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Life History of a Climax Forest in Pisgah State Forest in Winchester NH 1929-1930
Old-growth forest is uncommon across the northeastern United States, as most areas have been historically cleared for agriculture or harvested for timber. This study provides rare direct insight into the overstory and midstory dynamics across a semi-contiguous old-growth landscape in New England. Pisgah State Park in southwestern New Hampshire comprises 5300 ha of Hardwoods-Hemlock-White Pine forest, all but 300 ha of which was cutover by the 1880s. To protect a high-quality, old-growth stand from harvest, Harvard Forest purchased a 10 ha tract (the Harvard Tract) in 1927. In 1929 and 1930, Branch, Daley, and Lotti located and sampled all of the known remaining old-growth stands in the Pisgah area. This included 74 0.04 ha old-growth stands, 14 of which were located the Harvard Tract. They also surveyed 27 0.04 ha stands that had been cut just prior to the study (stump plots), where stumps as well as the remaining overstory trees were recorded. Note that only 61 old-growth plots and 23 stump plots have valid measurements. Species, diameter class, and position (overstory or midstory) were recorded for each tree; cover type, elevation, and location was described for each plot. Dead and downed trees were also recorded.
Monitoring O. mykiss Life Stages on the Stanislaus River 2021-2025
This study is designed to partly address steelhead conservation measures outlined in the U.S. Bureau of Reclamation's Proposed Action for the Long-Term Operation of the CVP. This effort is funded under the Central Valley Project Improvement Act (CVPIA) authority Provision 3046 (g)(4), which states the primary purpose of this effort shall be to support the Secretary's efforts in fulfilling the requirements of this title through improved scientific understanding concerning, but not limited to, measures needed to restore anadromous fisheries to optimum and sustainable levels in accordance with the restored carrying capacities of Central Valley rivers, streams, and riparian habitats. Conceptual and quantitative work described in this report encompass multiple O. mykiss life-stages and transitions among life stages. The geographic scope of the report includes the Lower Stanislaus River, extending from Goodwin Dam down to the confluence of the San Joaquin River. The goal of this study is to develop a framework for monitoring life stages, and transitions among life stages, to quantify how water project operations and environmental variation influence life history expression, abundance, and population productivity. Data is recorded in a combination of paper to computer files and electronic-only files. Annual reports summarize the survey findings. While this project is ongoing, this is a completed dataset for the collection sessions performed in the years 2021- 2025.
Life histories of the perennial geophyte Erythronium grandiflorum (Liliaceae) in Colorado subalpine transplant garden from annual measurements, 1991 onward
In an outdoor garden at Irwin, Colorado, we established glacier lily plants in open-bottomed PVC pots that protected them from gopher attack. The initial cohorts were excavated from field sites as mature corms of unknown age. Later cohorts were grown from seed, so their ages are known. Each spring since 1991, we have noted fruit and flower production. In August, after the aboveground parts have died back, we exhume the plants, wash off the soil, weigh the corms, characterize their morphology, photograph them, and replant them. If a corm splits, we replant the pieces in separate pots. The study is ongoing, with 264 plants in 2019. Main findings through 2020: plants produce 0-4 flowers per year, depending on size; most plants flower each year; death is rare, with many plants having survived the entire study; setting a fruit reduces corm substantially (cost of reproduction); plants appear to regulate weight by adjusting flower production, and by splitting; genotypes vary in splitting propensity. Oddly, mortality is higher in very large corms than in mid-sized ones. Evidence for senescence is scant.
Fruit, seed dispersal, and life history traits of tropical rainforest trees of the Anamalai Hills, Western Ghats, India
<p>This dataset contains compiled Fruit, seed dispersal, and life history traits of tropical rainforest trees of the Anamalai Hills, Western Ghats, India. The list of species included are mainly from the following two related publications:<br>- Muthuramkumar, S., Ayyappan, N., Parthasarathy, N., Mudappa, D., Raman, T.R.S., Selwyn, M.A. and Pragasan, L.A. (2006), <a href="https://doi.org/10.1111/j.1744-7429.2006.00118.x">Plant Community Structure in Tropical Rain Forest Fragments of the Western Ghats, India</a>. <em>Biotropica</em>, 38: 143-160. https://doi.org/10.1111/j.1744-7429.2006.00118.x<br>- Osuri, A., Chakravarthy, D., Mudappa, D., Raman, T., Ayyappan, N., Muthuramkumar, S., & Parthasarathy, N. (2017). <a href="http://httpd//doi.org/10.1017/S0266467417000219">Successional status, seed dispersal mode and overstorey species influence tree regeneration in tropical rain-forest fragments in Western Ghats, India</a>. <em>Journal of Tropical Ecology</em>, 33(4), 270-284. doi:10.1017/S0266467417000219<br>The present dataset is an expanded and updated version of the related dataset available at <a href="https://doi.org/10.5061/dryad.vd0nn">https://doi.org/10.5061/dryad.vd0nn</a><br> <br>Species traits information was collated from <a href="http://www.biotik.org/">BIOTIK (http://www.biotik.org/</a>), <a href="http://www.flowersofindia.net/">Flowers of India (http://www.flowersofindia.net/)</a>, India Biodiversity Portal (http://indiabiodiversity.org/), <a href="https://doi.org/10.5061/dryad.234/1">Global wood density database (https://doi.org/10.5061/dryad.234/1)</a> and <a href="https://doi.org/10.1017/S0266467417000219">Osuri et al. (2014): https://doi.org/10.1017/S0266467417000219</a>. We also referred to the following previous studies that provided information on the successional status of rain-forest species in the Western Ghats (Chetana 2013, Pascal 1988, Raman et al. 2009, Sreejith 2005).