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99 results for “physical properties”
Dataset on surface peat stoichiometry and physical properties in boreal undrained peatlands in Finland, Natural Resources Institute Finland (Luke) and Geological Survey of Finland (GTK)
<p><strong>Dataset on surface peat stoichiometry and physical properties in boreal undrained peatlands in Finland </strong></p><p><strong>Creators: </strong>Larmola T, Anttila J, Turunen J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M </p><p>The dataset consists of peat properties in a subset of 16 undrained peatland sites (32 peat samples) in Geological Survey of Finland (GTK) national peatland inventory. These sites were sampled between 2002 and 2017 and the subset selected from GTK peat sample archives. These 16 sites represented two pine-<i>Sphagnum-</i> dominated site types (IR, KR) and two treeless sedge fen types (VSN, RhSN) all in 4 replicates and sampled in 2 depths 20-40, 40-60cm). </p><p><strong>Peat analyses</strong> The peat samples were analyzed for C:H:N:S and ash concentration with Leco 628 CHNS analyzer following standard SFS EN13039 with FINAS accredited adjustments JOK3023. The dry matter content was analyzed after drying the sample at 105 ℃ and ash content based on loss on ignition at 550 ℃. The O concentration was determined by difference: %O = 100 - % (ash + total C + N + H + S).</p><p><strong>Stoichiometric calculations</strong>The O concentration was determined by difference: %O = 100 - % (ash + total C + N + H + S). Atomic ratios of C:N, H:C and O:C were calculated based on the individual sample mass values. The C oxidation state (Cox), the oxidative ratio (OR), and the degree of unsaturation (DU) were calculated following equations in the study by Masiello et al. (2008). The analyses are described in more detail in Turunen et al. (manuscript). </p><p>Related datasets used in the same publication are:</p><p>Larmola T, Anttila J, Alm J Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in Finland</p><p>Turunen J. (2023). Surface peat data, Geological Survey of Finland (Version 1) [Data set]. Zenodo. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.8434148&data=05%7C01%7Cluke.tuula.larmola%40valtion.mail.onmicrosoft.com%7Cc48ffad4c0e341d0fa5808dbcaff0289%7C7c14dfa4c0fc47259f0476a443deb095%7C0%7C0%7C638326968887189768%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=AyYOxR7Mas2ef8y3wI7oCrzHWSyqBzt%2FJB0CMN%2BJUiU%3D&reserved=0">https://doi.org/10.5281/zenodo.8434148</a></p><p> </p><p><strong>Data column description </strong></p><p>ID - Site identifier</p><p>site - undrained peatland (UDP) for all rows</p><p>ncoord - North coordinate (latitude), degrees.</p><p>depth - Sampling depth. 20: 0-20 cm, 40: 20-40cm, 60: 40-60cm.</p><p>type - Site type classification according to the Finnish peatland site type system.</p><p>origin - UDP site type. I: treed peatland (peat typically Sphagnum-wood), II: treeless peatland (or sparsely treed, peat typically Sphagnum-sedge)</p><p>type_num - Nutrient level according to site type. 1 is the most nutrient rich and 4 is the least.</p><p>Cmol - Molar carbon concentration in the sample</p><p>Hmol - Molar hydrogen concentration in the sample</p><p>Nmol - Molar nitrogen concentration in the sample</p><p>Omol - Molar oxygen concentration in the sample</p><p>Smol - Molar sulphur concentration in the sample</p><p>bd - Bulk density, kg/m3</p><p>cox - C oxidation state</p><p>or - Oxidative ratio</p><p>du - Degree of unsaturation</p><p>hc - H:C ratio</p><p>cn - C:N ratio</p><p>oc - O:C ratio</p><p><strong>References</strong></p><p>Masiello CA, Gallagher ME, Randerson JT, Deco RM, Chadwick OA (2008) Evaluating two experimental approaches for measuring ecosystem carbon oxidation state and oxidative ratio, Journal of Geophysical Research 113, G03010, <a href="https://doi.org/10.1029/2007JG000534">https://doi.org/10.1029/2007JG000534</a></p><p>Turunen J, Anttila J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M, Alm J, Larmola T 2023. Impacts of forestry drainage on surface peat stoichiometry and physical properties in boreal peatlands in Finland. <i>manuscript.</i></p>
Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in Finland, Natural Resources Institute Finland
<p><strong>Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in Finland</strong></p><p><strong>Creators: Larmola T, Anttila J, Alm J </strong></p><p>The dataset consists of peat properties in a subsample of 30 drained peatland forests in Finland selected from the permanent sample plots of the 8th National Forest Inventory (systematic sample of plots on drained peatland forests, e.g., Hotanen et al. 2006). The subsample included equally different site types of forestry-drained peatlands of those parts of Finland where drainage for forestry is economically viable (Latitude 60-66 ºN, annual temperature sum > 750 dd). </p><p><strong>The site selection criteria</strong> were average peat layer thickness of over 20 cm, no clear-cut areas, site drained before 1995 and ditching had detectably altered hydrology or vegetation. <strong>Peat analyses</strong> Finnish Forest Research Institute (now Natural Resources Institute Finland) sampled peat cores with a box corer in 2002, samples were analysed for bulk density, archived and remaining samples at depths 20-30, 30-40 cm (total of 58) were analysed in 2021. The peat samples were analyzed for C:H:N:S and ash concentration with Leco 628 CHNS analyzer following standard SFS EN13039 with FINAS accredited adjustments