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8,120 results for “Long term”
SBC LTER: Reef: Long-term experiment: Kelp removal: Urchin size frequency distribution
These data describe the size frequency distribution of red (Mesocentrotus franciscanus) and purple (Strongylocentrotus purpuratus) sea urchins within permanent plots of a long-term experiment designed to examine trajectories of change in the structure and productivity of kelp forest communities in response to changes in the frequency and severity of disturbance to giant kelp. The diameter of the test (shell without spines) was recorded to the nearest 0.5 cm for 50 red and 50 purple sea urchins located within a 40 m x 2 m area of each plot. Size frequency data of red and purple sea urchins are not collected in the continual kelp removal plots. When combined with size-mass relationships established in the laboratory these data were used to provide a non-destructive, in situ estimate of the dry mass per unit area of bottom for each species. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel and included an annual kelp removal treatment designed to simulate increases in the frequency and severity of winter wave disturbance and a continual kelp removal treatment that allowed the effects of giant kelp on the community to be evaluated. The last experimental removals of giant kelp occurred in winter 2016 or winter 2017, depending on the site. Data collection continued in all plots until spring 2023 to document the recovery trajectory of the reef fish community following the cessation of experimental kelp removal.
SBC LTER: Reef: Long-term experiment: Kelp removal: Understory kelp allometrics
These data consist of morphometric measurements of the understory kelps Pterygophora californica and Laminaria farlowii used to estimate their mass within permanent plots of a long-term experiment designed to examine trajectories of change in the structure and productivity of kelp forest communities in response to changes in the frequency and severity of disturbance to giant kelp. The mass of individual P. californica is estimated from the number of blades having a length > 30 cm, whereas the mass of L. farlowii is estimated from the length of its single blade. Measurements are recorded for up to 30 individuals of each species within the 40 m x 2 m sampling area of each plot. These data are combined with allometric relationships established in the laboratory to provide non-destructive, in situ estimates of the dry mass per unit area of bottom for each species.
SBC LTER: Reef: Long-term experiment: Kelp removal: Cover of sessile organisms, Uniform Point Contact
These data describe the percent cover of sessile invertebrates and understory macroalgae within permanent plots of a long-term experiment designed to examine trajectories of change in the structure and productivity of kelp forest communities in response to changes in the frequency and severity of disturbance to giant kelp. Percent cover was determined using a uniform point contact method that consists of noting the identity and relative vertical position of all organisms under 80 uniformly placed points located within a 1 m wide band centered on permanent 40 m transects in each sampling plot. Each species may only be recorded once per point. Using this method, the percent cover of all species combined may exceed 100%, however, the maximum percent cover possible for any single species cannot exceed 100%. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel and included an annual kelp removal treatment designed to simulate increases in the frequency and severity of winter wave disturbance and a continual kelp removal treatment that allowed the effects of giant kelp on the community to be evaluated. The last experimental removals of giant kelp occurred in winter 2016 or winter 2017, depending on the site. Data collection continued in all plots until spring 2023 to document the recovery trajectory of the reef fish community following the cessation of experimental kelp removal.
SBC LTER: Reef: Long-term experiment: Kelp removal: Abundance and size of Giant Kelp
These data describe the abundance and size of giant kelp (Macrocystis pyrifera) within permanent plots of a long-term experiment designed to examine trajectories of change in the structure and productivity of kelp forest communities in response to changes in the frequency and severity of disturbance to giant kelp. The number of giant kelp > 1 m tall were recorded within four contiguous 20 m x 1m permanent plots located within a 40 m x 2 m area. The number of fronds > 1 m tall were counted for each individual and used as an estimate of its size. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel and included an annual kelp removal treatment designed to simulate increases in the frequency and severity of winter wave disturbance and a continual kelp removal treatment that allowed the effects of giant kelp on the community to be evaluated. The last experimental removals of giant kelp occurred in winter 2016 or winter 2017, depending on the site. Data collection continued in all plots until spring 2023 to document the recovery trajectory of the reef fish community following the cessation of experimental kelp removal.
