Find research datasets worth reusing

Long-term Plant Biomass Monitoring Data from the Georgia Coastal Ecosystems LTER Project on Sapelo Island, Georgia

The Georgia Coastal Ecosystems LTER program (GCE) monitors plant biomass annually with the goal of testing the hypothesis that end-of-year biomass varies as a function of 1) freshwater discharge from the Altamaha River (especially in low-marsh plots), 2) local rainfall (especially in high-marsh plots), and 3) average sea level. In 2000 we created permanent plots at all 10 GCE marsh monitoring sites. Plots were established at creek-bank and mid-marsh sites (8 plots per zone per site). Most sites are dominated by Spartina alterniflora (smooth cordgrass), but zones at some sites are dominated by Juncus roemerianus, Spartina cynosuroides, or Zizaniopsis miliacea. An additional marsh zone (high marsh Juncus) was established at site 10 in 2005 and site 9 in 2012 to increase replication of sites with Juncus. Plants have been non-destructively monitored in October of every year from 2000 to the present, measuring the stem count, height and flowering status of every plant in each plot. Stem clipping samples were also collected adjacent to plots in 2002, 2007, and 2020, then measured, dried, weighed and statistically analyzed in order to generate allometric regression relationships between height and mass for estimation of plant biomass in corresponding plots. This data set includes cumulative long-term observations of plant stem count, height and biomass per marsh zone, plot and species at 10 GCE LTER sampling sites from 2000 to 2023, and will be updated annually to include the prior year observations.

openCC (other)Mar 2024View details →

Long-term Hydrographic Mooring Data from the Georgia Coastal Ecosystems LTER Salinity Monitoring Program - Primary 30 Minute Observational Data

Conductivity, temperature and sub-surface water pressure were measured continuously at fixed hydrographic moorings distributed across the Georgia Coastal Ecosystems LTER study area to document spatial and temporal variability of salinity and its relationship to water level and river discharge. Mooring locations were chosen to span the salinity gradient as well as to take advantage of existing physical infrastructure (e.g. docks or pilings) for mounting instruments and proximity to marsh study sites. Eight moorings were established between 2001 and 2003 to characterize salinity patterns in the three primary sounds in the GCE domain (Sapelo, Doboy and Altamaha), and a ninth mooring was added near a freshwater tidal forest along the Altamaha River in 2014. Observations were logged at 30 minute intervals by Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately quarterly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms, and short-duration gaps (<6 hours) due to instrument swaps, quality control analysis or brief data interruptions were filled by interpolation. Long-duration gaps due to instrument or mooring loss were filled with null values to produce a monotonic time series. This data set includes cumulative 30 minute observations at all 9 moorings through 31-Dec-2022, and will be updated annually to include observations from the prior year.

openCC (other)Mar 2024View details →

Long-term Hydrographic Mooring Data from the Georgia Coastal Ecosystems LTER Salinity Monitoring Program - Daily Summarized Data

Conductivity, temperature and sub-surface water pressure were measured continuously at fixed hydrographic moorings distributed across the Georgia Coastal Ecosystems LTER study area to document spatial and temporal variability of salinity and its relationship to water level and river discharge. Mooring locations were chosen to span the salinity gradient as well as to take advantage of existing physical infrastructure (e.g. docks or pilings) for mounting instruments and proximity to marsh study sites. Eight moorings were established between 2001 and 2003 to characterize salinity patterns in the three primary sounds in the GCE domain (Sapelo, Doboy and Altamaha), and a ninth mooring was added near a freshwater tidal forest along the Altamaha River in 2014. Observations were logged at 30 minute intervals by Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately quarterly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms, and short-duration gaps (<6 hours) due to instrument swaps, quality control analysis or brief data interruptions were filled by interpolation. Long-duration gaps due to instrument or mooring loss were filled with null values to produce a monotonic time series. Values flagged as invalid were then removed and interpolated up to 6 hours, and daily-summarized values were calculated by statistical aggregation. This data set includes the daily-summarized data at all 9 moorings through 31-Dec-2022, and will be updated annually to include observations from the prior year.