JOK3023. The dry matter content was analyzed after drying the sample at 105 ℃ and ash content based on loss on ignition at 550 ℃. </p><p><strong>Stoichiometric calculations</strong>The O concentration was determined by difference: %O = 100 - % (ash + total C + N + H + S). Atomic ratios of C:N, H:C and O:C were calculated based on the individual sample mass values. The C oxidation state (Cox), the oxidative ratio (OR), and the degree of unsaturation (DU) were calculated following equations in the study by Masiello et al. (2008). The analyses are described in more detail in Turunen et al. (manuscript). </p><p>Related datasets used in the same publication are:</p><p>Larmola, T. Anttila J, Turunen J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M Dataset on surface peat stoichiometry and physical properties in boreal undrained peatlands in Finland, Natural Resources Institute Finland (Version 1) [Dataset]. Zenodo. doi.org/<strong>10.5281/zenodo.10068486</strong></p><p>Turunen J. (2023). Surface peat data, Geological Survey of Finland (Version 1) [Data set]. Zenodo. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.8434148&data=05%7C01%7Cluke.tuula.larmola%40valtion.mail.onmicrosoft.com%7Cc48ffad4c0e341d0fa5808dbcaff0289%7C7c14dfa4c0fc47259f0476a443deb095%7C0%7C0%7C638326968887189768%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=AyYOxR7Mas2ef8y3wI7oCrzHWSyqBzt%2FJB0CMN%2BJUiU%3D&reserved=0">https://doi.org/10.5281/zenodo.8434148</a></p><p> </p><p><strong>Data column description</strong></p><p>ID - Site identifier</p><p>site - Forestry-drained peatland (FDP) for all rows</p><p>ncoord - North coordinate (latitude), degrees.</p><p>depth - Sampling depth. 30: 20-30 cm, 40: 30-40cm, avg: average of both depths.</p><p>type - Site type classification according to the Finnish peatland site type system.</p><p>origin – Origin of the FDP site type at undrained state. I: treed peatland (peat typically Sphagnum-wood), II: treeless peatland (or sparsely treed, peat typically Sphagnum-sedge)</p><p>type_num - Nutrient level according to site type. 1 is the most nutrient rich and 4 is the least.</p><p>Cmol - Molar carbon concentration in the sample</p><p>Hmol - Molar hydrogen concentration in the sample</p><p>Nmol - Molar nitrogen concentration in the sample</p><p>Omol - Molar oxygen concentration in the sample</p><p>Smol - Molar sulphur concentration in the sample</p><p>bd - Bulk density, kg/m3</p><p>cox - C oxidation state</p><p>or - Oxidative ratio</p><p>du - Degree of unsaturation</p><p>hc - H:C ratio</p><p>cn - C:N ratio</p><p>oc - O:C ratio</p><p>n - Number of samples. 2 for averages from both depths, 1 for all other rows.</p><p> </p><p><strong>References</strong></p><p>Hotanen JP, Maltamo M, Reinikainen A (2006) Canopy stratification in peatland forests in Finland. Silva Fennica 40:53–82.</p><p>Masiello CA, Gallagher ME, Randerson JT, Deco RM, Chadwick OA (2008) Evaluating two experimental approaches for measuring ecosystem carbon oxidation state and oxidative ratio, Journal of Geophysical Research 113, G03010, <a href="https://doi.org/10.1029/2007JG000534">https://doi.org/10.1029/2007JG000534</a></p><p>Turunen J, Anttila J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M, Alm J, Larmola T 2023. Impacts of forestry drainage on surface peat stoichiometry and physical properties in boreal peatlands in Finland. <i>manuscript.</i></p><p> </p>
Physical and chemical properties of soils on Watershed 5 of Hubbard Brook Experimental Forest, before and after whole-tree harvest
We sampled soils on watershed 5 at the Hubbard Brook Experimental Forest in 1983, prior to a whole-tree harvest conducted in the winter of 1983-84. We resampled in 1986, 1991, and 1998. All sampling was performed using a quantitative soil pit method. Samples of the combined Oi and Oe horizons; the Oa horizon; 0-10 cm, 10-20 cm, and >20 cm layers of mineral soil; and the C horizon were collected. Grab samples of pedogenic mineral horizons were also taken from the sides of a subset of pits in each year. Here we report soil chemistry, mass of soil, percent rock, bulk density, and organic matter. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Soil physical and chemical properties of gypsum & non-gypsum soils from the Chihuahuan and Mojave Deserts in 2023
This dataset contains data for soil physical and chemical properties of gypsum and non-gypsum soils in the northern Chihuahuan and eastern Mojave Deserts. Data were obtained from 20 study sites total, 10 located on soils derived from gypsum parent material and 10 located on soils derived from non-gypsum parent materials. Sites were grouped into 10 pairs, in which every gypsum site was partnered with a non-gypsum site located in the same region. Apart from soil type, partnered-site characteristics (topography, climate, elevation, slope, aspect, and presence of biocrusts) were held relatively constant. Site info and characteristics data can be accessed at knb-lter-jrn.210616001. Soil physical properties included: percent gravel, percent < 2mm fraction, soil aggregate stability, and soil compaction. Soil chemical properties were: percent gypsum content, pH, EC, and soil soluble concentrations of calcium, magnesium, potassium, sulfur, and phosphorus. The resulting soil data was used to understand physical and chemical differences between gypsum and non-gypsum soils and to examine how biocrust community types and moss species abundance and composition were associated with the measured soil variables. This study and dataset are complete.