SBC LTER: Reef: Long-term experiment: Kelp removal: Fish abundance
These data describe the abundance and size of reef-associated fish within permanent plots of a long-term experiment designed to examine trajectories of change in the structure and productivity of kelp forest communities in response to changes in the frequency and severity of disturbance to giant kelp. The number, size and species identity of reef fish were recorded within a 2 m wide swath centered along a 40 m long transect extending up to 2 m off the bottom. Fish size was measured as total length estimated to the nearest cm. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel and included an annual kelp removal treatment designed to simulate increases in the frequency and severity of winter wave disturbance and a continual kelp removal treatment that allowed the effects of giant kelp on the community to be evaluated. The last experimental removals of giant kelp occurred in winter 2016 or winter 2017, depending on the site. Data collection continued in all plots until spring 2023 to document the recovery trajectory of the reef fish community following the cessation of experimental kelp removal.
SBC LTER: Reef: Long-term experiment: Kelp removal: Invertebrate and algal density
These data describe the abundance of common reef associated species of macro invertebrates and macroalgae within permanent plots of a long-term experiment designed to examine trajectories of change in the structure and productivity of kelp forest communities in response to changes in the frequency and severity of disturbance to giant kelp. The number of individuals of approximately 50 taxa were recorded by divers along 40 m transects within each plot. Small species of macroalgae and macinvertebrates were counted within six permanent 1 m2 quadrats positioned uniformly along the 40 m transect, while larger species were counted within four contiguous 20 m2 sub-sections of each 40 m x 2 m transect. Also included at the quadrat scale are estimates of an average size-related measurement of each species, which was developed specifically for each species for the purpose of estimating its biomass. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel and included an annual kelp removal treatment designed to simulate increases in the frequency and severity of winter wave disturbance and a continual kelp removal treatment that allowed the effects of giant kelp on the community to be evaluated. The last experimental removals of giant kelp occurred in winter 2016 or winter 2017, depending on the site. Data collection continued in all plots until spring 2023 to document the recovery trajectory of the reef fish community following the cessation of experimental kelp removal.
Long-Term Core Site Grasshopper Dynamics for the Sevilleta National Wildlife Refuge, New Mexico
Grasshoppers are important animals in semi-arid environments, both as herbivores and as food resources for higher level consumer animals. Grasshoppers tend to be numerous and speciose in semi-arid environments, especially desert grasslands, where they range from environmental specialists to environmental generalists. Grasshopper populations tend to change considerably from year to year, often in response to annual variation in rainfall and plant production. The purpose of this study was to monitor grasshopper species composition and abundance over large temporal and spatial dimentions which include black grama grassland, blue grama grassland, creosotebush shrubland, and pinyon/juniper woodland environments at the Sevilleta, in relation to seasonal and annual variation in precipitation and plant production. Data were collected for all individual species to provide information on community dynamics as well as population dynamics, starting in 1992 and continuing to the present. The working research hypothesis for this study was that grasshopper populations in all environments will correlate positively to seasonal and annual variation in precipitation and plant production. Spring grasshopper populations will be especially high during El Nino years, and late summer populations especially high during La Nina years.
Demographic data from long-term symbiont removal experiments with grasses and Epichloë fungal endophytes
This project was designed to understand the demographic effects of vertically transmitted fungal endophytes (Epichloë spp.) on their grass hosts. The experiment includes seven host-symbiont taxonomic pairs: Agrostis perennans - E. amarillans, Elymus villosus - E. elymi, Elymus virginicus - E. elymi or EviTG-1, Festuca subverticillata - E. starrii, Poa alsodes - E. alsodes, Poa sylvestris - E. PsyTG-1, Schedonorus arundinaceus - E. coenophiala. Experimental plots were established at the Indiana University Lilly-Dickey Woods Research and Teaching Preserve in south-central Indiana, USA in 2007. For each species, 5-10 plots were planted with naturally symbiotic (S+) hosts, and 5-10 plots were plated with hosts that were disinfected of fungal endophytes by heat treatment (S-). Over 15 years (2007-2022) we collected demographic data on the survival, growth, reproduction, and recruitment of all plants in all plots. Beginning in 2018 we also collected data on the locations of all plants in every plot.
Nitrogen budget in a Chihuahuan desert grassland long-term nitrogen fertilization experiment at the Sevilleta National Wildlife Refuge
Although the negative consequences of increased nitrogen (N) supply on plant communities and soil chemistry are well known, most studies have focused on mesic grasslands, and the fate of added N in arid and semi-arid ecosystems remains unclear. To study the impacts of long-term increased N deposition on ecosystem N-pools, we sampled a 26-year-long fertilization (10 g N m-2 yr-1) experiment in the northern Chihuahuan Desert at the Sevilleta National Wildlife Refuge (SNWR) in New Mexico. To determine the fate of the added N, we measured multiple soil, microbial, and plant N pools in shallow soils at three time points across the 2020 growing season.