Long-term Mollusc Population Abundance and Size Data from the Georgia Coastal Ecosystems LTER Fall Marsh Monitoring Program

This data set includes long-term observational data on mollusc species abundance and size distribution at 10 Georgia Coastal Ecosystems marsh sites used for annual plant and invertebrate population monitoring. Infaunal and epifaunal molluscs were hand-collected from within quadrats of known area in mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites annually in October. Molluscs were also collected from an additional high marsh Juncus zone (n = 4 quadrats) at several sites beginning in 2009. The molluscs were returned to the lab, preserved in ethanol, identified and counted to determine species abundance and density in each plot. The length of each measurable individual was then determined using calipers or an ocular micrometer mounted in a stereomicroscope to determine mollusc size. Population abundance and size measurement data are reported separately by site, zone, plot and species because analyses were performed at different times, specimens were not individually identifiable, and not all individuals were measureable. This data set includes cumulative long-term observations from 2000 to 2022, and will be updated annually to include the prior year observations.

Long-term Burrowing Crab Population Abundance Data from the Georgia Coastal Ecosystems LTER Fall Marsh Monitoring Program

This data set includes long-term observational data on burrowing crab abundance at 10 Georgia Coastal Ecosystems marsh sites used for annual plant and invertebrate population monitoring. Crab abundance was determined by performing surveys of crab hole occurance within replicate 625 square centimeter quadrats and converting the counts to number per square meter. Surveys were performed annually during October within the mid-marsh and creek bank zones at GCE marsh study sites 1 through 10 (i.e. n = 4 per zone at each site). Surveys were also performed in an additional high marsh Juncus zone at several sites beginning in 2009 (i.e. n = 4 quadrats per site). Note that this census method does not differentiate which species made a particular hole and therefore only estimates total burrowing crab abundance, potentially including species Uca pugnax, Uca minax, Uca pugilator, Armases cinereum, Eurytium limosum, Sesarma reticulatum and Panopeus spp. Crab holes that are not actively maintained are quickly covered by tidal activity and other sediment disturbances, therefore plugged holes were assumed to be unoccupied and excluded from the counts. This data set includes cumulative observations from 2000 to 2023, and will be updated annually to include the prior year observations.

Long-term Plant Biomass Monitoring Data from Altamaha River Plant Transition Sites near the Georgia Coastal Ecosystems LTER Project on Sapelo Island, Georgia

The Georgia Coastal Ecosystems LTER program (GCE) monitors plant biomass annually to measure the species and size distribution of plants at 3 sampling sites on the creekbank of the Altamaha River. The sites were chosen to capture the transition from Spartina alterniflora to Spartina cynosuroides (site SCSA) and the transition from Spartina cynosuroides to Zizaniopsis miliacea (sites ZSC1 and ZSC2). The quadrats were established as permanent plots in October 2012 by placing PVC stakes along the creekbank at each site. Plots were evenly spaced, but were not randomly located because the goal was to start with mixtures of vegetation in most of the plots, and vegetation was distributed in patches along the creekbanks. Therefore, these plots provide useful measures of vegetation change, but are not a random sample of the vegetation at the site. Plots will be replaced each year as necessary to replace any lost to disturbance. The plots were visually surveyed and the species, shoot height, and flowering status was recorded individually for each shoot over 10 cm in height present in each plot. Observations from plots exhibiting signs of disturbance were noted in a separate data set (PLT-GCEM-1801c). This data set includes cumulative long-term observations of plant stem count, height and biomass per plot and species at 3 Altamaha River transition sites from 2012 to 2023, and will be updated annually to include the prior year observations.