DebDaB: A database of supraglacial debris thickness and physical properties
<p><strong>DebDaB: A database of supraglacial debris thickness and physical properties</strong></p> <p>DebdaB is a database of measured and reported physical properties and thickness of supraglacial debris that is openly available and open to community submissions.</p> <p>The majority of the database (90%) is compiled from 172 sources in the literature, and the remaining 10% has not been published before. DebDaB contains 8,286 data entries for supraglacial debris thickness, of which 1,852 entries also include sub-debris ablation rates, 167 data entries of thermal conductivity of debris, 157 of aerodynamic surface roughness length, 77 of debris albedo, 56 of debris emissivity and 37 of debris porosity. The data are distributed over 83 glaciers in 13 regions in the Global Terrestrial Network for Glaciers. </p> <p>This is version 2 of the dataset, corresponding to the revised version of the database after peer-review of its accompanying "Data descriptor manuscript" submitted for publication to the scientific journal "Earth System Science Data (ESSD)" from Copernicus Publications. The preprint is available at <a href="https://doi.org/10.5194/essd-2024-559">https://doi.org/10.5194/essd-2024-559 </a></p> <p>DebDaB version 2 consists of the following files:</p> <ul> <li>DebDaB_v2.zip : The actual DebDaB database, provided as a navigable Open Document Spreadsheet (.ods) with spreadsheet tabs for each of the debris properties. Additionally, the database is also provided as separate .csv files for each debris property, and as a GeoPackage (.gpkg). </li> <li>Readme_files.zip: A .txt file for each of the debris property tabs, describing all the fields in each tab. </li> <li>Templates_for_data_submission.zip: Templates (.csv files and additionally .xlsx files) for data submission for each of the debris properties in DebDaB. Data submissiosn to DebDaB should be sent to debriscoveredglaciers@ista.ac.at. </li> <li>DebDaB_data_sources.pdf: List of DebDaB sources from published literature. </li> <li>DebDaB_data_sources.bib: BibTeX list of DebDaB sources from published literature. </li> <li>Manuscript_codes.zip: The codes to download and process the data to generate the figures for data descriptor manuscript on ESSD.</li> </ul> <p>The data descriptor manuscript is in open review stage at: <a href="https://essd.copernicus.org/preprints/essd-2024-559/">https://essd.copernicus.org/preprints/essd-2024-559/ </a></p> <p><strong>DebDaB is open to new data submissions</strong>, and therefore future data submissions of previously unpublished data to DebDaB will entail co-authorship on the DebDaB database on Zenodo. </p> <p>According to the authors’ understanding of FAIR principles, authors of published literature and published data, that:</p> <ul> <li>Correct existing data within DebDaB, in case of errors</li> <li>Send the raw data from digitised figures</li> <li>Submit additional data that was previously unavailable (for example, accurate coordinates or additional data or metadata which is not already available)</li> </ul> <div>will have the right to be added as co-authors on the database in Zenodo. The authors are working to reevaluate their policies to conform to changes or unusual circumstances in authorship contributions, and are happy to involve eager people in the core team.</div> <div> </div> <div><strong>How to submit data: </strong>Please use the templates provided in the database files for data submissions and send it to debriscoveredglaciers@ista.ac.at. Authors who submit data will be asked to fill in a form regarding authorship contributions. </div> <p><strong>Important note on citations:</strong> DebDaB data users must cite the data descriptor manuscript (Fontrodona-Bach et al. 2025), the DebDaB zenodo repository<br>(Groeneveld et al., 2025), <strong>and the original data sources</strong> when using the database, given that DebDaB is mostly<br>a compilation of previously published data. To facilitate the citations of original data sources, each of the data entries in DebDaB contains the corresponding<br>original reference and corresponding DOI.</p> <p><strong>Manuscript citation:</strong> Fontrodona-Bach, A., Groeneveld, L., Miles, E., McCarthy, M., Shaw, T., Melo Velasco, V., and Pellicciotti, F.: DebDaB: A database of supraglacial debris thickness and physical properties, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-559, in review, 2025.</p> <p><strong>Zenodo citation:</strong> Groeneveld, L., Fontrodona-Bach, A., Miles, E., McCarthy, M., Melo Velasco, V., Shaw, T., Pellicciotti, F., Bauder, A., Buri, P., Kneib, M., Kumar, A., Mishra, A., & Petersen, L. (2025). DebDaB: A database of supraglacial debris thickness and physical properties (Version v2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14514803" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.