Long-Term Trends in Seagrass Metabolism Data from Figures Covering 2007-2018
This dataset contains the data used to create figures 2, 4 and 5 in: Berger, A.C., Berg, P., McGlathery, K.J. and Delgard, M.L. (2020), Long-term trends and resilience of seagrass metabolism: A decadal aquatic eddy covariance study. Limnol Oceanogr, 65: 1423-1438. https://doi.org/10.1002/lno.11397
Long-term Memory (LTM) for famous Faces, Places, and common Objects
Open the record for dataset details and reuse information.
Genome-Wide DNA Methylation in Peripheral Blood and Long-Term Exposure to Source-Specific Transportation Noise and Air Pollution: The SAPALDIA Study (Supplementary Data)
<p>The zip file contains supplementary data for the publication - Genome-Wide DNA Methylation in Peripheral Blood and Long-Term Exposure to Source-Specific Transportation Noise and Air Pollution: The SAPALDIA Study, accepted for publication in Environmental Health Perspectives (DOI: 10.1289/EHP6174).</p> <p>The description of the files are noted below:</p> <p><strong>1. Readme File for SAPALDIA Noise and Air Pollution EWAS Single Exposure.zip </strong></p> <p>This zip file contains all the results of the association between source-specific transportation noise (aircraft, railway and road traffic), air pollution (NO<sub>2</sub> and PM<sub>2.5</sub>), and genome-wide DNA methylation, derived from multi-exposure models.</p> <p><strong>SAPALDIA_EWAS_SingleExposure_AircraftLden.txt</strong> contains the results for aircraft noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_RailwayLden.txt</strong> contains the results for railway noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_RoadtrafficLden.txt</strong> contains the results for road traffic noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_NO2.txt</strong> contains the results for nitrogen dioxide</p> <p><strong>SAPALDIA_EWAS_SingleExposure_PM25.txt</strong> contains the results for fine particulate matter</p> <p> </p> <p><strong>General footnote for all files:</strong>SAPALDIA: Swiss cohort study on air pollution and lung and heart diseases in adults. CpG: Cytosine-phosphate-Guanine. CHR: chromosome. SE: standard error. Lden: day-evening-night noise level. NO<sub>2</sub>: nitrogen dioxide. PM<sub>2.5</sub>: particulate matter with aerodynamic diameter <2.5 µm. Beta coefficients represent increase or decrease in DNA methylation per 10 dB increase in aircraft, railway or road traffic Lden or 10 µg/m<sup>3</sup> increase in NO<sub>2</sub> or PM<sub>2.5</sub>. All estimates were from single exposure epigenome-wide linear mixed models, with random intercept at the level of participant. Each model was adjusted for age, sex, educational level, area, and neighborhood socio-economic status, greenness index, smoking status and pack years, exposure to passive smoke, consumption of fruits, vegetables and alcohol, nested study, asthma status, survey, source-specific noise truncation indicator (for Lden models) and leukocyte composition. In a preliminary step, DNA methylation β-values were regressed on the Illumina control probe-derived first 30 principal components to correct for correlation structures and technical bias, and residuals of these regressions covering 430,477 CpGs were used as the technical bias-corrected methylation level at the CpG sites.</p> <p>Extreme values of the residuals (lying beyond three times the interquartile range below the first quartile and above the third quartile at each CpG site) were replaced with their corresponding detection threshold value (“modified winsorization”). The “winsorized” data were then used as the dependent variables in the epigenome-wide association study.</p> <p> </p> <p><strong>2. Readme File for SAPALDIA Noise and Air Pollution EWAS Multi Exposure.zip </strong></p> <p>This zip file contains all the results of the association between source-specific transportation noise (aircraft, railway and road traffic), air pollution (NO<sub>2</sub> and PM<sub>2.5</sub>), and genome-wide DNA methylation, derived from multi-exposure models.