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Raspberry Pi nest cameras – an affordable tool for remote behavioural and conservation monitoring of bird nests
<p><span><span><span><span><span><span><span><span><span><span><span>1. Bespoke (custom-built) Raspberry Pi cameras are increasingly popular research tools in the fields of behavioural ecology and conservation, because of their comparative flexibility in programmable settings, ability to be paired with other sensors, and because they are typically cheaper than commercially built models.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>2. Here we describe a novel, Raspberry Pi-based camera system that is fully portable and yet weatherproof – especially to humidity and salt spray. The camera was paired with a passive infra-red sensor, to create a movement-triggered camera capable of recording videos over a 24-hr period. We describe an example deployment involving "retro-fitting" these cameras into artificial nest boxes on Praia Islet, Azores archipelago, Portugal, to monitor the behaviours and interspecific interactions of two sympatric species of breeding storm-petrel (Monteiro's storm-petrel <i>Hydrobates monteiroi</i> and Madeiran storm-petrel <i>Hydrobates castro</i>) during their chick-rearing periods.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>3. Of the 138 deployments, 70% of all deployments were deemed to be "Successful" (Successful was defined as continuous footage being recorded for more than one hour without an interruption), which equated to 87% of the individual 30 s videos. The bespoke cameras proved to be easily portable between 54 different nests and reasonably weatherproof (~14% of deployments classed as "Partial" or "Failure" deployments were specifically due to the weather/humidity), and we make further trouble-shooting suggestions to mitigate additional weather-related failures.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>4. Here we have shown that this system is fully portable and capable of coping with salt spray and humidity, and consequently the camera-build methods and scripts could be applied easily to many different species that also utilise cavities, burrows, and artificial nests, and can potentially be adapted for other wildlife monitoring situations to provide novel insights into species-specific daily cycles of behaviours and interspecies interactions.</span></span></span></span></span></span></span></span></span></span></span></p>
Results from the monitoring of veterinary medicinal product residues and other substances in live animals and animal products - Bosnia and Herzegovina
<p>This dataset contains the monitoring results of veterinary medicinal product residues and other substances measured in live animals and animal products analysed by the national competent authority of Bosnia and Herzegovina. The presence of unauthorised substances, residues of veterinary medicinal products or chemical contaminants in food may pose a risk factor for public health.</p> <p>For this reason and in order to ensure a high level of consumer protection, a comprehensive legislative framework has been established in the European Union (EU) which defines maximum limits permitted in food and monitoring programmes for the control of the presence of these substances in the food chain. Regulation (EU) No 37/2010 establishes maximum limits for residues of veterinary medicinal products in food-producing animals and animal products. Maximum residue levels for pesticides in or on food and feed of plant and animal origin are laid down in Regulation (EC) No 396/2005. Commission Regulation (EC) 1881/2006 lays down the maximum limits for the presence of certain contaminants in animal products. Council Directive 96/23/EC lays down measures to monitor certain substances and residues thereof, mainly veterinary medicinal products, in live animals and animal products. Additionally, Commission Decision 97/747/EC lays down levels and frequencies of sampling for certain animal products.</p> <p>The chemical monitoring data collected and published by EFSA include the analytical results provided by EU Member States and three pre-accession countries: Bosnia-Herzegovina, Montenegro, North Macedonia and Serbia.</p> <p>The dataset contains the results of laboratory tests from samples taken from bovines, pigs, sheep, goats, horses, poultry, rabbits, farmed game, wild game aquaculture, milk, eggs and honey.</p> <p>Targeted samples are taken with the aim of detecting illegal treatment or controlling compliance with the maximum levels laid down in the relevant legislation. This means that, in their national plans Member States target the groups of animals (species, gender, age) where the probability of finding residues is the highest.</p> <p>Suspect samples are taken as a consequence of i) non-compliant results on samples taken in accordance with the monitoring plan, ii) possession or presence of prohibited substances at any point during manufacture, storage, distribution or sale through the food and feed production chain, or iii) suspicion or evidence of illegal treatment or non-compliance with the withdrawal period for an authorised medicinal veterinary product.</p> <p>Residues of pharmacologically active substances mean active substances, excipients or degradation products and their metabolites, which remain in food.</p> <p>Unauthorised substances or products mean substances or products prohibited under European Union legislation.</p> <p>Non-compliant sample is a sample that has been analysed for the presence of one or more substances and failed to comply with the legal provisions for at least one substance. Thus, a sample can be non-compliant for one or more substances.</p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>VMPR_2020 – Veterinary Office of Bosnia and Herzegovina</p> <p>VMPR_2018 – Veterinary Office of Bosnia and Herzegovina</p>
ATOPE+Breast, Continuous Monitoring of Training Load in Patients with Breast Cancer during Therapeutic Exercise Intervention
<p>The ATOPE+Breast dataset (ATOPE+ for patients with breast cancer) describes the daily status of 23 patients with breast cancer during therapeutic exercise intervention with daily measures of HRV, self-reported wellness, physical activity, and sleep. Besides, the ATOPE+Breast dataset contains information about training sessions, such as intensity recorded, demographic data, treatment details, initial evaluations of quality of life, physical activity levels, previous medical conditions, and risk factors. ATOPE+Breast was recorded using the ATOPE+ mHealth system, whose usability [1] and reliability [2] were successfully validated.</p> <p>References</p> <ul> <li>[1] S. Moreno-Gutierrez et al., “ATOPE+: An mHealth System to Support Personalized Therapeutic Exercise Interventions in Patients With Cancer,” IEEE Access, vol. 9, pp. 16878–16898, 2021, doi: 10.1109/ACCESS.2021.3049398.</li> <li>[2] P. Postigo-Martin et al., “mHealth system (ATOPE+) to support exercise prescription in breast cancer survivors: A validity and reliability, cross-sectional observational study (ATOPE study) (Preprint),” JMIR mHealth and uHealth, Preprint, Jan. 2022. doi: 10.2196/preprints.36733.</li> </ul>