</a><a href="https://doi.org/10.5281/zenodo.14224835" target="_blank" rel="noopener">14224835</a></p> <p><strong>Original data sources citation:</strong> See <em>DebDaB_data_sources.pdf</em> or <em>DebDaB_data_sources.bib</em></p> <p>The authors acknowledge the Debris-Covered Glaciers Working Group (DCGWG) from the International Association of Cryospheric Sciences (IACS) for setting the stage and drawing together the debris-covered glaciers community to focus on broader needs transcending a specific research topic, and starting the zenodo community on debris-covered glaciers, where this database is hosted. </p> <p><strong>Author contributions: </strong>The following spreadsheet states the contribution of each of the co-authors on the database: <br><a href="https://docs.google.com/spreadsheets/d/1nTieH_ZkwqnUpHQMYn7bygEcV5RzX4DJuqZ_qd-_PzE/edit?usp=sharing" target="_blank" rel="noopener">Author contributions statement (click here)</a></p> <p>A description of what each contribution field means is below:</p> <ul> <li><em>Conceptualisation:</em> This refers to the original idea and shaping of the database and is therefore closed.</li> <li><em>Data curation:</em> The data managers of DebDaB. Primarily the quality checks and curation done to all the collected published and unpublished data. It may also include authors who have compiled a lot of measurements from sources the authors did not have, and merged them into DebDaB, or if someone else takes on the role of ingesting/homogenizing data in the future.</li> <li><em>Data collection: </em>Field measurements as well as scouring past literature that the authors have missed, digitising sources, or advocating for old missing data sources to be entered into DebDaB.</li> <li><em>Formal analysis:</em> In the case of methods being applied to derive debris property values from other measurements, such as the case for surface roughness and thermal conductivity.</li> <li><em>Supervision/funding: </em>This refers to funding provided for the generation of DebDaB itself, but also funding for the data collection (measurements). </li> </ul>
Inferring size-based functional responses from the physical properties of the medium
<p>Databases used to test the model described in the article "Inferring size-based functional responses from the physical properties of the medium", Frontiers in Ecology and Evolution. Please read the "Readme.pdf" file for detailed information. This file explains all the variables and provides full references for the data in each of the datasets.</p> <p>"Portalier_et_al_2021_Species_Speeds.csv" provides species speeds according to body size for numerous species in aquatic systems.</p> <p>"Portalier_et_al_2021_Predator_Prey_Interactions.csv" provides attack rates, capture probabilities and handling times for numerous predator-prey interactions in aquatic systems.</p>
Effect of Restoration on Physical and Chemical Peat Properties in Previously Drained Boreal Peatlands, latitude 57-63, Sweden, 2021
The major objective behind peatland restoration is to improve ecosystem services, such as increased biodiversity, increased carbon sequestration, increased groundwater storage, and improved surface water quality. However, a century or more of drained conditions has drastically changed the soil properties in relation to natural wetlands and this is likely to profoundly influence the potential for various biogeochemical peat processes. Thus, peatland restoration may result in undesired impacts and potential environmental threats. Two such undesired effects are increased methane production and increased mercury methylation. In this study, we investigated how nine boreal peatlands across a latitudinal gradient in Sweden have been affected by rewetting after up to a century of drained conditions. Each peatland was sampled for three 50 cm deep peat cores that were analyzed for carbon, nitrogen, δ13C, δ15N, bulk density, and organic matter proportion. Adjacent to each restored peatland, we sampled a corresponding pristine (natural) peatland to facilitate a comparison of how the peat properties have been affected by drainage and subsequent rewetting of the peatlands. Groundwater depth was monitored at all peatland locations to confirm restored conditions at the rewetted peatlands. The results indicate that a long period of drained conditions and subsequent rewetting have changed the peat properties, with differences shown in C/N ratio, dry bulk density, and organic matter content. Rewetting will thus not regenerate a pristine environment. Instead, it creates new conditions to which various biogeochemical processes will respond and these do not necessarily represent conditions prior to disturbance. Our study will provide background information to understand the biogeochemical dynamics in peatlands after restoration, especially since the study covers a large span of nutrient conditions and catchment settings. This understanding will be fundamental for the development of strat
Baltimore Ecosystem Study: Physical, chemical and biological properties of forest and home lawn soils
