeritus</i> <br> -  -  -  -  -  -  <i>Buceros</i> <br> -  -  -  -  -  -  -  <i>Buceros rhinoceros</i> <br> -  -  -  -  -  -  <i>Rhinoplax</i> <br> -  -  -  -  -  -  -  <i>Rhinoplax vigil</i> <br> -  -  -  -  Psittaciformes <br> -  -  -  -  -  Psittacidae <br> -  -  -  -  -  -  <i>Loriculus</i> <br> -  -  -  -  -  -  -  <i>Loriculus galgulus</i> <br> -  -  -  -  Passeriformes <br> -  -  -  -  -  Cisticolidae <br> -  -  -  -  -  -  <i>Orthotomus</i> <br> -  -  -  -  -  -  -  <i>Orthotomus ruficeps</i> <br> -  -  -  -  -  -  -  <i>Orthotomus atrogularis</i> <br> -  -  -  -  -  -  -  <i>Orthotomus sericeus</i> <br> -  -  -  -  -  -  <i>Prinia</i> <br> -  -  -  -  -  -  -  <i>Prinia flaviventris</i> <br> -  -  -  -  -  Chloropseidae <br> -  -  -  -  -  -  <i>Chloropsis</i> <br> -  -  -  -  -  -  -  <i>Chloropsis cyanopogon</i> <br> -  -  -  -  -  Aegithinidae <br> -  -  -  -  -  -  <i>Aegithina</i> <br> -  -  -  -  -  -  -  <i>Aegithina viridissima</i> <br> -  -  -  -  -  Pycnonotidae <br> -  -  -  -  -  -  <i>Pycnonotus</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus atriceps</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus simplex</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus erythropthalmos</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus goiavier</i> <br> -  -  -  -  -  -  <i>Alophoixus</i> <br> -  -  -  -  -  -  -  <i>Alophoixus bres</i> <br> -  -  -  -  -  -  <i>Iole</i> <br> -  -  -  -  -  -  -  <i>Iole olivacea</i> <br> -  -  -  -  -  Dicaeidae <br> -  -  -  -  -  -  <i>Prionochilus</i> <br> -  -  -  -  -  -  -  <i>Prionochilus xanthopygius</i> <br> -  -  -  -  -  -  <i>Dicaeum</i> <br> -  -  -  -  -  -  -  <i>Dicaeum trigonostigma</i> <br> -  -  -  -  -  Monarchidae <br> -  -  -  -  -  -  <i>Hypothymis</i> <br> -  -  -  -  -  -  -  <i>Hypothymis azurea</i> <br> -  -  -  -  -  -  <i>Rhipidura</i> <br> -  -  -  -  -  -  -  <i>Rhipidura javanica</i> <br> -  -  -  -  -  Irenidae <br> -  -  -  -  -  -  <i>Irena</i> <br> -  -  -  -  -  -  -  <i>Irena puella</i> <br> -  -  -  -  -  Muscicapidae <br> -  -  -  -  -  -  <i>Trichixos</i> <br> -  -  -  -  -  -  -  <i>Trichixos pyrropygus</i> <br> -  -  -  -  -  Tephrodornithidae <br> -  -  -  -  -  -  <i>Hemipus</i> <br> -  -  -  -  -  -  -  <i>Hemipus picatus</i> <br> -  -  -  -  -  -  -  <i>Hemipus hirundinaceus</i> <br> -  -  -  -  -  Pellorneidae <br> -  -  -  -  -  -  <i>Kenopia</i> <br> -  -  -  -  -  -  -  <i>Kenopia striata</i> <br> -  -  -  -  -  -  <i>Pellorneum</i> <br> -  -  -  -  -  -  -  <i>Pellorneum capistratum</i> <br> -  -  -  -  -  -  -  <i>Pellorneum bicolor</i> <br> -  -  -  -  -  -  <i>Malacopteron</i> <br> -  -  -  -  -  -  -  <i>Malacopteron affine</i> <br> -  -  -  -  -  -  <i>Malacocincla</i> <br> -  -  -  -  -  -  -  <i>Malacocincla malaccensis</i> <br> -  -  -  -  -  -  <i>Alcippe</i> <br> -  -  -  -  -  -  -  <i>Alcippe brunneicauda</i> <br> -  -  -  -  -  Corvidae <br> -  -  -  -  -  -  <i>Corvus</i> <br> -  -  -  -  -  -  -  <i>Corvus enca</i> <br> -  -  -  -  -  Acanthizidae <br> -  -  -  -  -  -  <i>Gerygone</i> <br> -  -  -  -  -  -  -  <i>Gerygone sulphurea</i> <br> -  -  -  -  -  Timaliidae <br> -  -  -  -  -  -  <i>Cyanoderma</i> <br> -  -  -  -  -  -  -  <i>Cyanoderma erythropterum</i> <br> -  -  -  -  -  -  -  <i>Cyanoderma rufifrons</i> <br> -  -  -  -  -  -  <i>Mixornis</i> <br> -  -  -  -  -  -  -  <i>Mixornis bornensis</i> <br> -  -  -  -  -  -  <i>Macronus</i> <br> -  -  -  -  -  -  -  <i>Macronus ptilosus</i> <br> -  -  -  -  -  -  <i>Stachyris</i> <br> -  -  -  -  -  -  -  <i>Stachyris maculata</i> <br> -  -  -  -  -  -  -  <i>Stachyris poliocephala</i> <br> -  -  -  -  -  -  <i>Pomatorhinus</i> <br> -  -  -  -  -  -  -  <i>Pomatorhinus montanus</i> <br> -  -  -  -  -  Nectariniidae <br> -  -  -  -  -  -  <i>Anthreptes</i> <br> -  -  -  -  -  -  -  <i>Anthreptes simplex</i> <br> -  -  -  -  -  -  <i>Arachnothera</i> <br> -  -  -  -  -  -  -  <i>Arachnothera longirostra</i> <br> -  -  -  -  -  -  -  <i>Arachnothera affinis</i> <br> -  -  -  -  -  Eurylaimidae <br> -  -  -  -  -  -  <i>Eurylaimus</i> <br> -  -  -  -  -  -  -  <i>Eurylaimus ochromalus</i> <br> -  -  -  -  Piciformes <br> -  -  -  -  -  Ramphastidae <br> -  -  -  -  -  -  <i>Megalaima</i> <br> -  -  -  -  -  -  -  <i>Megalaima australis</i> <br> -  -  -  -  -  -  -  -  <i>Megalaima australis duvaucelii</i> (as homotypic_synonym: <i>Psilopogon duvaucelii</i>)<br> -  -  -  -  -  Picidae <br> -  -  -  -  -  -  <i>Meiglyptes</i> <br> -  -  -  -  -  -  -  <i>Meiglyptes tukki</i> <br> -  -  -  -  -  -  <i>Dinopium</i> <br> -  -  -  -  -  -  -  <i>Dinopium rafflesii</i> <br> -  -  -  -  -  -  <i>Blythipicus</i> <br> -  -  -  -  -  -  -  <i>Blythipicus rubiginosus</i> <br> -  -  -  -  -  -  <i>Mulleripicus</i> <br> -  -  -  -  -  -  -  <i>Mulleripicus pulverulentus</i> <br> -  -  -  -  Cuculiformes <br> -  -  -  -  -  Cuculidae <br> -  -  -  -  -  -  <i>Cacomantis</i> <br> -  -  -  -  -  -  -  <i>Cacomantis sonneratii</i> <br> -  -  -  -  -  -  -  <i>Cacomantis merulinus</i> <br> -  -  -  -  -  -  <i>Rhinortha</i> <br> -  -  -  -  -  -  -  <i>Rhinortha chlorophaea</i> <br> -  -  -  -  -  -  <i>Cuculus</i> <br> -  -  -  -  -  -  -  <i>Cuculus vagans</i> <br> -  -  -  -  -  -  <i>Centropus</i> <br> -  -  -  -  -  -  -  <i>Centropus sinensis</i> <br> -  -  -  -  Columbiformes <br> -  -  -  -  -  Columbidae <br> -  -  -  -  -  -  <i>Chalcophaps</i> <br> -  -  -  -  -  -  -  <i>Chalcophaps indica</i> <br></div><p></p>
Area wide monitoring of plant and honey bee (Apis mellifera) viruses in blueberry (Vaccinium corymbosum) agroecosystems facilitated by honey bee pollination
<p>Metadata associated with plant and bee virus identification on blueberry farms</p>
Supporting data for Comparison of Oxford Nanopore and Illumina sequencing for SARS-CoV-2 variant monitoring in wastewater
<p>This dataset contains the FASTQ files used in the paper <em>Comparison of Oxford Nanopore and Illumina sequencing for SARS-CoV-2 variant monitoring in wastewater</em>. </p> <p>The FASTQ files are classified in 2 different folders: MinION and MiSeq. Inside the MinION folder, there are two more folders, one for the R9.4.1 flow cell data and the other for the R10.4.1 flow cell data. </p> <p>Twist synthetic RNA mixtures are named as mix1, mix2, etc. </p> <p>Twist synthetic RNA corresponding to the Wuhan sequence is named as mix11_WH</p> <p>Wastewater samples are named as WWTP_1, WWTP_2, etc. </p>