Assessing the Impact of Pest Monitoring Traps on Bombus griseocollis (Hymenoptera: Apidae) Colony Growth and Development
<p>Insect traps use visual and olfactory cues to attract target pests; however, they vary in their specificity and often unintentionally capture non-target beneficial insects (bycatch), including <em>Bombus</em>. Concerns have been expressed that bycatch may contribute to <em>Bombus</em> mortality and the consequential loss of pollination services. Here, we quantified the impact of trap captures on <em>Bombus griseocollis</em> colony growth and development by evaluating the following four treatments: colonies paired with traps, colonies paired with traps and pheromone lures, traps and pheromone lures (but no colonies), and colonies with no trap and no lure. Trap contents were collected biweekly to determine <em>B. griseocollis </em>capture rates. Colony growth and development data were collected weekly by weighing colonies and recording foraging activity. Based on microsatellite polymerase chain reaction (PCR) amplification, three <em>B. griseocollis </em>were collected from released colonies, while the remaining five were residents within the environment. Given the low number of <em>B. griseocollis </em>workers collected, any differences in colony weight change and active foraging were likely not a result of pest monitoring trap captures. However, trap captures could have a greater impact by interfering with functional diversity, colony establishment, and pollination services, emphasizing the need for additional research. Building on this research will provide a more comprehensive view of the impact of pest monitoring traps on <em>Bombus </em>populations, which could minimize risk to pollinator populations and pollination services.</p>
Molecular data from "Between a rock and a dry place: phylogenomics, biogeography, and systematics of ridge-tailed monitors (Squamata: Varanidae: Varanus acanthurus complex)"
<p><strong>Phylogenetic_dataset.csv</strong>: Unfiltered DArTseq data used in phylogenetic analyses. Readable by 'dartR' (Gruber et al. 2018).</p> <p><strong>Population_dataset.csv</strong>: Unfiltered DArTseq data used in population-level analyses. Readable by 'dartR' (Gruber et al. 2018).</p> <p><strong>Reference.csv</strong>: Spreadsheet listing individuals included in molecular analyses. Includes vouchers, species, name of each sample in DArTseq data sets, and GenBank accession numbers (GB) for mitochondrial data. ABTC stands for Australian Biological Tissue Collection; AA and CCM for field numbers of uncatalogued specimens. Other collection acronyms follow Sabaj (2019). We refrain from assigning individuals that were not included in the molecular analyses to any given species.</p>
United Kingdom results from the monitoring of pesticide residues in food
<p>This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits.</p> <p>A sample is considered <strong>free of quantifiable residues</strong> if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy.</p> <p>If a sample <strong>contains quantifiable residues</strong> but within the legally permitted limit (maximum residue level, MRL), it is described as a sample with quantified residue levels within the legal limits (below or at the MRL)</p> <p>A sample is considered <strong>non-compliant</strong> with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.</p> <p> </p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>MOPER_2020 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2019 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2018 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2017 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2016 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2015 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2014 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2013 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2012 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2011 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p> </p> <p><strong>We are seeking feedback on our open data please complete the survey at the link below:<br> https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14</strong></p>
Monitoring demography of resurrected populations of locally extinct and extant species to investigate drivers of species loss
<p>Extinctions are predicted to rise by an order of magnitude over the next century. Although contemporary documented extinctions are uncommon, local extirpations likely provide hints about global extinction risks. Comparing responses to global change of locally extinct versus extant species pairs in a phylogenetic framework could highlight why certain species are more vulnerable to extinction than others and which anthropogenic changes are most relevant to their decline. As anthropogenic changes likely interact to affect population declines, demographic studies partitioning the effects of multifactorial stressors are needed but remain rare. I examine demographic responses to nitrogen addition and deer herbivory, two major drivers of species losses in grasslands, in experimental reintroductions of fourteen locally extinct and extant confamilial native plants from Michigan prairies. Nitrogen consistently reduces survival, especially in locally extinct species, and growth of locally extinct species benefits less from nitrogen than growth of extant species. Nitrogen reduces population growth rates, largely via reductions in survival. Deer herbivory, meanwhile, had inconsistent effects on vital rates among species and did not affect population growth. Nitrogen and herbivory rarely interacted to affect vital rates. These results link community-level patterns of species loss under nitrogen addition to the population-level processes underlying those losses.</p>
MORPHEMIC Application Monitoring Dataset (Genom)
<p>The application component monitoring dataset for the Genom application under the MORPHEMIC project.</p>
Fig. 15 in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 15. Sex ratio in M. daubentonii throughout the year from bats recorded inside Liptsy 1: VIII–V — number of months August to June; n — number of bats.