Abstract: One-meter soil cores were taken to evaluate soil texture, bulk density, carbon and nitrogen pools, microbial biomass carbon and nitrogen content, microbial respiration, potential net nitrogen mineralization, potential net nitrification and inorganic nitrogen pools in 32 residential home lawns that differed by previous land use and age, but had similar soil types. These were compared to soils from 8 forested reference sites. Purpose: Soil cores were obtained from residential and forest sites in the Baltimore, MD USA metropolitan area. The residential sites were mostly within the Gwynns Falls Watershed (-76.012008W, -77.314183E, 39.724847N, 38.708367S and approximately 17 km2) Lawns on residential sites were dominated by a variety of cool season turfgrasses. Forest soil cores were taken from permanent forest plots of the Baltimore Ecosystem Study (BES) LTER (Groffman et al. 2006). These remnant forests are over 100 years old with soils that were comparable in type and texture to those underlying the residential study sites. Soils from all sites were from the Manor series (coarse-loamy, micaceous, mesic Typic Dystrudepts), which are well-drained upland soils with loamy textures and bedrock at 5 to 10 feet below the soil surface. To aid the site selection process we used neighborhoods in the Baltimore City metropolitan area that have been mapped using HERCULES, a high resolution land cover classification system designed to assist in the study of human-ecological systems (Cadenasso et al. 2007). Using HERCULES and additional data sources, we identified residential sites that were similar except for single factors that we hypothesized to be important predictors of ecosystem dynamics. These factors included land use history (agriculture and forest, n = 10 and n = 22), housing density (low and medium/high, n = 9 and n = 23), and housing age (4 to 58 yrs old, n = 32). Housing age was acquired from the Maryland Property View database. Prior land use was determined b
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): physical and chemical properties of soils, 2009-2017
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set includes measurements of moisture, bulk density, ash, carbon, and nitrogen concentrations, and carbon and nitrogen stable isotope composition (δ13C and δ15N) at depth increments in soil cores collected in 2009, 2010, 2011, 2013 and 2017 from warming and control treatment plots.
AGW01 Long-term measurement of groundwater physical and chemical properties from wells on watersheds N04D and N01B at Konza Prairie
In 1988 and 1990, 31 wells were installed at 20 sites within watershed N04D at Konza Prairie Biological Station to allow long-term monitoring of groundwater physical and chemical properties. Subsequently, four additional wells were installed in N04D in 1997, and five wells were installed in an adjacent watershed, N01B, in 2020. Most of the wells are nested to include wells completed in alluvium/colluvium and two Permian limestone units, the Morrill Limestone and the Eiss Limestone. The wells are completed along transects perpendicular to the main drainage in each watershed. Every four to six weeks, water samples and water depth measurements have been collected at subsets of the wells in N04D since 1991 and in N01B since 2021. At the same time, stream samples are collected from the outlet in N04D and N01B when the stream is flowing, as well as two upstream locations in N04D. Moreover, since 2024, stream samples are also collected at the same time from the outlet of a third watershed, N02B, when the steam is flowing. Methodologies have evolved since 1991 as detailed in the method description, though the primary goal of assessing groundwater and surface water major ion composition, pH, temperature, and groundwater level has been constant since 1991.
RCS01 Recovery and relative influence of root, microbial, and structural properties of soil on physically sequestered carbon stocks in restored grassland at Konza Prairie
Managing soil to sequester C can help mitigate increasing CO2 in the atmosphere. To maximize this ecosystem service, more knowledge of factors influencing C sequestration is needed. The objectives of this study were to (i) quantify recovery of the roots, microbial biomass and composition, and soil structure across a chronosequence of grassland restorations and (ii) use a structural equation model to develop a data-based hypothesis on the relative influence of physical and biological soil properties on the soil C aggregate fraction diagnostic of sequestered C. We hypothesized measured variables would recover with restoration age. Belowground plant biomass and tissue quality (C/N ratio), soil microbial biomass C, phospholipid fatty acid (PLFA) concentrations, soil structure, and soil C stocks in the bulk soil and each aggregate fraction were quantified from a cultivated field, prairies restored for 1 to 35-yr (n = 6), and a never-cultivated (native) prairie. Root biomass, microbial biomass C, arbuscular mycorrhizal fungi (AMF) PLFA biomass across the chronosequence increase to resemble native prairie following 35 yr of restoration. Many aspects of soil structure (i.e., bulk density, proportional mass of aggre- gate fractions, and aggregate mean weighted diameter) and the distribution C among soil fractions, including C in the micro-within-macro aggregate fraction (sequestered C), also became representative of native prairie within 35 yr of restoration. Total soil C stock and physically protected C increased at a similar rate (23 and 27 g C m-2 yr-1) respectively, across the chronosequence. After 35 yr of restoration, 50% of the total C pool was physically protected. The structural equation modeling developed by these data hypothesizes that microbial biomass C and AMF biomass (microbial composition) have the strongest causal influence on physically protected C. This model needs to be tested using independent sites to achieve greater inference.