</p> <p><strong>SAPALDIA_EWAS_MultiExposure_AircraftLden.txt</strong> contains the results for aircraft noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_RailwayLden.txt</strong> contains the results for railway noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_RoadtrafficLden.txt</strong> contains the results for road traffic noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_NO2.txt</strong> contains the results for nitrogen dioxide</p> <p><strong>SAPALDIA_EWAS_MultiExposure_PM25.txt</strong> contains the results for fine particulate matter</p> <p><strong>General table footnotes: </strong>SAPALDIA: Swiss cohort study on air pollution and lung and heart diseases in adults. CpG: Cytosine-phosphate-Guanine. CHR: chromosome. SE: standard error. Lden: day-evening-night noise level. NO<sub>2</sub>: nitrogen dioxide. PM<sub>2.5</sub>: particulate matter with aerodynamic diameter <2.5 µm. Beta coefficients represent increase or decrease in DNA methylation per 10 dB increase in aircraft, railway or road traffic Lden or 10 µg/m<sup>3</sup> increase in NO<sub>2</sub> or PM<sub>2.5</sub>. All estimates were from multi-exposure epigenome-wide linear mixed models, with random intercept at the level of participant, and were adjusted for age, sex, educational level, area, and neighborhood socio-economic status, greenness index, smoking status and pack years, exposure to passive smoke, consumption of fruits, vegetables and alcohol, nested study, asthma status, survey, source-specific noise truncation indicator and leukocyte composition. Multi-exposure models included all five exposures (Aircraft, railway, road traffic Lden and respective truncation indicators, NO<sub>2</sub> and PM<sub>2.5</sub>) at the same time. In a preliminary step, DNA methylation β-values were regressed on the Illumina control probe-derived first 30 principal components to correct for correlation structures and technical bias, and residuals of these regressions covering 430,477 CpGs were used as the technical bias-corrected methylation level at the CpG sites. Extreme values of the residuals (lying beyond three times the interquartile range below the first quartile and above the third quartile at each CpG site) were replaced with their corresponding detection threshold value (“modified winsorization”). The “winsorized” data were then used as the dependent variables in the epigenome-wide association study.</p>
A long term hourly eddy covariance dataset of consistently processed CO2 and H2O Fluxes from the Tibetan Alpine Steppe at Nam Co (2005 - 2019)
<p>The data set contains nearly 15 years of eddy covariance data from an alpine steppe ecosystem on the central Tibetan Plateau. The data was processed following standardized quality control methods to allow for comparability between the different years of our record and with other data sets. To ensure meaningful estimates of ecosystem atmosphere exchange, careful application of the following correction procedures and analyses was necessary: (1) Due to the remote location, continuous maintenance of the eddy covariance (EC) system was not always possible, so that cleaning and calibration of the sensors was performed irregularly. Furthermore, the high proportion of bare soil and high wind speeds led to accumulation of dirt in the measurement path of the infrared gas analyzer (IRGA). The installation of the sensor in such a challenging environment resulted in a considerable drift in CO2 and H2O gas density measurements. If not accounted for, this concentration bias may distort the estimation of the carbon uptake. We applied a modified drift correction procedure following Fratini et al. (2014) which, instead of a linear interpolation between calibration dates, uses the CO2 concentration measurements from the Mt. Waliguan atmospheric observatory as reference time series. (2) We applied rigorous quality filtering of the calculated fluxes to retain only fluxes which represent actual physical processes. (3) During the long measurement period, there were several buildings constructed in the near vicinity of the EC system. We investigated the influence of these obstacles on the turbulent flow regime to identify fluxes with uncertain land cover contribution and exclude them from subsequent computations. (4) We calculated the de-facto standard correction for instrument surface heating during cold conditions (hereafter called sensor self heating correction) following Burba et al. (2008) and a revision of the original method following Frank and Massman (2020). (5) Subsequently, we applied the traditional and widely used gap filling procedure following Reichstein et al. (2005) to provide a more complete overview of the annual net ecosystem CO2 exchange. (6) We estimated the flux uncertainty by calculating the random flux error (RE) following Finkelstein and Sims (2001) and by using the standard deviation of the fluxes used for gap filling (NEE_fsd) as a measure for spatial and temporal variation.</p> <p>References:</p> <ol> <li>Burba, G. G., McDermitt, D. K., Grelle, A., Anderson, D., and XU, L. (2008). Addressing the influence of instrument surface heat exchange on the measurements of CO2 flux from open-path gas analyzers, Global Change Biology, 14, 1854-1876, <a href="https://doi.org/10.1111/j.1365-2486.2008.01606.x">https://doi.org/10.1111/j.1365-2486.2008.01606.x</a>.