Fig. 13 in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 13. Bat activity (shown by lines) and relative abundance (shown by bars) during autumn swarming and spring departure periods — an example from Liptsy 1: VIII–V = number of months, August to May; IXs — summer period in September; IXa — autumn period in September; N — number of mist-netting nights; n — total number of bats in each period; Mdau — M. daubentonii; Mdas — M. dasycneme; Paur — P. auritus.
Fig. 14 in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 14. Sex ratio in M. daubentonii and P. auritus in winter in three Liptsy mines (L1, L2 and 3–4): n — number of bats; Mdau — M. daubentonii; Paur — P. auritus.
Fig. 16. Sex ratioin M. daubentonii and P in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 16. Sex ratioin M. daubentonii and P. auritus during autumn swarming and spring departure periods — an example from Liptsy 1 mine: VIII–V — number of months, August to May; n — number of bats; Mdau — M. daubentonii; Paur — P. auritus.
Fig. 10 in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 10. Bat distribution in galleries of different height. An example from Liptsy 1: Mdau — M. daubentonii; Paur — P. auritus.
Fig. 11 in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 11. Bat distribution in crevices or in the open; and on walls or ceilings. An example from Liptsy 1 mine: n — bat number included in each category; Mdau — M. daubentonii; Paur — P. auritus.
Fig. 7 in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 7. Fluctuations in the number of hibernating bats in Liptsy 3–4: n — number of counts; Mdau — M. daubentonii; Mdas — M. dasycneme; Paur — P. auritus. In winter 2004–2005 censuses were not conducted.
Fig. 9 in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 9. Annual fluctuations of bat numbers (shown by lines) and relative abundance (shown by bars) in Liptsy 1: VIII–VI — number of months, August to June; XIa — autumn period in November; XIw — winter period in November; IIIw — winter period in March; IIIs — spring period in March; N — number of counts with bats in each period; n — total number of bats in each period; Mdau — M. daubentoni; Mdas — M. dasycneme; Paur — P. auritus.
Fig. 4 in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 4. Cumulative number of M. daubentonii (MDA) and P. auritus (PAU) in three Liptsy mines (1, 2 and 3–4), for 10 winter seasons (1999–2009) in period of phenological winter: MDA1 (23 counts); PAU1 (17 counts); MDA2 (16 counts); PAU2 (11 counts); MDA3–4 (19 counts); PAU3–4 (7 counts).
Fig. 6 in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 6. Fluctuations in the number of hibernating bats in Liptsy 2: n — number of counts; Mdau — M. daubentonii; Mdas — M. dasycneme; Paur — P. auritus. In winters 2002–2003 and 2004–2005 censuses were not conducted.