AGW02 Measurement of groundwater physical and chemical properties from wells in contrasting land uses near Kings Creek, Konza Prairie
Wells were drilled in two sites on Konza Prairie Biological Station in April, 1993 approximately 100 m from Kings Creek. The two sites are located in a grassland and an agricultural area. The grassland site (K01A) is an old field that was planted with brome sometime prior to 1976. It has not been grazed for 25 years and is burned in spring every 1-2 years. The agricultural site is currently under a mix of cultivation and restoration plots. Historically, it was cultivated from sometime between 1939 and 1950 to the present. It is approximately 1 km downstream in an area geologically similar to K01A. The soil at both sites is mapped as Reading silt loam (fine, mixed, mesic Typic Arguidolls). Samples are taken monthly by PVC bailer following removal of 2 times the well volume. Samples are analyzed with same methods as used for LTER stream water chemistry.
Patterns in soil and physical properties of the Bisley Watersheds 1 and 2 (Big Dig 1988, Big Dig 1990)
(1) Exchangeable cation concentrations were measured using different soil extracting procedures (fresh soil and air-dried and ground soil) to establish a range of nutrient availability in the soil, and to determine the relationship between different, but commonly used laboratory protocols.(2) Soils extracted using fresh soils generally yielded significantly lower exchangeable Ca , Mg, and K concentrations than soils which were dried and ground prior to extraction. Soil nutrients generally decreased with depth in the soil.(3) Several soil properties varied predictably across the landscape and could be viewed in the context of a simple catena model. In the surface soils, exchangeable base cation concentrations and pH decreased along a gradient from ridge tops to riparian valleys, while soil organic matter, exchangeable Fe and acidity increased along this gradient. On the ridges, N,P, and K were positively correlated with soil organic matter; on slopes, N and P were positively correlated with organic matter, and Ca, Kg, and pH were negatively correlated with exchangeable Fe. (4) Soil nutrient availability in the upper catena appears to be primarily controlled by biotic processes, particularly the accumulation of organic matter. Periodic flooding and impeded drainage in the lower catena resulted in a more heterogeneous environment. Drying and grinding the soil prior to extraction had a greater impact on exchangeable cations from the upper catena than in the valley positions, probably due to greater soil organic matter content. See Silver, W.L., F.N. Scatena, A.H. Johnson, T.G. Siccama, and M.J. Sanchez. 1994. Nutrient availability in a montane wet tropical forest in Puerto Rico: spatial patterns and methodological considerations. Plant and Soil 164:129-145. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the Univ
The LILY Database: Linking Lithology to IODP Physical, Chemical, and Magnetic Properties Data
<p>During each expedition of the International Ocean Discovery Program and its precursor, the Integrated Ocean Drilling Program (jointly referred to as IODP), vast arrays of data are collected from drill cores. These data, which are accessible from the IODP LIMS (Laboratory Information Management System) database, include physical, chemical, and magnetic properties collected semi-continuously along cores using automated track systems, as well as a variety of analyses conducted on discrete subsamples taken from the cores. In addition, the lithology of all cores is described based on visual characteristics of the surface of split cores, visual examination of smear slides and thin sections, and compositional or mineralogical information derived from geochemical analyses. We extract basic lithologic information from this complex array of descriptive information and then tie that information to all other measurements. This new database is referred to as <strong>LI</strong>MS with <strong>L</strong>itholog<strong>y</strong> (LILY). LILY currently contains over 34 million data from 89 km of core recovered on 42 expeditions conducted 2009-2019. Some uses of LILY include identifying the abundance of different lithologies, finding data from core intervals with a specific lithology, assessing the efficacy of coring systems in different lithologies, or characterizing and analyzing physical, chemical, and magnetic properties based on lithology. We illustrate the use of LILY by computing the grain density by lithology from over 24,000 moisture and density measurements and then use those grain densities, along with the large IODP bulk density dataset, to compute a new high-resolution porosity dataset with over 3.7 million new porosity estimates.</p> <h2>CONTENT DESCRIPTION:</h2> <p><strong>The main LILY database is stored in the files with the suffix DataLITH.csv.