opencc-by-4.0Apr 2023View details →

zenodo52/100

Belvedere Glacier long-term monitoring Open Data

Introduction This dataset contains extensive, long-term monitoring data on the Belvedere Glacier, a debris-covered glacier located on the east face of Monte Rosa in the Anzasca Valley of the Italian Alps. The data is derived from photogrammetric 3D reconstruction of the full Belvedere Glacier and includes: <ul> <li>dense point clouds obtained with UAV-based MVS covering the entire glacier body</li> <li>high-resolution orthophotos</li> <li>high-resolution DEMs</li> </ul> Since 2015, in-situ survey of the glacier have been conducted annually using fixed-wing UAVs until 2020 and quadcopters from 2021 to 2022 to remotely sense the glacier and build high-resolution photogrammetric models. A set of ground control points (GCPs) were materialized all over the glacier area, both inside the glacier and along the moraines, and surveyed (nearly-) yearly with topographic-grade GNSS receivers (Ioli et al., 2022). For the period from 1977 to 2001, historical analog images, digitalized with photogrammetric scanners and acquired from aerial platforms, were used in combination with GCPs obtained from recent photogrammetric models (De Gaetani et al., 2021). Before downloading them, you can explore the photogrammetric point clouds of the Belvedere Glacier within web app based on Potree from <a href="https://thebelvedereglacier.it/" target="_blank" rel="noopener">https://thebelvedereglacier.it/</a> (use a web browser from a desktop/laptop for the best experience). Additionally, from here you can also visualize and download the coordinates of the GCPs measured by GNSS every year since 2015.   Belvedere Glacier The Belvedere Glacier is an important temperate alpine glacier located on the east face of Monte Rosa in the Anzasca Valley of Italy. The Belvedere Glacier is of particular importance among alpine glaciers because it is a debris-covered glacier and it reaches its lowest elevation at about 1800 m a.s.l. Over the last century, the Belvedere Glacier has experienced extraordinary dynamics, such as a surge-like movement or the formation of a supraglacial lake, which seriously threatened the nearby community of Macugnaga.   Data organization The data are organized by year in compressed zip folders named belvedere_YYYY.zip, which can be downloaded independently. Each folder contains all data available for that year (i.e. photogrammetric point clouds,  orthophotos, and DEMs) and the corresponding metadata. Metadata is provided as a .json file which contains all the main information for data usage. Point clouds are saved in compressed las format (.laz) and they can be inspected e.g., with CloudCompare. Orthophotos and DEMs are georeferenced images (.tif) that can be inspected with any GIS software (e.g., QGIS). Large point clouds are subdivided into regular tiles, which are numbered in a progressive row-wise order from the bottom-left corner of the point cloud bounding box. All the files are named according to the following naming schema: "belv_YYYY_surveyplatform_datatype[_resolution][vertical_datum][-tile_number].extension" where:  <ul> <li>YYYY: is the year of the survey</li> <li>surveyplatform: can be either "uav" for the UAV-based photogrammetry survey or "histo" for the historical aerial datasets.</li> <li>datatype: can be either "pcd" for point clouds, "orthophoto" for orthophotos and "dsm" for DSMs. </li> <li>resolution: on-ground resolution of each pixel in meters. This applies only to raster data (orthophoto and DSMs)</li> <li>vertical_datum: if the DSM is given in orthometric coordinates, the label "ortho" is present in the filename, otherwise the height of the dataset is supposed to be ellipsoidal.</li> <li>tile: tile number, if the data is tiled to avoid large files.</li> </ul> Data Usage This dataset can be used to estimate glacier velocities, volume variations, study geomorphological processes such as the process of moraine collapse, or derive other information on glacier dynamics. If you have any requests on the data provided, data acquisition, or the raw data themselves, you are encouraged to contact us.   Contributions The monitoring activity carried out on the Belvedere Glacier was designed and conducted jointly by the Department of Civil and Environmental Engineering (DICA) of Politecnico di Milano and the Department of Environment, Land and Infrastructure Engineering (DIATI) of Politecnico di Torino. The DREAM projects (DRone tEchnnology for wAter resources and hydrologic hazard Monitoring), involving teachers and students from Alta Scuola Politecnica (ASP) of Politecnico di Torino and Milano, contributed to the campaign from 2015 to 2017.   Acknowledgements <div>The authors thank CGR SpA for digitizing the historical images (1977, 1991, 2001, 2009) and making them available to the authors for the photogrammetric processing.</div> <div>The authors thank all students and collaborators contributing to the Alta Scuola Politecnica projects DREAM 1, DREAM 2, and DREAM 3 (DRone tEchnnology for wAter resources and hydrologic hazard Monitoring). </div> <div> </div> <div> </div> If you use the data, please, cite these our pubblications: Ioli, F., Dematteis, N., Giordan, D., Nex, F., Pinto, L., Deep Learning Low-cost Photogrammetry for 4D Short-term Glacier Dynamics Monitoring. PFG (2024). <a href="https://doi.org/10.1007/s41064-023-00272-w" target="_blank" rel="noopener">https://doi.org/10.1007/s41064-023-00272-w</a> Ioli, F.; Bianchi, A.; Cina, A.; De Michele, C.; Maschio, P.; Passoni, D.; Pinto, L. Mid-Term Monitoring of Glacier’s Variations with UAVs: The Example of the Belvedere Glacier. Remote Sensing, 14, 28 (2022). <a href="https://doi.org/10.3390/rs14010028" target="_blank" rel="noopener">https://doi.org/10.3390/rs14010028</a> De Gaetani, C.I.; Ioli, F.; Pinto, L. Aerial and UAV Images for Photogrammetric Analysis of Belvedere Glacier Evolution in the Period 1977–2019. Remote Sensing, 13, 3787 (2021). <a href="https://doi.org/10.3390/rs13183787" target="_blank" rel="noopener">https://doi.org/10.3390/rs13183787</a>