Source code and data files for the acoustic sensor-based wearable fetal movement monitor
<p>This repository contains data files and source code for the acoustic sensor-based wearable fetal movement monitor.</p>
Results of Common Loon territory monitoring (1979–2009) and of chick monitoring surveys (2004–2006) at Voyageurs National Park
<p>Recovering species are not returning to the same environments or communities from which they disappeared. Conservation researchers and practitioners are thus faced with additional challenges in ensuring species resilience in these rapidly changing ecosystems. Assessing the resilience of species in these novel systems can still be guided by species' ecology, including knowledge of their population size, life history traits, and behavioral adaptations, as well as the type, strength, and number of ways that they interact with other species in the community. We summarized broad trends of Common Loons (<em>Gavia</em> <em>immer</em>) breeding at Voyageurs National Park during 1973–2009 and evaluated the effects of increased risk from recovering Bald Eagles (<em>Haliaeetus</em> <em>leucocephalus</em>) on chick survival during 2004–2006. Adult Common Loons appear to have increased over time. Using Bayesian survival models that accounted for imperfect detection of unmarked individuals, we determined that chick survival of Common Loons was high from year to year and was unrelated to predation risk from Bald Eagles as chicks in territories closer to active nests did not experience greater mortality than those further away. We suggest that Common Loon chicks were unaffected by the recovery of this top predator during the three years of sampling. Previous research indicates that Bald Eagles and other predators are an important source of egg losses, but Common Loons can compensate by re–nesting. Despite current uncertainties from anthropogenic threats, knowledge of a species' ecology remains instrumental in determining their resilience during recovery. </p>
Figure 6 in Two new species of monitor lizards (Squamata: Varanus) endemic to the Louisiade and Tanimbar Archipelagos with a key to the subgenus Euprepiosaurus
Figure 6. Paratype (ZMUT Sa201) of Varanus louisiadensis sp. nov. on Rossell Island (photo VW).
Figure 5 in Two new species of monitor lizards (Squamata: Varanus) endemic to the Louisiade and Tanimbar Archipelagos with a key to the subgenus Euprepiosaurus
Figure 5. Paratype (ZMUT Sa199) of Varanus louisiadensis sp. nov. on Sudest Island (photo VW).
Figure 8 in Two new species of monitor lizards (Squamata: Varanus) endemic to the Louisiade and Tanimbar Archipelagos with a key to the subgenus Euprepiosaurus
Figure 8. Dorsal, ventral and head profile of the holotype (WAM112255) of Varanus tanimbar sp. nov.
Figure 4 in Two new species of monitor lizards (Squamata: Varanus) endemic to the Louisiade and Tanimbar Archipelagos with a key to the subgenus Euprepiosaurus
Figure 4. The holotype (ZMUT Sa197) of Varanus louisiadensis sp. nov. on Misima Island (photo VW).