</strong> Each file contains IODP LIMS data with lithology and other metadata added. The file prefix gives the type of data. For example, AVS_DataLITH.csv contains the Automated Vane Shear (AVS) shear strength data paired with lithology and other metadata. A list of all data types is given in Supporting Information Table S1 of Childress et al. (2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>). There are a total of 23 DataLITH files.</p> <ul> <li>AVS_DataLITH.csv: automated vane shear; shear strength measurements.</li> <li>CARB_DataLITH.csv: total carbon, hydrogen, nitrogen, and sulfur, inorganic carbon (carbonate), and organic carbon measured on discrete samples.</li> <li>GE_DataLITH.csv: gas elements from gas chromatography.</li> <li>GRA_DataLITH.csv: gamma ray attenuation bulk density from the Whole-Round Multisensor Logger (WRMSL).</li> <li>ICP_DataLITH.csv: Inductively-coupled plasma data.</li> <li>IW_DataLITH.csv: interstitial water chemistry.</li> <li>JR6A_DataLITH.csv: discrete magnetic measurements from the JR6A spinner magnetometer.</li> <li>KAPPA_DataLITH.csv: Kappabridge susceptibility meter measurements.</li> <li>MAD_DataLITH.csv: moisture and density from discrete samples.</li> <li>MS_DataLITH.csv: magnetic susceptibility from the WRMSL.</li> <li>MSP_DataLITH.csv: point magnetic susceptibility from the Section Half Multisensor Core Logger (SHMSL).</li> <li>NGR_DataLITH.csv: natural gamma radiation from the Natural Gamma Radiation Logger (NGRL).</li> <li>PEN_DataLITH.csv: pocket penetrometer compressional strength measurements.</li> <li>PWB_DataLITH.csv: P-wave velocity from the bayonet system.</li> <li>PWC_DataLITH.csv: P-wave velocity from the caliper system.</li> <li>PWL_DataLITH.csv: P-wave velocity from the WRMSL.</li> <li>RGB_DataLITH.csv: Red-Green-Blue color from the Section Half Imaging Logger (SHIL).</li> <li>RSC_DataLITH.csv: reflectance spectroscopy from the SHMSL.</li> <li>SRA_DataLITH.csv: source rock analyzer measurements.</li> <li>SRM_DataLITH.csv: Superconducting Rock Magnetometer (SRM) measurements of split-core sections.</li> <li>SRMD_DataLITH.csv: SRM measurements of discrete samples.</li> <li>TCON_DataLITH.csv: thermal conductivity measured with the Teka Berlin TK04 probe.</li> <li>TOR_DataLITH.csv: Torvane shear strength measurements.</li> </ul> <p>Other compressed data folders contain multiple files used in creating the LILY database:</p> <p>RawDESC.zip: Contains 7,940 .csv files derived from the raw text content of the DESClogik Excel worksheets that was extracted, converted to comma separated value (.csv) format, and put into files with a consistent naming convention, without applying any corrections or conversions to the original text. Each file is the direct extraction of a tab from the DESC workbooks, available at <a href="https://web.iodp.tamu.edu/DESCReport/">https://web.iodp.tamu.edu/DESCReport/</a></p> <p>CoreSUMM.zip: Contains one file with Core Summary information, which includes the expedition, site, hole, core, coring type, top and bottom depths drilled, advances and recoveries, time and date of recovery, and the number of sections. These data are further paired with additional metadata (expanded core type, latitude, longitude, and water depth). Coordinates and water depth for each hole are derived from LIMS (and the JANUS database at <a href="http://www-odp.tamu.edu/database/">http://www-odp.tamu.edu/database/</a> for older expeditions).</p> <p>RawDATA.zip: Contains the raw track/discrete dataset downloaded by expedition from IODP LIMS database and placed in folders for each type of data (AVS, CARB, SRM, etc.) </p> <p>RawLITH.zip: Contains 42 .csv files, with one file for each expedition. Each file contains all lithologic description (prefix, principal and suffix, etc.) information for an entire expedition, as it was originally described. These have been transformed to a consistent format and paired with consistent identification information and additional metadata. Headers are normalized across all expeditions and SampleID information is standardized.</p> <p>CleanLITH: Contains 42 .csv files. Each file contains all lithologic description (prefix, principal and suffix) information for an entire expedition. The lithologic descriptions have been standardized to a consistent nomenclature using the dictionary given in Support Information Table S4 of Childress et al. (2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>). These data are further paired with additional metadata (e.g., degree of consolidation, expanded core type, latitude, longitude, and water depth).</p> <h2>GitHub Repository:</h2> <ul> <li>Contains a few notebooks to demonstrate how to work with the LILY database</li> <li><a href="https://github.com/IODP/LILY">IODP LILY GitHub Repository</a></li> </ul>
A "short blanket" dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the underlying many-body physics?