zenodo48/100

Data from: Structure and dynamics of secondary and mature rainforests: insights from South Asian long-term monitoring plots

1) DESCRIPTION  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). 2) CONTACTS CONTACT #1 1. Name: Akhil Murali 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 82812 97441 4. Email address: akhil@ncf-india.org 5. ORCID: 0000-0001-6149-6458 CONTACT #2 1. Name: Srinivasan Kasinathan 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: srini@ncf-india.org 5. ORCID: 0000-0001-7323-6653  CONTACT #3 1. Name: Kshama Bhat 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: kshama@ncf-india.org 5. ORCID: 000-0002-6190-2687 CONTACT #4  1. Name: Jayashree Ratnam  2. Work Address: National Centre for Biological Sciences, TIFR, Bellary Road, Bengaluru 560065, Karnataka, India 3. Work Phone: +91 80 23666001  4. Email address: jratnam@ncbs.res.in  5. ORCID: 0000-0002-6568-8374 CONTACT #5 1. Name: Mahesh Sankaran  2. Work Address: National Centre for Biological Sciences, TIFR, Bellary Road, Bengaluru 560065, Karnataka, India 3. Work Phone: +91 80 23666001 4. Email address: mahesh@ncbs.res.in  5. ORCID: 0000-0002-1661-6542 CONTACT #6 1. Name: Divya Mudappa 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: divya@ncf-india.org 5. ORCID: 0000-0001-9708-4826 CONTACT #7 1. Name: T. R. Shankar Raman 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: trsr@ncf-india.org 5. ORCID: 0000-0002-1347-3953 CONTACT #8 1. Name: Anand M Osuri  2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: aosuri@ncf-india.org  5. ORCID: 0000-0001-9909-5633 3) GEOGRAPHIC COVERAGE and SITE DESCRIPTION a) Site type: : Tropical Forest b) Geography: : Anamalai Tiger Reserve, Southern Western Ghats. c) Habit: : Mid elevation Wet evergreen Forest d) Site History: :  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. 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). Climate: Humid tropical with about 2400 mm rainfall annually, falling mainly during the southwest monsoon. 4) TEMPORAL COVERAGE a) Begins: 2017-11-30 (Year, Month, Day) b) Ends: 2022-11-12 (Year, Month, Day) 5) SAMPLING DESIGN AND METHODS  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.  b) Data collection period and frequency: After the plot establishment in NOvember -- December 2017, the plots were recensused each year (around November).  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. 6) FILES INCLUDED The dataset includes the following 9 files, whose details and contents are explained below. (Wherever used in the various files, NA implies not available.) 01_README.txt Metadata (this file) including information on the dataset explaining associated files and their contents. 02_Candura_annual_census.csv  This contains the Annual census data with the following column headings:  site: Site name (Can = Candura) cno: Census Number (1 = 2017, 2 = 2018..., 6 = 2022) ymd: Date in DD-MM-YYYY format (Day Month Year) gno: Grid Number tno: Unique tag number for the plant pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals) sps: Species name as codes lx: X coordinate of tree in the 10 m × 10 m subplot (in metres) ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres) 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) c1: Alive status of the stem at the POM1 (coded according Marthews et al. 2014, page: 97) g1: Stem girth at POM1 (in centimetre) ht2: 20 cm above the ht1 or point of measurement 2 (POM2) recording girth at which the dendroband is attached c2: Alive status of the stem at the POM2 (coded acording Marthews et al. 2014, page: 97) g2: Girth at POM2 (in centimetre) dyn: whether the dendroband is attached to the tree or not (y-Yes, n-No) da: alive status of stem (d-dead,a-alive) remarks: remarks or notes 03_Manamboly_annual_census.csv This contains Annual census data with the following column headings:  site: Site name (Man = Manamboli) cno: Census Number (1 = 2017, 2 = 2018..., 6 = 2022) ymd: Date in DD-MM-YYYY format (Day Month Year) gno: Grid Number tno: Unique tag number for the plant pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals) sps: Species name as codes lx: X coordinate of tree in the 10 m × 10 m subplot (in metres) ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres) 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) c1: Alive status of the stem at the POM1 (coded according Marthews et al. 