</p> <p><strong>References:</strong><br>CHETANA, H. C. 2013. Assessing the ecological processes in abandoned tea plantations and its implication for ecological restoration in the Western Ghats, India. PhD thesis, Manipal University.<br>OSURI, A. M., KUMAR, V. S. & SANKARAN, M. 2014. Altered stand structure and tree allometry reduce carbon storage in evergreen forest fragments in India’s Western Ghats. <em>Forest Ecology and Management </em>329: 375–383.<br>PASCAL, J. P. 1988. <em>Wet evergreen forests of the Western Ghats of India: Ecology, structure, floristic composition and succession</em>. Institut Français de Pondichéry, Pondicherry.<br>RAMAN, T. R. S., MUDAPPA, D. & KAPOOR, V. 2009. Restoring rainforest fragments: survival of mixed-native species seedlings under contrasting site conditions in the Western Ghats, India. <em>Restoration Ecology</em> 17:137–147.<br>SREEJITH, K. A. 2005. Ecological and ecophysiological studies on the successional status of tree seedlings in tropical wet evergreen and semi-evergreen forests of Kerala. PhD thesis, Forest Research Institute, Dehradun.</p> <p><strong>Geographic Coverage:</strong><br>1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India<br>2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E)</p> <p><strong>Temporal Coverage:</strong><br>1. Begins: 2003-03-01 (Year, Month, Day)<br>2. Ends: 2024-02-10 (Year, Month, Day)</p> <p>Besides the <strong>README.txt</strong> file, the dataset includes the following comma-delimited text (csv) file with the data in columns as explained below:</p> <p><strong>Anamalai_tree_traits_2024.csv</strong></p> <p><strong>spec_name_ORIG:</strong> Scientific name of the species used during the data collection<br><strong>genus:</strong> Genus of the taxon<br><strong>specificEpithet:</strong> Specific epithet of the taxon in the Latin binomial name<br><strong>Accept_name_WFO:</strong> Updated scientific name of the species as in Plants of the World Online (POWO, https://powo.science.kew.org/)<br><strong>Habit:</strong> life form of the species(tree/shrub/cane/palm)<br><strong>Distribution:</strong> Distribution of the species in the study area (Native/Endemic/Introduced)<br><strong>IUCN_status:</strong> IUCN status of the species (CR-Critically Endangered,DD-Data deficient,EN-Endangered,LC-Least Concern,NT-Near Threatened,VU-Vulnerable,NA-Unknown)<br><strong>Wden_final:</strong> Wood density value assigned for the species (g cm^-3); NA - not available; sourced from Global wood density database (https://doi.org/10.5061/dryad.234/1)<br><strong>wd_level:</strong> Level in which the wood density value belongs (Species - wood density value is from species level; genus - wood density value assigned is the genus level average value)<br><strong>fruit_type:</strong> Morphological type of fruit<br><strong>fleshy_dry:</strong> Whether fruit is a dry fruit or fleshy, with aril or other parts <br><strong>seed_size:</strong> Species seed size: L = Large (>3 cm); M = Medium (1-3 cm); S = Small (<1 cm)<br><strong>disperser:</strong> Categories indicating seed dispersal mode: Bird, mammal, bird and mammal (Mammal_bird), gravity, wind, or unknown<br><strong>habitat:</strong> Habitat affinity category: EG_edg - evergreen forest edge; EG_for - evergreen forest; Dec_for - deciduous forest; Int – Introduced species; Unknown – Unknown<br><strong>habt_new:</strong> Habitat affinity new category: Mature – mature forest; Secondary – secondary forest, NA - unknown/Introduced species<br><strong>ad_ht:</strong> Species maximum adult height (m)</p>
Identification of an altitudinal migration pattern of Abies pinsapo in the Baetic Mountains through the presence of its life stages
<p>This data set is used to explore the altitudinal shift of <em>Abies pinsapo</em> Boiss. in the Baetic System. We analysed the potential distribution of the realised and reproductive niches of <em>A. pinsapo</em> populations in the Ronda Mountains (Southern Spain) by using species distribution models (SDMs) for two life stages within the current populations. The realised and reproductive niches of <em>A. pinsapo</em> are different to one another, which may indicate a displacement in its altitudinal distribution.</p>
Product Images for Life Cycle Assessment Dataset For Peritoneal Dialysis and Haemodialysis in Modena