</li> <li>Finkelstein, P. L. and Sims, P. F. (2001). Sampling error in eddy correlation flux measurements, J. Geophys. Res. Atmos., 106, 3503–3509, doi:10.1029/2000JD900731.</li> <li>Frank, J. M. and Massman, W. J.: A new perspective on the open-path infrared gas analyzer self-heating correction, Agricultural and Forest Meteorology, 290, 107986, doi:10.1016/j.agrformet.2020.107986, 2020.</li> <li>Fratini, G., McDermitt, D. K., and Papale, D. (2004). Eddy-covariance flux errors due to biases in gas concentration measurements: origins, quantification and correction, Biogeosciences, 11, 1037-1051, <a href="https://doi.org/10.5194/bg-11-1037-2014">https://doi.org/10.5194/bg-11-1037-2014</a>.</li> <li>Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-m., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and valentini, R. (20050. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Global Change Biology, 11, 1424-1439, <a href="https://doi.org/10.1111/j.1365-2486.2005.001002.x">https://doi.org/10.1111/j.1365-2486.2005.001002.x</a>.</li> </ol>
IPBES Data Management Tutorials - Session 3.7: Data management report details: Long-term storage details
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management reports </em>chapter provides an overview and discussion of specific elements of IPBES data management reports.</p> <p>This session, <em>Data management report details: Long-term storage details</em>, explores the reasons why IPBES recommends Zenodo as a long-term repository. </p>
Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyvaskyla for 2030 and 2050
<p>******************* Please view the README.txt or README.md file for detailed documentation of data. ********************</p> <p>Title: Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyväskylä for 2030 and 2050</p> <p>Date of release: 25/11/2020</p> <p>Identifier: 10.5281/zenodo.4275759</p> <p>Permalink: http://dx.doi.org/10.5281/zenodo.4275759</p> <p>Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J.; Ruusunen, M. Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model, <strong><em>Under Review</em></strong>, 2020.</p> <p>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files.</p> <p><br> Contact information: Jari Pulkkinen, University of Oulu, Oulu, Finland, jari.pulkkinen@oulu.fi; Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi<br> </p> <p>Dates of data: 2030, 2050</p> <p>Type of data: Outdoor Temperature</p> <p>Geographic location: Jyväskylä</p> <p>Time resolution: hourly, full year</p> <p>Format: All data is stored in .csv files</p> <p>Number of files: 1 .zip --> 50 files + README.txt + README.md</p> <p>This directory contains the following datasets: A summary of all the files has been compiled and stored in the "README.txt" and "README.md" files</p> <p> </p> <p>Notifications:</p> <p>Contains modified Copernicus Climate Change Service (C3S) information [2018] and modified Finnish Meteorological Institute [2017,2019] information from etsin.fairdata.fi and from Open data repository (https://en.ilmatieteenlaitos.fi/open-data).</p> <p><br> Contains modified Climate One Building information [2019] (reference Lawrie L.K. and Crawley D.B. 2019) and Test Reference Year 2012 (TRY2012) information from Jylhä et al. [2011] and Jylhä et al. [2015] (Energy demand for the heating and cooling of residential houses in Finland in a changing climate).</p> <p>Contains modified Ruosteenoja et al. [2016] information.</p> <p>Other data and information sources are described in README.txt, README.md, references and on the associated publication.</p>
Data from: Structure and dynamics of secondary and mature rainforests: insights from South Asian long-term monitoring plots
<p><strong>1) DESCRIPTION </strong></p> <p>The dataset contains annual woody stems (shrubs and trees) census data collected from two long-term ecological monitoring plots spanning one hectare each in the Anamalai Hills of the Southern Western Ghats, India. These two plots represent one situated in a mature forest located within relatively undisturbed rainforest of the Anamalai Tiger Reserve (ATR) and one in secondary forest on the Valparai Plateau, respectively. Both plots have been censused and measured from 2017 to 2022 following the standardized protocol (RAINFOR-GEM, Marthews et al. 2014).