2014, page: 97) g1: Stem girth at POM1 (in centimetre) ht2: 20 cm above the ht1 or point of measurement 2 (POM2) recording girth at which the dendroband is attached c2: Alive status of the stem at the POM2 (coded acording Marthews et al. 2014, page: 97) g2: Girth at POM2 (in centimetre) dyn: whether the dendroband is attached to the tree or not (y-Yes, n-No) da: alive status of stem (d-dead,a-alive) remarks: remarks or notes 04_Candura_vernier.csv 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. site: Site name (Can = Candura) cno: Census Number ymd: Date in DD-MM-YYYY format (Day Month Year) gno: Grid Number tno: Unique tag number for the plant pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals) sps: Species name as codes vern1_d1 First measure of diameter at POM1 (in millimetre)  vern2_d1 Second measure of diameter at POM1 (in millimetre)  vern3_d1 Third measure of diameter at POM1 (in millimetre)  calc_g1: Girth at POM1 (in centimetre; calculated using the averaged value as diameter from the three measurements) vern1_d2 First measure of diameter at POM2 (in millimetre)  vern2_d2 Second measure of diameter at POM2 (in millimetre)  vern3_d2 Third measure of diameter at POM2 (in millimetre)  calc_g2 Girth at POM2 (in centimetre; calculated using the averaged value as diameter from the three measurements) Remarks Remarks and notes 05_Manamboli_vernier.csv 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. site: Site name (Man = Manamboli) cno: Census Number ymd: Date in DD-MM-YYYY format (Day Month Year) gno: Grid Number tno: Unique tag number for the plant pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals) sps: Species name as codes vern1_d1 First measure of diameter at POM1 (in millimetre)  vern2_d1 Second measure of diameter at POM1 (in millimetre)  vern3_d1 Third measure of diameter at POM1 (in millimetre)  calc_g1: Girth at POM1 (in centimetre; calculated using the averaged value as diameter from the three measurements) vern1_d2 First measure of diameter at POM2 (in millimetre)  vern2_d2 Second measure of diameter at POM2 (in millimetre)  vern3_d2 Third measure of diameter at POM2 (in millimetre)  calc_g2 Girth at POM2 (in centimetre; calculated using the averaged value as diameter from the three measurements) Remarks Remarks and notes 06_Candura_Height_data.csv This contains data on the heights of individual trees in plot as measured in 2018. site: Site name (Can = Candura) ymd: Date in YYYY/MM/DD format (Year Month Day) gno: Grid Number:  tno: unique tag number:  pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals) sps: Species name as codes:  lx: X coordinate of tree in the 10 m × 10 m subplot (in metres) ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres) height: Height of tree in metres remarks: Remarks: and notes 07_Manamboli_Height_data.csv This contains data on the heights of individual trees in plot as measured in 2018. site: Site name (Man = Manamboli) ymd: Date in YYYY-MM-DD format (Year Month Day) gno: Grid Number:  tno: unique tag number:  pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals) sps: Species name as codes:  lx: X coordinate of tree in the 10 m × 10 m subplot (in metres) ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres) height: Height of tree in metres remarks: Remarks: and notes 08_Species_name_match.csv 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). sps: Species codes query : Scientific name of the plant at the time of data collection:  scientificName: : with auther citation:  key: GBIF key rank: Taxonomic rank or level of identification (GENUS, SPECIES) kingdom: Taxonomic Kingdom (plants) provided by GBIF name matching tool:  phylum: Taxonomic Phylum provided by GBIF name matching tool class: Taxonomic Class provided by GBIF name matching tool order: Taxonomic Order provided by GBIF name matching tool family: Taxonomic Family provided by GBIF name matching tool genus: Taxonomic Genus provided by GBIF name matching tool botanical_name: Updated scientific name of the species provided by GBIF name matching tool habt_new: Successional guild of the species (Mature = mature forest species; Secondary = secondary successional species; Int - Introduced species) 09_R_scrpt_for_manuscript.R Text file with code in the R statistical and programming environment (www.r-project.org). Reference 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/.