<p>The database contains a collection of images showcasing the individual components of peritoneal dialysis (PD) products, along with their corresponding weights. These images serve as a visual record for life cycle assessment (LCA) purposes, focusing on the material composition and environmental impact of each product.</p> <ol> <li> <p><strong>Patient Education Materials</strong>: Photographs of educational materials provided to patients, with accompanying data on the weight of the paper and packaging.</p> </li> <li> <p><strong>Catheters and Surgical Kits</strong>: Images display the disassembled components of PD catheters and surgical kits, including tubing, connectors, and packaging. Each image is annotated with the precise weight of the individual components.</p> </li> <li> <p><strong>Dialysis Solution Bags</strong>: The database includes images of both CAPD and APD solution bags, separated into their constituent parts (e.g., plastic bag, solution, and protective wrapping), with weights noted for each component.</p> </li> <li> <p><strong>Connection Devices and Consumables</strong>: Detailed images of connection devices, clamps, and other consumable items, with individual component weights clearly labeled.</p> </li> <li> <p><strong>Packaging and Transport Materials</strong>: Photographs of transport packaging, such as cardboard boxes and plastic wraps, alongside recorded weights for each element.</p> </li> <li> <p><strong>Maintenance Items</strong>: Visuals of terminal catheter sets, cleaning agents, and related products, each accompanied by their respective weight data.</p> </li> <li> <p><strong>Disposal Components</strong>: Images of used solution bags, syringes, and other single-use items, separated into recyclable and non-recyclable components, with weights specified for each.</p> </li> </ol> <p>This image-based database provides a clear and comprehensive reference for the material breakdown and weight distribution of PD product components, essential for conducting a thorough LCA and identifying areas for environmental improvement.</p>
Dataset for Gate-to-Gate Life Cycle Assessment of Lithium-Ion Battery Recycling Pre-Treatment
<p>Recycling spent lithium-ion batteries (LIBs) is crucial for improving environmental sustainability and conserving resources. Due to the diversity of LIB applications and recycling technologies, the environmental and energy impacts are not well understood. Comprehensive assessments must consider the distinct operations, methodologies, technology efficiency, and final treatment of materials. This study provides a partial gate-to-gate life cycle analysis (LCA) of a small-scale recycling plant in the Czech Republic, focusing on pre-treatment of spent LIBs from electric vehicles (EVs) and consumer electronics cells (CECs). The study highlights the benefits of recycling pre-treatment for CECs, significantly reducing environmental impact categories (EICs) such as climate change, eutrophication, and resource use. A high secondary use rate of obtained materials is crucial for environmental benefits, with metal reuse from packaging, connectors, and current collectors being especially important.</p>
Holdridge Life Zones Classification in New Caledonia Habitats
<h1>Description</h1> <p>This dataset aims to represent, in geographic space, the distribution of life zones as first defined by Holdridge in 1947 and updated in 1967. Life zones are delineated through three parameters:</p> <ul> <li>Mean Annual Biotemperature (°C): This axis represents the average annual temperature, considering only temperatures above 0°C, as it influences biological activity. It determines the thermal regime of the environment.</li> <li>Annual Precipitation (mm): This axis measures the total annual precipitation, indicating moisture availability. It is crucial for determining the hydric regime and supporting different types of vegetation and ecosystems.</li> <li>Potential Evapotranspiration Ratio (PET): This axis is the ratio of potential evapotranspiration to annual precipitation. It reflects the balance between water demand and supply, indicating aridity or humidity levels and influencing vegetation types and ecosystem dynamics.</li> </ul> <p>We used a combination of WorldClim datasets (Biotemperature and potential evapotranspiration) and Météo-France Aurelhy datasets (Annual Precipitation) specifically designed for New Caledonia to produce the raster with a 1 km² resolution.</p> <h1>Content</h1> <p>This dataset was produced, analyzed, and verified using a combination of open-source software, including QGIS, PostgreSQL, PostGIS, Python, R and the GDAL library, all running on Linux.</p> <ul> <li>amap_raster_holdridge_nc.tif is a GeoTIFF, utilizing the WGS84 international coordinate system, and consists of a single band with three major classes coded as <ul> <li>Dry life zone (rast = 1)</li> <li>Moist life zone (rast = 2)</li> <li>Rain life zone (rast = 3)</li> </ul> </li> <li>holdridge_3classes_NC.png is an image illustrating the valid domain of life zones in New Caledonia and the classification used in the dataset.</li> </ul> <h1>Limitations</h1> <p>Strictly, the classification leads to five distinct classes (very dry, dry, moist, wet, and rain), but as the two extreme classes cover less than 0.5% of New Caledonia, we merged very dry and dry into the "dry" class, as well as wet and rain into the "rain" class as illustrated in the Figure <a href="../api/records/12731521/draft/files/holdridge_3classes_NC.png/content" target="_blank" rel="noopener noreferrer">holdridge_3classes_NC.png</a>.</p>
The behavioral phenotype of early life adversity
<p>In this dataset, we categorized studies investigating the effects of early life adversity on behavior in mice and rats. The dataset is ideal for meta-analyses. For more information about the dataset and the project, see https://osf.io/ra947/</p>
Stanislaus River Steelhead Life Cycle Monitoring Program
The Stanislaus River Steelhead Life Cycle Monitoring Program is an ongoing survey starting in 2021 that aims to estimate the amount of steelhead (Onocorhynchus mykiss) present in the Stanislaus River by recording steelhead and redds observed. In addition to estimating the amount of steelhead, the survey aims to collect data relevant to steelhead spawning, documenting environmental conditions such as temperature and flow, and recording other fish activity present.