</p> <p><br><strong>2) CONTACTS</strong></p> <p>CONTACT #1<br>1. Name: Akhil Murali<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 82812 97441<br>4. Email address: akhil@ncf-india.org<br>5. ORCID: 0000-0001-6149-6458</p> <p>CONTACT #2<br>1. Name: Srinivasan Kasinathan<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: srini@ncf-india.org<br>5. ORCID: 0000-0001-7323-6653 </p> <p>CONTACT #3<br>1. Name: Kshama Bhat<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: kshama@ncf-india.org<br>5. ORCID: 000-0002-6190-2687</p> <p>CONTACT #4 <br>1. Name: Jayashree Ratnam <br>2. Work Address: National Centre for Biological Sciences, TIFR, Bellary Road, Bengaluru 560065, Karnataka, India<br>3. Work Phone: +91 80 23666001 <br>4. Email address: jratnam@ncbs.res.in <br>5. ORCID: 0000-0002-6568-8374</p> <p>CONTACT #5<br>1. Name: Mahesh Sankaran <br>2. Work Address: National Centre for Biological Sciences, TIFR, Bellary Road, Bengaluru 560065, Karnataka, India<br>3. Work Phone: +91 80 23666001<br>4. Email address: mahesh@ncbs.res.in <br>5. ORCID: 0000-0002-1661-6542</p> <p>CONTACT #6<br>1. Name: Divya Mudappa<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: divya@ncf-india.org<br>5. ORCID: 0000-0001-9708-4826</p> <p>CONTACT #7<br>1. Name: T. R. Shankar Raman<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: trsr@ncf-india.org<br>5. ORCID: 0000-0002-1347-3953</p> <p>CONTACT #8<br>1. Name: Anand M Osuri <br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: aosuri@ncf-india.org <br>5. ORCID: 0000-0001-9909-5633</p> <p><br><strong>3) GEOGRAPHIC COVERAGE and SITE DESCRIPTION</strong></p> <p>a) Site type: : Tropical Forest<br>b) Geography: : Anamalai Tiger Reserve, Southern Western Ghats.<br>c) Habit: : Mid elevation Wet evergreen Forest<br>d) Site History: : </p> <p>i) MANAMBOLI- The Mature Forest plot (10.357748° N, 76.889747° E; 825 m asl) is situated within a relatively undisturbed 200-hectare mid-elevation tropical wet evergreen rainforest tract at the core of the Anamalai Tiger Reserve (ATR). This area has been protected from logging and other significant disturbances since its establishment as a protected area in 1979.</p> <p>ii) CANDURA- The Secondary Forest plot (10.30855411° N, 76.83391853° E; 875 m asl) is situated within a 124-hectare rainforest remnant on the Valparai Plateau: the Candura rainforest remnant. The Candura site experienced episodic selective logging in the 1990s and early 2000s, with the last logging episode occurring in 2004. In the early 2000s, the understorey of the remnant was cleared for Vanilla (Vanilla planifolia) cultivation in the central and southern parts (abandoned in 2007), robusta coffee (Coffea canephora) in the northwestern corner (abandoned in the early 2000s), and pepper in 21 hectares in the northeastern part (established in 2015, abandoned in 2021).</p> <p>Climate: Humid tropical with about 2400 mm rainfall annually, falling mainly during the southwest monsoon.</p> <p><br><strong>4) TEMPORAL COVERAGE</strong></p> <p>a) Begins: 2017-11-30 (Year, Month, Day)<br>b) Ends: 2022-11-12 (Year, Month, Day)</p> <p><br>5) SAMPLING DESIGN AND METHODS </p> <p>a) Plot Design: Each 1 ha plot of 100 m × 100 m, sub-divided into 100 continuous sub-plots of 10 m × 10 m, was surveyed and mapped to maximum accuracy using a theodolite in the field, with grid corners permanently staked. <br>b) Data collection period and frequency: After the plot establishment in NOvember -- December 2017, the plots were recensused each year (around November). <br>c) Research Methods: All woody plant individuals with girth at breast height (GBH, at 1.3 m) ≥10 cm were tagged with numbered aluminum tags and spatially mapped. Plant species were identified using standard floral keys. Stem GBH was measured for all single stemmed individuals. For trees with buttresses, the GBH point of measurement (POM) was taken at 50 cm above the buttresses or at the height where the stem is regular. New saplings that recruited into the ≥10 cm GBH class were identified, mapped, tagged, and added to the monitoring. Stems that appeared to be dead were recorded at each monitoring and those that showed no signs of recovery in subsequent visits were recorded as mortality.</p> <p><br><strong>6) FILES INCLUDED</strong></p> <p>The dataset includes the following 9 files, whose details and contents are explained below. (Wherever used in the various files, NA implies not available.)</p> <p>01_README.txt<br>Metadata (this file) including information on the dataset explaining associated files and their contents.