opencc-by-4.0Dec 2023View details →

zenodo44/100

Long-term moss monitoring network for atmospheric deposition in Germany, link to research data and scientific software

Research data and scientific software related to a study that aims to restructure a long-term monitoring network using moss as biomonitor for atmospheric deposition in Germany. Data from the European Moss Survey 2005 and a statistically based methodology including a decision support system were used to design the spatial network for the 2005 survey.

opencc-by-4.0Jan 2017View details →

edi44/100

FISHPASS ASSESSMENT PLAN LONG-TERM MONITORING OF HYDROLOGIC AND WATER QUALITY DATA

The Great Lakes Fishery Commissions’ (GLFC) FishPass project seeks to reconnect the waterscape for only desired species (i.e., selective passage) by integrating a multitude of existing and novel passage techniques and technologies. The probability of a fish passing through a sorting system is dependent on environmental conditions and a fish’s motivation ─ its internal state in relation to environmental stimuli. While fish decision making abilities introduce complexity to the sorting operations, they also provide an opportunity to exploit behavioral tendencies and abilities to achieve selective sorting. The FishPass Assessment Plan details a monitoring program aimed at quantifying fish movement and sorting capabilities associated with both individual mechanisms and integrated sorting systems. The results of the monitoring program will be used to inform future adjustments to the selection of techniques and technologies and their configuration to optimize passage of desirable species while blocking and/or removing undesirable species. A key component to the Assessment Plan is the long-term monitoring of abiotic variables in and around FishPass. This data set contains the hydrologic (e.g., river discharge, water level) and water quality data (e.g., temperature, specific conductivity, conductivity, and turbidity) collected at mostly static stations throughout the Boardman/Ottaway River. The dataset is updated annually. These data are collected until the initiation and/or substantial completion of the FishPass structure. Collection of this type of data are expected to continue after FishPass construction completion but modifications to the extent and location of monitoring stations are anticipated. As a result, a new dataset will be updated in the future containing all long term hydrologic and water quality monitoring post construction. R. Swanson, GLFC Assessment Biologist, is primarily responsible for maintaining the monitoring equipment, data retrieval, quality assuran

openCC (other)Jan 2026View details →

edi44/100

Lake ice surveys, 1874-2022, Adirondack Long-Term Ecological Monitoring Program Project No. 8 by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative.

The objective of this dataset is to document ice-in and ice-out dates on several lakes on the State University of New York College of Environmental Science and Forestry's Huntington Wildlife Forest (HWF). Lakes include: Arbutus, Catlin, Deer, Military, Rich, Wolf and Lodo Pond; some records exist for Long Pond and other water bodies but they are not included here except in some comment fields.

openCC (other)Dec 2022View details →

edi44/100

Throw trap and electrofishing data collected during 1996–2022 from the Everglades, Florida, United States for the publication "Contrasting invasion histories and effects of three non-native fishes observed with long-term monitoring data"

This dataset was used to analyze the effects of three non-native fishes in the Florida Everglades for a publication in the journal Biological Invasions. The dataset incorporates plot-level mean densities (# of individuals per square meter) of common aquatic animals collected during 1996–2022 from 17 sites across three regions of the Everglades: Taylor Slough, Shark River Slough, and Water Conservation Area 3A. Prey species included are nine common small fishes and three common decapod species (two crayfish species and grass shrimp). The dataset includes throw trap data on three predator taxa: African Jewelfish (Hemichromis letourneuxi), Mayan Cichlids (Mayaheros uruphthalmus), and sunfishes (Lepomis spp.). Annual indices of mean wet season electrofishing catch-per-unit-effort of Asian Swamp Eels (Monopterus albus/javanesis), Mayan Cichlids, sunfishes, and the three other large 'top predator' fishes (Amia calva, Lepisosteus platyrhincus, Micropterus salmoides) are included for plots where electrofishing was performed from 1997-2021. Hydrologic measures used in analyses and R code used to conduct analyses are also included.

openCC (other)Aug 2023View details →

opencc-by-4.0Feb 2022View details →

Data set from long-term wind and acceleration monitoring of the Gjemnessund Bridge