Life History Traits of Resprouting Puerto Rican Tropical Dry Forest Trees, Guánica Forest, 1981-2018
This dataset provides trait and demographic data for 44 tropical dry forest tree species from the Guánica State Forest in southwest Puerto Rico. The study area spans 4,500 ha of semi-deciduous TDF, where the sampled species represent over 90% of all individuals with a diameter at breast height (dbh) ≥2.5 cm. The dataset integrates ten functional traits, combining newly collected measurements (2017–2018) with previously published data (Vargas et al. 2021b). Previously published data includes xylem-specific hydraulic conductivity (ks), Huber value (hv), and hydraulic safety margin (HSM), with species-level data availability ranging from 19 to 44 species, except for HSM, which was measured for six species. Trait measurements were primarily collected during the wet season (August–November), except stomatal behaviour traits (psimax, psidv, and gsmax), which were assessed during the winter dry season before leaf fall. Demographic data encompass species-specific growth rates and annual survival rates for adult trees, derived from four permanent census plots (625 m² to 10,000 m²) distributed across the forest. These plots, established in mature upland TDF on limestone substrates with mollisol soils, were monitored between 1992 and 2019. Growth rate estimates are based on diameter increments recorded at regular censuses over 20.4–26.4 years. Survival rates were calculated over a 21-year period (1998–2019), mitigating the influence of extreme drought events. Standardised measurement protocols ensured data consistency, including repeated diameter assessments at multiple stem locations and the exclusion of wet-season measurements to prevent water-related swelling artifacts. Growth rates were derived from the regression slope of dbh against time, incorporating a minimum of two dbh measurements per individual (following Poorter et al. 2010). Annual survival rate was calculated over a 21-year timespan (1998–2019) to avoid bias introduced by an intense drought in 1997. The followin
Wind Value Literature Selection for End-of-Life Valuation Excel File WP 5.1
<p>Authors from Wind Value, the Re-Wind Network and IEA Wind Task 45 carried out research on methods of processing end-of-life wind turbine blades. This included a structured literature review which selected the literature in the Excel file. Part of this work helps to estimate the value of an end-of-life wind farm contributing to Work Package 5.1 of the Wind Value project. The paper was pubished as Deeney et al. (2025) <a href="https://www.sciencedirect.com/science/article/pii/S1364032125000917?via%3Dihub">End-of-life wind turbine blades and paths to a circular economy</a>, <em>Renewable and Sustainable Energy Reviews. </em></p>
Tab and comma delimited versions of Discover Life bee species guide and world checklist (Hymenoptera: Apoidea: Anthophila)
<p><span><em><strong>Introduction</strong></em></span></p> <p>This archive includes a tab-delimited (tsv) and comma-delimited (csv) version of the <a href="http://www.discoverlife.org/mp/20q?act=x_checklist&guide=Apoidea_species">Discover Life bee species guide and world checklist </a>(Hymenoptera: Apoidea: Anthophila). Discover Life is an important resource for bee species names and this update is from Draft-55, November 2020. Data were accessed and transformed into a tsv file in August 2023 using <a href="https://www.globalbioticinteractions.org/">Global Biotic Interactions</a> (GloBI) <a href="https://github.com/globalbioticinteractions/nomer">nomer</a> software. GloBI now incorporates the Discover Life bee species guide and world checklist in its functionality for searching for bee interactions.</p> <p><span><strong>Update! New Dataset also includes Subgenera Names</strong></span></p> <p>A new, tab-delimited version of the Discover Life taxonomy as derived from Dorey et. al, 2023 can be found via Zenodo at <a href="https://doi.org/10.5281/zenodo.10463762">https://doi.org/10.5281/zenodo.10463762</a>. This version of the Discover Life world species guide and checklist includes subgeneric names.</p> <p><span><strong>Citation</strong></span></p> <p><strong>Please cite the original source for this data as:</strong></p> <blockquote> <p><strong>Ascher, J. S. and J. Pickering. 2022.<br>Discover Life bee species guide and world checklist (Hymenoptera: Apoidea: Anthophila).<br>http://www.discoverlife.org/mp/20q?guide=Apoidea_species </strong>Draft-56, 21 August, 2022</p> </blockquote> <p><span><strong><em>nomer</em></strong></span></p> <p>nomer is a command-line application for working with taxonomic resources offline. nomer incorporates many of the present taxonomic catalogs (e.g., catalog of life, ITIS, EOL, NCBI) and provides simple tools for comparing between resources or resolving taxonomic names based on one or more taxonomic name catalogs. Discover Life is in nomer version 0.5.1 and this full dataset can be recreated by installing nomer from <a href="https://github.com/globalbioticinteractions/nomer">https://github.com/globalbioticinteractions/nomer</a> and running</p> <blockquote> <p>$ nomer list discoverlife > discoverlife.tsv</p> </blockquote> <p><span><em><strong>Data Columns</strong></em></span></p> <p>Discover Life provides a world name checklist and includes other names (synonyms and homonyms) that refer to the same species. In the tsv file, the provided name is both the accepted, or checklist name, or "other name." All names will be listed as a providedName. Below is an example subset of the transformed version of the data.</p> <ul> <li>providedExternalId= link to name on Discover Life</li> <li>providedName=an accepted or "<em>other name</em>" in the Discover Life bee checklist. "Other names" can be synonyms or homonyms.</li> <li>providedAuthorship=authorship for the providedName</li> <li>providedRank=rank of the providedName</li> <li>providedPath=higher taxonomy of the providedName. This will be the same as the accepted name or resolvedName</li> <li>relationName=relationship between the "<em>other name</em>" and the bee name in the Discover Life checklist. It may include itself</li> <li>resolvedExternalID=an <strong>accepted name</strong> in the Discover Life bee checklist</li> <li>resolvedExternalId=link to name on Discover Life</li> <li>resolvedAuthorship=authorship of the accepted, or checklist name</li> <li>resolvedRank=rank of the accepted, or checklist name</li> <li>resolvedPath=higher taxonomy of the accepted, or checklist name</li> </ul> <p><span><em><strong>Changes</strong></em></span></p> <p>No major changes to format in this version.