</p> <p>02_Candura_annual_census.csv <br>This contains the Annual census data with the following column headings: <br>site: Site name (Can = Candura)<br>cno: Census Number (1 = 2017, 2 = 2018..., 6 = 2022)<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ht1: Point of measurement at 1.3 m above the ground or 50 cm above the top of the highest buttress or stilt root (POM1)<br>c1: Alive status of the stem at the POM1 (coded according Marthews et al. 2014, page: 97)<br>g1: Stem girth at POM1 (in centimetre)<br>ht2: 20 cm above the ht1 or point of measurement 2 (POM2) recording girth at which the dendroband is attached<br>c2: Alive status of the stem at the POM2 (coded acording Marthews et al. 2014, page: 97)<br>g2: Girth at POM2 (in centimetre)<br>dyn: whether the dendroband is attached to the tree or not (y-Yes, n-No)<br>da: alive status of stem (d-dead,a-alive)<br>remarks: remarks or notes</p> <p>03_Manamboly_annual_census.csv<br>This contains Annual census data with the following column headings: <br>site: Site name (Man = Manamboli)<br>cno: Census Number (1 = 2017, 2 = 2018..., 6 = 2022)<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ht1: Point of measurement at 1.3 m above the ground or 50 cm above the top of the highest buttress or stilt root (POM1)<br>c1: Alive status of the stem at the POM1 (coded according Marthews et al. 2014, page: 97)<br>g1: Stem girth at POM1 (in centimetre)<br>ht2: 20 cm above the ht1 or point of measurement 2 (POM2) recording girth at which the dendroband is attached<br>c2: Alive status of the stem at the POM2 (coded acording Marthews et al. 2014, page: 97)<br>g2: Girth at POM2 (in centimetre)<br>dyn: whether the dendroband is attached to the tree or not (y-Yes, n-No)<br>da: alive status of stem (d-dead,a-alive)<br>remarks: remarks or notes</p> <p>04_Candura_vernier.csv<br>This file has the girth measurement of trees with lianas where digital vernier calipers were used to measure stem diameter since it was not possible to measure stem girth using measuring tape.<br>site: Site name (Can = Candura)<br>cno: Census Number<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>vern1_d1 First measure of diameter at POM1 (in millimetre) <br>vern2_d1 Second measure of diameter at POM1 (in millimetre) <br>vern3_d1 Third measure of diameter at POM1 (in millimetre) <br>calc_g1: Girth at POM1 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>vern1_d2 First measure of diameter at POM2 (in millimetre) <br>vern2_d2 Second measure of diameter at POM2 (in millimetre) <br>vern3_d2 Third measure of diameter at POM2 (in millimetre) <br>calc_g2 Girth at POM2 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>Remarks Remarks and notes</p> <p>05_Manamboli_vernier.csv<br>This file has the girth measurement of trees with lianas where digital vernier calipers were used to measure stem diameter since it was not possible to measure stem girth using measuring tape.<br>site: Site name (Man = Manamboli)<br>cno: Census Number<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>vern1_d1 First measure of diameter at POM1 (in millimetre) <br>vern2_d1 Second measure of diameter at POM1 (in millimetre) <br>vern3_d1 Third measure of diameter at POM1 (in millimetre) <br>calc_g1: Girth at POM1 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>vern1_d2 First measure of diameter at POM2 (in millimetre) <br>vern2_d2 Second measure of diameter at POM2 (in millimetre) <br>vern3_d2 Third measure of diameter at POM2 (in millimetre) <br>calc_g2 Girth at POM2 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>Remarks Remarks and notes</p> <p>06_Candura_Height_data.csv<br>This contains data on the heights of individual trees in plot as measured in 2018.<br>site: Site name (Can = Candura)<br>ymd: Date in YYYY/MM/DD format (Year Month Day)<br>gno: Grid Number: <br>tno: unique tag number: <br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes: <br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>height: Height of tree in metres<br>remarks: Remarks: and notes</p> <p>07_Manamboli_Height_data.csv<br>This contains data on the heights of individual trees in plot as measured in 2018.<br>site: Site name (Man = Manamboli)<br>ymd: Date in YYYY-MM-DD format (Year Month Day)<br>gno: Grid Number: <br>tno: unique tag number: <br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes: <br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>height: Height of tree in metres<br>remarks: Remarks: and notes</p> <p>08_Species_name_match.csv<br>This file provides the combined list of species codes updated taxonomy and successional guild. Scientific names were updated to current taxonomy using the species name matching tool of the Global Biodiversity Information Facility, GBIF (www.gbif.org).