The Gjemnessund Bridge has been monitored by accelerometers and anemometers for almost ten years. The data collected between 2013 and 2018 are now available in this open-access research entry, for free access and download. The data is collected in two h5-files (hierachical data format), with sampling rates 2 Hz and 10 Hz, downsampled from the raw sampling rate of 200 Hz. Some minimal signal processing is applied to the data in line with that applied to the Hardanger Bridge data described in Fenerci et al. (2021) and the Bergsøysund Bridge data described in Kvåle et al. (2022). The structure of the data is identical to that of the latter reference, which is described in a preprint appended to that research entry. The Python package opyndata available on GitHub contains useful tools compatible with the format of the dataset, for data import, processing and visualization.

opencc-by-4.0Jan 2018View details →

Data and results for manuscript "Imaging groundwater infiltration dynamics in karst vadose zone with long-term ERT monitoring"

This data set contains raw and inverted data from an Electrical Resistivity Tomography (ERT) monitoring experiment conducted over a period of three years at the Rochefort Cave Laboratory (RCL) site in South Belgium. It highlights variable hydrodynamics in the karst vadose zone of Lorette Cave. More conventional hydrological measurements (drip discharge monitoring, soil moisture and water conductivity data sets) are also included in the package, which aims at provide a thorough understanding of the groundwater infiltration. Seasonal changes affect all the imaged areas leading to increases in resistivity in spring/summer attributed to enhanced evapotranspiration, whereas winter is characterised by a general decrease in resistivity associated with a groundwater recharge of the vadose zone. This study provides detailed images of the sources of drip discharge spots traditionally monitored in caves and aims to support modelling approaches of karst hydrological processes.

opencc-by-4.0Nov 2018View details →

Supplementary Data for "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" (abridged version)

This is a supplementary data set associated with the publication "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" from the Center for Atmospheric Particle Studies, submitted to Atmospheric Measurement Techniques. This is an abbreviated version which does not include the calibrated models; these models must be re-generated by running the codes contained with the data set.

opencc-by-4.0Dec 2022View details →

Long-term snow chemical composition monitoring - Hansbreen glacier (Hornsund) - raw data

During the accumulation season, snow samples were taken on Hansbreen Glacier. Several times per season. Snow samples were collected in polyethylene sterile bags and transported to the Polish Polar Station Hornsund. After melting at room temperature, the pH, conductivity and chemical composition (major ions) were analysed in the chemical laboratory of the Polish Polar Station. Snow chemical composition: major ions, HCO3-, pH, conductivity Presented data from 2015 to 2019 The data has not been checked, which means that it is raw data. Principal Investigator (PI) Adam Nawrot

opencc-by-4.0Dec 2022View details →

Long-term atmospheric precipitation monitoring in Hornsund region (Fuglebekken) - raw data

Since 2004, snow and rain samples have been collected in the Fuglebekken catchment in close vicinity of the Polish Polar Station Hornsund. The rain and snow samples are collected after every event. The pH, conductivity and chemical composition (major ions) are analysed at the Polish Polar Station’s chemical laboratory. The rain gauge is checked approximately once a day. Presented data from 2016 to 2022 The data has not been checked, which means that it is raw data.

dryad40/100

Data for: Large-scale long-term passive-acoustic monitoring reveals spatiotemporal activity patterns of boreal bats

The distribution ranges and spatio-temporal patterns in the occurrence and activity of boreal bats are yet largely unknown due to their cryptic lifestyle and lack of suitable and efficient study methods. We approached the issue by establishing a permanent passive-acoustic sampling setup spanning the area of Finland to gain an understanding on how latitude affects bat species composition and activity patterns in northern Europe. The recorded bat calls were semi-automatically identified for three target taxa; Myotis spp., Eptesicus nilssonii or Pipistrellus nathusii and the seasonal activity patterns were modeled for each taxa across the seven sampling years (2015–2021). We found an increase in activity since 2015 for E. nilssonii and Myotis spp. For E. nilssonii and Myotis spp. we found significant latitude -dependent seasonal activity patterns, where seasonal variation in patterns appeared stronger in the north. Over the years, activity of P. nathusii increased during activity peak in June and late season but decreased in mid season. We found the passive-acoustic monitoring network to be an effective and cost-efficient method for gathering bat activity data to analyze spatio-temporal patterns. Long-term data on the composition and dynamics of bat communities facilitates better estimates of abundances and population trend directions for conservation purposes and predicting the effects of climate change.

opencc-zeroFeb 2023View details →