</p> <p><span><em><strong>References</strong></em></span></p> <p>Jorrit Poelen, & José Augusto Salim. (2022). globalbioticinteractions/nomer: (0.2.11). Zenodo. https://doi.org/10.5281/zenodo.6128011</p> <p>Poelen JH, Simons JD and Mungall CH. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. <a href="https://doi.org/10.1016/j.ecoinf.2014.08.005">https://doi.org/10.1016/j.ecoinf.2014.08.005</a>.</p> <p>Seltmann KC, Allen J, Brown BV, Carper A, Engel MS, Franz N, Gilbert E, Grinter C, Gonzalez VH, Horsley P, Lee S, Maier C, Miko I, Morris P, Oboyski P, Pierce NE, Poelen J, Scott VL, Smith M, Talamas EJ, Tsutsui ND, Tucker E (2021) Announcing Big-Bee: An initiative to promote understanding of bees through image and trait digitization. Biodiversity Information Science and Standards 5: e74037. <a href="https://doi.org/10.3897/biss.5.74037">https://doi.org/10.3897/biss.5.74037</a></p> <p>Dorey, J.B., Fischer, E.E., Chesshire, P.R. et al. A globally synthesised and flagged bee occurrence dataset and cleaning workflow. Sci Data 10, 747 (2023). https://doi.org/10.1038/s41597-023-02626-w</p>
Unlocking the power of computer modelling and simulation across the life sciences product lifecycle
<p><strong>Unlocking the Power of Computer Modelling and Simulation Across the Life Sciences Product Lifecycle</strong></p> <p>In an era where technology continuously reshapes the boundaries of research and development, the field of life sciences stands at the cusp of a transformative shift. The potent combination of computer modelling and simulation has begun to unlock unprecedented opportunities across the product lifecycle in life sciences, promising to revolutionize everything from medicinal product development to clinical research. Let's delve into how these technological advancements are paving the way for groundbreaking progress in medicine and healthcare.</p> <p><strong>The Fusion of Technology and Life Sciences</strong></p> <p><em>In Silico Methods: A New Frontier in Medicine</em></p> <p>The term 'in silico' refers to computer simulations used in the study of biological and chemical processes. The video highlights the growing importance of in silico methods in the life sciences sector, particularly in the United Kingdom. These methods allow for the virtual testing of new medicinal products, significantly reducing the need for costly and time-consuming physical trials.</p> <p><em>Bridging the Gap with Computational Modeling</em></p> <p>Computational modeling is another key aspect discussed in the presentation. It involves the use of computer algorithms and mathematical models to simulate real-world medical data. This approach enables researchers to predict how medicinal products will behave in various scenarios, including their interaction with different types of patient data. As a result, computational modeling is instrumental in enhancing the precision of clinical research and improving medical equitability by considering a broader range of patient profiles.</p> <p><strong>The Impact on Clinical Research and Patient Care</strong></p> <p><em>Enhancing Precision and Efficiency</em></p> <p>One of the most notable benefits of integrating computer modelling and simulation into the life sciences is the enhanced precision and efficiency it brings to clinical research. By leveraging real-world medical data, researchers can obtain more accurate predictions about the efficacy and safety of new medicinal products. This not only accelerates the development process but also ensures that treatments are more tailored to individual patient needs.</p> <p><em>Promoting Medical Equitability</em></p> <p>The video underscores the role of these technologies in promoting medical equitability. Through the use of patient data simulations, it becomes possible to account for a wider array of genetic, environmental, and lifestyle factors that influence health outcomes. This inclusive approach ensures that the benefits of medical advancements are accessible to a diverse population, addressing disparities in healthcare access and treatment efficacy.</p> <p><strong>Conclusion: The Future is Now</strong></p> <p>The integration of computer modelling and simulation in the life sciences heralds a new era of medical research and patient care. As we continue to explore the potential of these technologies, it's clear that they hold the key to unlocking more efficient, precise, and equitable healthcare solutions. The journey towards fully realizing this potential is just beginning, but the promise it holds is immense. As we stand on the brink of this technological revolution, one thing is certain: the future of medicine and healthcare is being shaped here and now, and it's brighter than ever.</p>
Inter-Chemical Correlation results for the study: HHEARx2018-2532 (Environmental Toxins in Early Life: Shaping Health and Disease in Childhood)
Title: Environmental Toxins in Early Life: Shaping Health and Disease in Childhood <br>Species: Homo sapiens <br>Number of samples: 1147 <br>Number of named analytes: 48 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=69 <br>
Life Cycle Assessment Dataset For Peritoneal Dialysis in Modena
<p>The database outlines a structured pathway for managing patients with end-stage renal disease (ESRD) undergoing peritoneal dialysis (PD). It provides detailed descriptions of the various stages and protocols involved in the treatment process.</p> <ol> <li> <p><strong>Patient Education and Evaluation</strong>: The initial stages focus on educating patients about renal replacement therapy options and assessing their eligibility and suitability for PD. These assessments consider medical history, anatomical factors, and the suitability of the home environment.</p> </li> <li> <p><strong>Pre-Surgical and Surgical Procedures</strong>: The database includes pre-surgical evaluations, the surgical placement of the peritoneal catheter (performed under local anesthesia or through laparoscopic surgery), and subsequent checks to confirm the catheter's functionality.</p> </li> <li> <p><strong>Training and Initiation of PD</strong>: Training sessions for patients are outlined for both continuous ambulatory peritoneal dialysis (CAPD) and automated peritoneal dialysis (APD). These sessions are initiated following confirmation of catheter functionality. The database also specifies the use of products such as CAPD by Fresenius and APD by Baxter and describes the progressive implementation of the dialytic dose.