Data of microbiological decomposition monitoring of pine coniferous litter in the soils at the Moscow region by the ICP IM method
<p>Данные мониторинга микробиологического разложения хвойного опада сосны в почвах Московской области по методу ICP IM</p> <p>Data of microbiological decomposition monitoring of pine coniferous litter in the soils at the Moscow region by the ICP IM method</p> <p> </p> <p>Исследования проведены на базе двух особо охраняемых природных территориях (далее ООПТ) в Московской области и г. Москве, расположенных на расстоянии 90 км друг от друга. На обеих ООПТ работы проводились на постоянных пробных площадках (ППП) площадью 1 га.</p> <p>Эталон – Приокско-Террасный государственный природных биосферный заповедник. В заповеднике заложены 4 ППП, расположенные в бассейне малой реки Тоденка, большая часть бассейна которой находится в границах Заповедника. ППП расположены не далее 2 км от русла реки, в преобладающих по площади в ООПТ типах лесах: сосняке сложном (две ППП), березняке сложном широкотравном и дубраве широкотравной. В сосняках Заповедника были заложены 2 ППП, различающиеся по увлажнению и месторасположению, на террасе и на коренном берегу.</p> <p>Модельная находится под более высокой антропогенной нагрузкой в г. Москве с лесопарковая часть природно-исторического парка «Кузьминки-Люблино» (далее – Лесопарк), имеющая также схожий с Заповедником рельеф и породный состав лесов. Территории Заповедника и Лесопарка имеют сходство физико-географических условий формирования: обе ООПТ расположены на надпойменных террасах крупных рек Волжского бассейна (рек Оки и Москвы соответственно), на обеих территориях формируются слабо дифференцированные дерново-подзолистые почвы ржавоземы на флювиогляциальных песках (Brunic Arenosols) под сосновыми и березовыми лесами. Основные физико-химические свойства почв Лесопарка соответствуют естественным аналогам.</p> <p><strong>Метод изучения скорости разложения опада.</strong> Методической основой проводимых измерений скорости биоразложения опада является метод подпрограммы «MB Microbial decomposition» программы ICP IM [https://www.syke.fi/en-US/Research__development/Nature/Monitoring/Integrated_Monitoring/Manual_for_Integrated_Monitoring] с некоторыми изменениями.</p> <p>Изменения методики касались срока экспозиции. Мы использовали стандартный срок экспозиции в 1 год и отказались от экспозиции иголок более 1 года, как рекомендовано в методике ICP IM. Такая модификация метода позволила существенно сократить трудозатраты при получении сравнимых результатов.</p> <p>Измерение разложения опада проводились методом закладки конвертов из нейлоновой сетки 8 на 8 см с ячеей 1 мм с упакованными в них пробами на срок 1 год. Конверты запечатывались металлическими скобами. Пробы закладывались и снимались в последней декаде октября – начале ноября. В таблицах и тексте год указывается по году снятия образца, соответственно, пробы, заложенные в 2013 г. и собранные в 2014 г. относятся к 2014 г.</p> <p>Согласно методике, в экспериментах по изучению разложения опада использовались иголки сосны обыкновенной (<em>Pinus</em> <em>sylvestris</em> L.), которые собирались с ветвей невысоких деревьев (10–20 лет) в одном и том же квартале Заповедника и только пожелтевшие, перед их массовым опадом (обычно в начале октября). Предварительно все пробы высушивались до абсолютно сухого веса при 85 <sup>0</sup>С и взвешивались перед упаковкой в конверты. Взвешивание осуществлялось с точностью до 0.001 г, вес проб соснового опада 1 г.</p> <p>Конверты каждый год раскладывались на каждой ППП размерами 100×100 м возле углов и в центре пробных площадей в пределах квадрата 3×3 м в фиксированных точках. Конверты располагались в верхних 5 см почвы, под углом в 15<sup>0</sup>, под моховым покровом (при его наличии) или подстилкой и привязывались леской для удобства поиска. Не допускалось размещение конверта в приствольном круге деревьев. После экспозиции в течение 12 месяцев (350–380 суток) конверты снимались, а содержимое проб аккуратно с использованием пинцета промывалось дистиллированной водой от твердых частиц субстрата и мицелия грибов до остатков иголок и высушивалось до абсолютно сухого веса. Измерение потери массы каждой пробы проводилось методом взвешивания с точностью 0.001 г. Ежегодно закладывалось по 5 проб на каждой ППП в сосняке и березняке, но через год не всегда удавалось найти все заложенные конверты, так как происходили ветровалы и иные нарушения. На ППП в дубняке закладывалось по 10 конвертов.</p>
Data set from long-term wind and acceleration monitoring of the Gjemnessund Bridge
<p>The Gjemnessund Bridge has been monitored by accelerometers and anemometers for almost ten years. The data collected between 2013 and 2018 are now available in this open-access research entry, for free access and download. The data is collected in two h5-files (hierachical data format), with sampling rates 2 Hz and 10 Hz, downsampled from the raw sampling rate of 200 Hz. Some minimal signal processing is applied to the data in line with that applied to the Hardanger Bridge data described in Fenerci et al. (2021) and the Bergsøysund Bridge data described in Kvåle et al. (2022). The structure of the data is identical to that of the latter reference, which is described in a preprint appended to that research entry. The Python package opyndata available on GitHub contains useful tools compatible with the format of the dataset, for data import, processing and visualization.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.