Figure 9 in Two new species of monitor lizards (Squamata: Varanus) endemic to the Louisiade and Tanimbar Archipelagos with a key to the subgenus Euprepiosaurus
Figure 9. Dorsal and ventral aspect of paratype WAM 109940 of Varanus tanimbar sp. nov.
Identification and characterization of Western Province reef fisheries; Developing management and conservation protocols using traditional ecological knowledge, underwater monitoring and catch assessments
<p>Solomon Islands has highly biodiverse coral reef ecosystems critically important to cultural identity and socio-economic security. Because Solomon Islanders maintain among the highest global fish consumption rates largely from inshore fisheries valued at USD$65 million yr<sup>-1</sup>, maintaining these resources is critical to livelihoods and health. Among these resources are coral reef fish that form (fish) spawning aggregations (FSA). For these species, FSAs are the primary means of population replenishment and form the basis of a trophic web ranging from detritivores to marine megafauna. FSAs are highly vulnerable to overfishing and in Solomon Islands have experienced increasing fishing pressure from a changing economy and a domestic export fishery. Recent estimates show Western Province exporting at least 6 mt mo<sup>-1</sup> to Honiara markets, with an unknown but substantial quantity of this supply derived from FSAs. In Solomon Islands, marine tenure remains a core element of coral reef management, but its effectiveness relies on community awareness and participation in monitoring. The project seeks to improve site-based and national scale enabling conditions for FSA management through: (1) evaluating FSA sites known to be experiencing fishing; (2) providing awareness presentations to traditional reef owners; (3) training local communities and dive operator staff in developing appropriate monitoring protocols; (3) implementing these protocols at known FSA sites; (4) co-developing EAFM plans with traditional reef owners and dive operators; (5) providing information sharing from successful regional LMMAs; (6) working to improve enforcement of existing FSA regulations and local and provincial scales; and (7) building consensus to adapt existing regulations to improve management effectiveness.</p>
Experimental video of integrated human tracking and vital sign monitoring
<p>Experimental video of integrated human tracking and vital sign monitoring.</p>
Deuterium Magnetic Resonance Imaging Using Deuterated Water-Induced 2H-Tissue Labeling Allows Monitoring Cancer Treatment at Clinical Field Strength
<p>Raw 2H-NMR Data of deuterium content in tumor tissues. </p>
Dataset for publication "In Situ Monitoring of the Al(110)-[EMImCl]:AlCl3 Interface by Reflection Anisotropy Spectroscopy"
<p>Dataset are obtained from Reflection anisotropy spectroscopy, CV, SEM/EDX, and computaitonal RAS</p><p>The data are sorted according to the Figures of the paper.</p><p>Abstract of the paper:</p><p>Recently, Al-batteries (AlBs) have become promising candidates for post-lithium batteries, with [EMImCl]:AlCl3 (1:1.5) as the most commonly used electrolyte. However, progress in the development of AlBs is currently hindered by the lack of understanding of its solid-electrolyte interface. Monitoring the structure of this interface under operational conditions by complementary spectroscopy could help to identify and overcome bottlenecks of the system. Reflection anisotropy spectroscopy (RAS), an optical in situ technique, provides access to physical and chemical properties of electrochemical interfaces on an atomistic level. Herein, we report the first example of RAS as an in situ characterization technique for non-aqueous battery systems, investigating an Al(110)-based model system. During chemical pre-treatment in [EMImCl]:AlCl3 , the Al(110) surface passivation film is modified. The oxide film is partially etched while an inhomogeneous passivation layer forms, increasing the surface roughness. Upon electrochemical cycling, applied potential-dependent oscillations of the anisotropy are observed and demonstrate the applicability of RAS to monitor phenomena such as plating/stripping and surface passivation in real-time.</p>
Figure 1 in Long term monitoring of coral and fish assemblages (1983-2014) in Tiahura reefs, Moorea, French Polynesia
Figure 1. - Location of Tiahura sector in Moorea and layout of the sampling sites (quadrats of 50 × 2 m) in the three habitats (fringing and barrier reefs, outer slope).
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.