</p> </li> <li> <p><strong>Routine Maintenance and Monitoring</strong>: Routine care includes monthly clinical evaluations, annual peritoneal equilibrium tests (PET), and periodic changes to the terminal catheter set to maintain treatment safety and effectiveness.</p> </li> <li> <p><strong>Handling Complications</strong>: Protocols for managing potential complications are detailed, including procedures for addressing catheter malfunctions, diagnosing and treating peritonitis, and catheter removal when necessary.</p> </li> <li> <p><strong>Data Collection on Patient Outcomes</strong>: The database suggests tracking patient preferences for different therapies and monitoring outcomes at various stages, providing insights into patient choices and clinical results.</p> </li> </ol> <p>This comprehensive database is designed to standardize and optimize the delivery of PD care. It offers healthcare professionals in nephrology a detailed framework for improving patient outcomes and streamlining clinical workflows.</p>
Multimodal video and IMU kinematic dataset on daily life activities using affordable devices (VIDIMU)
<p>Human activity recognition and clinical biomechanics are challenging problems in physical telerehabilitation medicine. However, most publicly available datasets on human body movements cannot be used to study both problems in an out-of-the-lab movement acquisition setting. The objective of the VIDIMU dataset is to pave the way towards affordable patient tracking solutions for remote daily life activities recognition and kinematic analysis. </p> <p>The VIDIMU dataset includes 54 healthy young adults that were recorded on video and 16 of them were simultaneously recorded using custom IMUs. For each subject, 13 activities were registered using a low-resolution video camera and five Inertial Measurement Units (IMUs). Inertial sensors were placed in the lower or the upper limbs of the subject, respectively for activities that involve movement with the lower or the upper body. Video recordings were postprocessed using the state-of-the-art pose estimator <em>BodyTrack</em> (similar to OpenPose, and included in NVIDIA Maxine-AR-SDK) to provide a sequence of 3D joint positions for each movement. Raw IMU recordings were post-processed to compute joint angles by inverse kinematics with <em>OpenSim</em>. For recordings including simultaneous acquisition of video and IMU data types, these signals were used for data file synchronization. Collected data can be further used in applications related to human activity recognition and biomechanics related experiments in simulated home-like settings.</p> <p> </p> <p> </p>
Density independent prey choice, taxonomy, life history and web characteristics determine the diet and biocontrol potential of spiders (Linyphiidae and Lycosidae) in cereal crops - Dataset
<p>Materials and Methods</p> <p>Fieldwork</p> <p>Money spiders (Araneae: Linyphiidae) and wolf spiders (Araneae: Lycosidae) were the two most common families present in these field surveys, so were prioritised for collection. Spiders were visually located along transects in two adjacent barley fields at Burdons Farm, Wenvoe in South Wales (51°26'24.8"N, 3°16'17.9"W) and collected from occupied webs and the ground, between April and September 2018. Surveys and sampling were conducted five days per week across this period. Each transect was adjacent to a randomly selected tramline and they were distributed across the entire field. The areas searched were 4 m<sup>2</sup> quadrats at least 10 m apart and all observed linyphiids and lycosids were collected in approximately 15-minute searches. The spiders included in this study were taken from 64 locations across 24 days (Supplementary Table 3) along the aforementioned transects. Spiders were individually placed into 1.5 ml microcentrifuge tubes containing 100 % ethanol using an aspirator, regularly changing meshing, at least every five spiders, to limit potential cross-contamination between spiders (spiders were also subsequently washed during transferral to fresh ethanol at the identification and, separately, dissection stages). Linyphiids occupying webs were prioritised for collection, but ground-active linyphiid spiders were also collected. For each spider taken from a web, the height of the web from the ground and its approximate dimensions were recorded, the latter calculated as approximate web area. Spiders were taken to Cardiff University, transferred to fresh ethanol, adults identified to species-level and juveniles to genus, and stored at -80 °C in 100 % ethanol until subsequent DNA extraction. To obtain data on local prey density, 4 m<sup>2</sup> of ground and crop stems were suction sampled using a ‘G-vac’ for 30 seconds at each quadrat from which spiders were collected, with the collected material emptied into a bag, any organisms immediately killed with ethyl-acetate and material frozen for storage before sorting into 70 % ethanol in the lab.</p> <p>All invertebrates were identified to family level due to the restriction of many of the metabarcoding-derived dietary data to this level, and the difficulty associated with finer taxonomic resolution of many taxa. Exceptions included springtails of the superfamily Sminthuroidea (Sminthuridae and Bourletiellidae, which were often indistinguishable following suction sampling and preservation due to the fine features necessary to distinguish them) which were left at super-family, mites (many of which were immature or in poor condition) which were identified to order level and wasps of the superfamily Ichneumonoidea (which were identified no further due to obscurity of wing venation due to damage).</p> <p> </p> <p>Extraction and high-throughput sequencing of spider gut DNA</p> <p>Given their prevalence in field collections, dietary analysis was carried out for the linyphiid genera <em>Erigone</em>, <em>Tenuiphantes</em>, <em>Bathyphantes</em> and <em>Microlinyphia </em>(Araneae: Linyphiidae), and the Lycosidae genus <em>Pardosa</em>. Spiders were transferred to and washed in fresh 100 % ethanol to reduce external contaminants prior to identification via morphological key <sup>1</sup>. Abdomens were removed from spiders and again washed in and transferred to fresh 100 % ethanol. DNA was extracted from the abdomens via Qiagen TissueLyser II and DNeasy Blood & Tissue Kit (Qiagen) as per the manufacturer protocol, but with an extended lysis time of 12 hours to account for the complex and branched gut system in spider abdomens <sup>2</sup>. At least one extraction negative (blank tubes treated identically to samples) was included per 12 spiders (each extraction typically contained 24 spiders, thus two extraction negatives), which was included in subsequent PCR and high-throughput sequencing to detect instances of lab/reagent contamination.