<br>sps: Species codes<br>query : Scientific name of the plant at the time of data collection: <br>scientificName: : with auther citation: <br>key: GBIF key<br>rank: Taxonomic rank or level of identification (GENUS, SPECIES)<br>kingdom: Taxonomic Kingdom (plants) provided by GBIF name matching tool: <br>phylum: Taxonomic Phylum provided by GBIF name matching tool<br>class: Taxonomic Class provided by GBIF name matching tool<br>order: Taxonomic Order provided by GBIF name matching tool<br>family: Taxonomic Family provided by GBIF name matching tool<br>genus: Taxonomic Genus provided by GBIF name matching tool<br>botanical_name: Updated scientific name of the species provided by GBIF name matching tool<br>habt_new: Successional guild of the species (Mature = mature forest species; Secondary = secondary successional species; Int - Introduced species)</p> <p>09_R_scrpt_for_manuscript.R<br>Text file with code in the R statistical and programming environment (www.r-project.org).</p> <p><br><strong>Reference</strong><br>Marthews TR, Riutta T, Oliveras Menor I, Urrutia R, Moore S, Metcalfe D, Malhi Y, Phillips O, Huaraca Huasco W, Ruiz Jaén M, Girardin C, Butt N, Cain R and colleagues from the RAINFOR and GEM networks (2014). Measuring Tropical Forest Carbon Allocation and Cycling: A RAINFOR-GEM Field Manual for Intensive Census Plots (v3.0). Manual, Global Ecosystems Monitoring network, http: //gem.tropicalforests.ox.ac.uk/.</p> <p> </p>
Supplementary material to: Long-term (bio)deterioration of Fe-containing and Fe-depleted sandstones: An experimental insight into biotic and abiotic interactions.
<p>This dataset includes: micorphotographs, scanning electron microscope images and related EDS spectra, thermal analysis (DSC-TG), grain size distribution. Abbreviations used in the supplementary file names refer to: GMB (growth medium inoculated with the bacteria, Pseudomonas fluorescens), GM (sterile growth medium), ARE (artificial root exudates), H2O (water), NR (Sample Nowa Ruda), Z (Sample Żerkowice ŻR).</p>
Datasets from study: "Land surface observations boost temperature forecast skill: experiments using Long Short-Term Memory surrogate for physics-based models to assess potential predictability"
<p>This repository contains the datasets needed to reproduce the figures from manuscript: Land surface observations boost temperature forecast skill: experiments using Long Short-Term Memory surrogate for physics-based models to assess potential predictability.</p> <p>In this study, we examine the potential of land surface temperature and vegetation data, which are not routinely assimilated in NWP models, for enhancing temperature forecast skill. We build surrogate models for NWP using Long Short-Term Memory.</p>
Global GFED-based monthly burned area time series (1996-2016) at 1 km and ESA CCI MODIS-based long-term monthly P90 burned area occurrence at 500 m
<p>Contains two separate datasets:</p> <ol> <li>Global <a href="https://www.globalfiredata.org/data.html">GFED-based monthly burned area</a> (in ha) <a href="https://youtu.be/kBJcP8mL2Qs">time series (1996-2016)</a> at 1 km (downscaled using cubic-splines from 25 km);</li> <li>Global burned area long term (2000-2012) P90 (quantile probability = 0.9) based on the <a href="http://maps.elie.ucl.ac.be/CCI/viewer/index.php">ESA CCI burned area accumulated weekly product</a>;</li> </ol> <p>Original GFED monthly data is provided as HDF4 files (ftp.fuoco.geog.umd.edu/data/GFED/GFED4). Dataset is described in detail in <a href="https://doi.org/10.1002/jgrg.20042">Giglio et al. (2013)</a>. Processing steps are available <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/GFED"><strong>here</strong></a>. Antarctica is not included.</p> <p>To access and visualize global datasets use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a> or watch <a href="https://youtu.be/kBJcP8mL2Qs"><strong>this video</strong></a>.</p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> </ul> <p>All files provided as Cloud-Optimized GeoTIFFs / internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>nhz = theme: natural hazards,</li> <li>monthly.burned.ha = variable: estimated monthly burned area in ha,</li> <li>gfed = data source GFED data,</li> <li>m = mean value,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000.02 = time reference aggregated: month Feb of year 2000,</li> <li>v4 = version number: GFEDv4,</li> </ul>
Data to support the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362
<p>Soil organic carbon content and water content at the different pressure points, as measured by Ioanna Panagea for the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362 from the the long term experiments belonging in some of the SoilCare project partners. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.