<p>Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials.<br> In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram.<br> We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water but provides a less accurate description of the vapor-liquid equilibrium properties.<br> This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions.<br> An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits the opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties but losing accuracy in the description of the liquid properties.<br> These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict properties for state points that are not explicitly included in the training process.<br> The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties.</p>
Soil physical and chemical properties based on genetic horizon from 4 replicate pits placed around the replicate LTER control plots sampled in 1988 and 1989.
Dataset contains the following soil properties for each genetic horizon - site, Soil pit, upper and lower boundary (cm), Mg meq/100gm, Ca meq/100gm, K meq/100gm, CEC meq/100gm, pH, %C, %sand, %silt, %clay, Total %N, Total %P, % organic matter, Mn meq/100gm, Available-P ppm, %CO3, bulk density gm/cm3, Volume wt gm/m2.
Patterns of and controls over nitrogen inputs by green alder (Alnus viridis spp. fruticosa) to a secondary successional chronosequence in interior Alaska II - Soil Physical and Chemical Properties
In September of 1999 we collected soil cores to identify stage, replicate stand, canopy, and soil horizon patterns of soil physical (color, bulk density, pH) and chemical (N, C, P) parameters.
Soil chemical and physical property data for Green Lakes Valley, 1986.
Laboratory analyses were performed on soil samples from the Green Lakes Valley in the City of Boulder Watershed in order to characterize the chemical characteristics of the soil resource. The samples were collected during the 1986 excavations that were required during installation of zero-tension soil solution samplers.
Chemico-physical soil properties in Lilium pomponium populations
<p>Chemico-physical soil properties in <em>Lilium pomponium</em> populations. Soil data includes silt (g/kg), clay (g/kg), sand (g/kg), total nitrogen (g/kg), active carbonate (g/kg), total carbonate (g/kg), available extractable Ca (meq/100g), cation exchange capacity (meq/100g), electrical conductivity (1:5, dS.m-1), available extractable P (Olsen method, mg/kg), available extractable Mg (meq/100g), pH, available extractable K (meq/100g), carbon/nitrogen ratio, available extractable Na (meq/100g), organic matter (g/kg). Data of populations are reported in relevé locality data table</p>
Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Database of Physical gene-gene Interactions in young adult C.elegans.
<p>This repository contains Supplementary Information for manuscript Suriyalaksh et al Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes corresponding to the curation of physical gene-gene interactions for young adult C elegans worms </p> <p>We manually curated 239,001 regulatory interactions from 289 young adult wild-type (WT) C.elegans datasets, consisting of 126 genes and 495 unique transcription factors (see TableS1_datasets_for_prior.csv for references). </p> <p>This repository contains 3 different files:</p> <p>TableS1_datasets_for_prior.csv - contains datasets used as sources for physical gene-gene or TF-gene interactions</p> <p>TableS2_physical_priors.xlsx - contains three tabs:<br> ChIPATAC - contains physical TF-gene interactions from 115 L4 or young-adult ChIP-seq datasets from modERN (Kudron et al., 2018) + ChIP-seq datasets (GSE28350, GSE81521) from (Hochbaum et. al, 2011, Li et. al, 2016).</p> <p>eY1HATAC- contains 3,501 TF-gene interactions from eY1H assay by Fuxman Bass et al. (2016).</p> <p>motifATAC - contains 202 unique TF DNA recognition motifs using “direct evidence” option from CiS-BP motif database (Weirauch et al., 2014), obtained through RTFBSDB R package (Wang et al., 2016) - see TableS1</p> <p>TableS3_WT_functional_priors.csv - contains functional knockdown data that we use as gold standard to validate inferred networks in Suriyalaksh et al. (see TableS1_datasets_for_prior.csv for sources)</p> <p>---</p> <p>Description of methodology to obtain regulatory interactions in TableS2:</p> <p>Regulatory sequences for each gene were acquired from ENSEMBL (Aken et al., 2017), obtained using biomaRt R package (accessed on 31st Oct 2017). This study used WBcel235/ce11 version of the C. elegans genome, and WormBase WS260 genome annotations.</p> <p>For motifs, TFs whose motifs overlapped with an open ATAC-seq region by at least one base pair were kept. For ChIP-seq, TF binding sites that overlapped with an open ATAC-seq region by at least one base pair were kept using bedtools intersect and bedtools merge commands.</p> <p>An interaction from a TF to a gene was inferred by aligning transcription start sites (TSS) using bedtools window commands with 1000 bp window size to the TF-binding locations from ChIP-seq and motifs.</p> <p>For eY1H data, an interaction is included if the TSS site of the target gene overlaps with an open ATAC-seq region by at least one base pair.</p> <p>For gene-gene interactions, of the 298 studies compiled in WormExp v1.0 database (Yang et al, 2016, updated 27/07/16), 98 studies were included in the database spanning 126 different genes (see Table S1 in this repository).</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.