</p> <p>For amplification of DNA, two primer pairs were used. BerenF-LuthienR <sup>3</sup> amplified a broad range of invertebrates including spiders, and TelperionF-LaureR, amplified a range of invertebrates but fewer spiders (modified from TelperionF-LaurelinR <sup>3</sup> via one base-pair change from Laurelin; 5’-ggrtawacwgttcawccagt-3’). Primers were labelled with unique 10 bp molecular identifier tags (MID-tags) so that each individual had a unique pairing of forward and reverse tags for identification of each spider post-sequencing. PCR reactions of 25 µl contained 12.5 µl Qiagen PCR Multiplex kit, 0.2 µmol (2.5 µl of 2 µM) of each primer and 5 µl template DNA. Reactions were carried out in the same thermocycler, optimised via temperature gradient, with an initial 15 minutes at 95 °C, 35 cycles of 95 °C for 30 seconds, the primer-specific annealing temperature for 90 seconds and 72 °C for 90 seconds, respectively, followed by a final extension at 72 °C for 10 minutes. BerenF-LuthienR and TelperionF-LaureR used annealing temperatures of 52 °C and 42 °C, respectively.</p> <p>Within each PCR 96-well plate, 12 negative controls (extraction and PCR), 2 blank controls and 2 positive controls were included (i.e. 80 samples per plate), based on Taberlet <em>et al. </em>(2018). Positive controls were mixtures of invertebrate DNA comprised of non-native Asiatic species in four different proportions (Supplementary Table 1) and blanks were empty wells within each plate to identify tag-jumping into unused MID-tag combinations. PCR negative controls were DNase-free water treated identically to DNA samples. A negative control was present for each MID-tag to identify any contamination of primers. All PCR products were visualised in a 2 % agarose gel with SYBRSafe (Thermo Fisher Scientific, Paisley, UK) and placed in categories based on their relative brightness. The concentration of these brightness categories was quantified via Qubit dsDNA High-sensitivity Assay Kits (Thermo Fisher Scientific, Waltham, MA, USA) with at least three representatives of each category per plate. The PCR products were then proportionally pooled according to these concentrations. Each pool was cleaned via SPRIselect beads (Beckman Coulter, Brea, USA), with a left-side size selection using a 1:1 ratio (retaining ~300-1000 bp fragments). The concentration of the pooled DNA was then determined via Qubit dsDNA High-sensitivity Assay Kits and pooled together into one library per primer pair. Library preparation for Illumina sequencing was carried out on the cleaned libraries via NEXTflex Rapid DNA-Seq Kit (Bioo Scientific, Austin, USA) and samples were sequenced on an Illumina MiSeq via a V3 chip with 300-bp paired-end reads (expected capacity ≤25,000,000 reads). Bioinformatic analysis followed (Drake et al., 2021; Supplementary Information 1).</p> <p> </p> <p>Statistical analysis</p> <p>All analyses were conducted in R v4.0.0 <sup>6</sup>. Initial multivariate analyses used binary data (i.e., presence/absence) given the various problems inherent to quantifying metabarcoding data <sup>7,8</sup>. Prey species that occurred only once across all of the dietary samples were removed before further analyses to prevent outliers skewing the results, which is particularly problematic for non-metric multidimensional scaling. Spider diets were compared between variables using multivariate generalized linear models (MGLMs) via ‘manyglm’ in the ‘mvabund’ package <sup>9</sup> with a binomial error family and Monte Carlo resampling. Model independent variables included spider genus, spider life stage (juvenile or adult, the latter defined by fully developed genitalia), spider sex and all two-way interactions between these variables. Pairwise two-way interactions were also included between the aforementioned variables and Julian day to account for how seasonality may affect these relationships.</p> <p>Coarse dietary differences were visualised by non-metric multidimensional scaling (NMDS) via metaMDS in the ‘vegan’ package <sup>10</sup> with Jaccard distance in two dimensions and 999 tries. For NMDS, outliers (usually samples containing rare taxa) were identified by plotting and subsequently removed to facilitate separation of samples and achieve minimum stress. For visualisation of the effect of categorical variables against the dietary NMDS, spider plots were created using ‘ordispider’ with ‘ggplot’ and the ‘RColorBrewer’ ‘Accent’ colour palette <sup>11</sup>. Spider diet was compared against web characteristics for spiders for which both data were available using the MGLM process outlined above, but with starting models containing web height, web area, an interaction between the two, and pairwise interactions between genus, life stage and sex with the two web variables. This model used the same binomial error family as above, but with a ‘cloglog’ link function. For visualisation of the effect of continuous variables against the NMDS, surf plots were created with scaled coloured contours using the function “ordisurf” of the “ggplot” package in R.</p> <p>All prey taxa were classified as agricultural pests, natural enemies or excluded from subsequent analyses of intraguild predation and biocontrol (Supplementary Table 2). Intraguild predation and biocontrol variables were created by counting the number of natural enemy taxa, and, separately, of agriculturally relevant “pest” taxa (taxa containing species that commonly adversely affect agricultural productivity; Supplementary Table 2) in each spider’s diet. These resultant count data (effectively the diversity of pests and natural enemies predated by each individual spider) were separately analysed against spider genus, life stage and sex via GLM. “Site” (denoting the 4 m<sup>2</sup> area from which spiders were collected within fields) was initially included as a random effect in generalized linear mixed-models, but no significant effect was observed when comparing this model against a standard GLM via a likelihood ratio test of nested models using the ‘lrtest’ command in the ‘lmtest’ package <sup>12</sup>. Standard GLMs were thus used to avoid issues relating to singularity in the mixed models. The assumptions for the resultant Poisson error family GLMs were tested using the “testResiduals” function of the ‘DHARMa’ package <sup>13</sup>. Intraguild predation and biocontrol differences between significant terms were visualised using violin plots with the quartiles, median and 95 % upper limit annotated using the ‘geom_violin’ function in ‘ggplot2’.</p> <p><em>In situ</em> spider prey choice was analysed using network-based null models in the ‘econullnetr’ package <sup>14</sup> with the ‘generate_null_net’ command, visually represented with the ‘plot_preferences’ command. Binary dietary data were used alongside suction sample count data to represent prey availability. These suction sample data, as described above, were collected at the same sites as the spiders three days after spider collection. Prior to the taxonomic prey choice analysis, an hemipteran identified no further than order level through dietary analysis was removed due to the inability to pair it to any present prey taxa with certainty. Standardised effect sizes (SES) were extracted for all comparisons for each individual spider and compared between genera, life stages and sexes using permutational multivariate analysis of variance (PerMANOVA) using the ‘adonis’ function of the ’vegan’ package with 9999 permutations and a Euclidean distance matrix to determine overall differences in prey choice.</p> <p> </p> <p>References</p> <p>1. Roberts, M. J. <em>The Spiders of Great Britain and Ireland (Compact Edition)</em>. (Harley Books, 1993).</p> <p>2. Krehenwinkel, H., Kennedy, S., Pekár, S. & Gillespie, R. G. A cost-efficient and simple protocol to enrich prey DNA from extractions of predatory arthropods for large-scale gut content analysis by Illumina sequencing. <em>Methods Ecol. Evol.</em> <strong>8</strong>, 126–134 (2017).</p> <p>3. Cuff, J. P. <em>et al.</em> Money spider dietary choice in pre- and post-harvest cereal crops using metabarcoding. <em>Ecol. Entomol.</em> <strong>46</strong>, 249–261 (2021).</p> <p>4. Taberlet, P., Bonin, A., Zinger, L. & Coissac, E. <em>Environmental DNA</em>. (Oxford University Press, 2018).</p> <p>5. Drake, L. E. <em>et al.</em> An assessment of minimum sequence copy thresholds for identifying and reducing the prevalence of artefacts in dietary metabarcoding data. <em>Methods Ecol. Evol.</em> <strong>in press</strong>, (2021).</p> <p>6. R Core Team. R: A language and environment for statistical computing. (2020).</p> <p>7. Deagle, B. E., Thomas, A. C., Shaffer, A. K. & Trites, A. W. Quantifying sequence proportions in a DNA-based diet study using Ion Torrent amplicon sequencing: which counts count? <em>Mol. Ecol. Resour.</em> <strong>13</strong>, 620–633 (2013).</p> <p>8. Deagle, B. E. <em>et al.</em> Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data? <em>Mol. Ecol.</em> <strong>28</strong>, 391–406 (2019).</p> <p>9. Wang, Y., Naumann, U., Wright, S. T. & Warton, D. I. mvabund – an R package for model-based analysis of multivariate abundance data. <em>Methods Ecol. Evol.</em> <strong>3</strong>, 471–474 (2012).</p> <p>10. Oksanen, J. <em>et al.</em> vegan: Community Ecology Package. (2016).</p> <p>11. Neuwirth, E. RColorBrewer: ColorBrewer palettes. (2014).</p> <p>12. Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. <em>R News</em> <strong>2</strong>, 7–10 (2002).</p> <p>13. Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. (2020).</p> <p>14. Vaughan, I. P. <em>et al.</em> econullnetr: an r package using null models to analyse the structure of ecological networks and identify resource selection. <em>Methods Ecol. Evol.</em> <strong>9</strong>, 728–733 (2018).</p>
Life cycle inventories for the article: Circular Battery Production in the EU: Insights from integrating Life Cycle Assessment into System Dynamics Modeling on Recycled Content and Environmental Impacts
<p>This repository provides the unregionalized life cycle inventories to the paper "<span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a>".</p> <h2>Contents</h2> <p>The repository is split into 2 parts and comprises the following files:</p> <p><strong>01_production: </strong>contains the necessary life cycle inventories for battery production.</p> <ul> <li><strong>01_primary</strong>: contains the life cycle inventories for battery production from primary materials.</li> <li><strong>02_secondary</strong>: contains the life cycle inventories for battery production from secondary materials.</li> <li><strong>03_active_material</strong>: contains the life cycle inventories for the active battery materials from primary materials.</li> <li><strong>04_active_material</strong>: contains the life cycle inventories for the active battery materials from secondary materials.</li> </ul> <p> </p> <p><strong>02_recycling: </strong>contains the necessary inventories for battery recycling.</p> <ul> <li><strong>01_process</strong>: contains the life cycle inventories for battery recycling.</li> <li><strong>02_intermediate</strong>: contains the life cycle inventories for the intermediate system for battery recycling.</li> <li><strong>03_output</strong>: contains the life cycle inventories for the resulting substances from battery recycling.</li> </ul> <h2>Summary</h2> <p>These files allow to reproduce the results of our study. Each file contains the life cycle inventory of one distinct battery capacity (20, 45, 68, 85, 95, 100 kWh) with a specific cell chemistry (LFP, NCA, NMC333, NMC532, NMC622, NMC811, NMC955) for battery production (based on Knehr et al. 2022) or for battery recycling (based on Blömeke et al. 2023).</p> <h2>Related publication</h2> <p>More details on the scientific context is provided in the publication itself:</p> <p><span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a></p> <h2>Funding</h2> <p>This publication (Raphael Ginster and Steffen Blömeke) was created within the Research Training Group CircularLIB, supported by the Ministry of Science and Culture of Lower Saxony with funds from the program zukunft.niedersachsen of the Volkswagen Foundation (MWK | ZN3678).</p> <p>The publication on which this dataset is based were funded by the German Federal Ministry of Education and Research within the Competence Cluster Recycling & Green Battery (greenBatt) under the grant numbers 03XP0302A (Christian Scheller) and 03XP0331A (Jan-Linus Popien). The authors are responsible for the contents of this publication.</p>
Value chains under the framework of life cycle assessment indicators
<p>Tables included in the article "Monitoring the bioeconomy: value chains under the framework of life cycle assessment indicators"</p>
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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.