Find research datasets worth reusing

openCC (other)Sep 2021View details →

Long-term Meteorological Data from the GCE-LTER Eddy Covariance Flux Tower on Sapelo Island, Georgia

Long-term measurements of key meteorological variables were made using electronic sensors attached to the GCE-LTER eddy covariance flux tower deployed in a Spartina alterniflora salt marsh on Sapelo Island, Georgia. Variables measured include air temperature, relative humidity, precipitation, wind speed, wind direction, photosynthetically-available and total solar radiation. Measurements were logged at 5 minute intervals using a Campbell Scientific Instruments CR3000 data logger then re-scaled to 15 minute and daily interval data sets. Quality control analyses were performed to remove values deemed invalid due to sensor failure or miscalibration, and to assign Q/C qualifiers to values outside expected ranges or failing various sanity and quality checks of the data. Measurements began in 2013; however, the total solar pyranometer was not installed until 2018 and minimum and maximum 5 minute air temperature were not added until 2019 so not all variables span the complete period of record. Measurements are continuing at this site and this data set will be updated annually to include additional observations.

openCC0Oct 2022View details →

Long-term climate indices (SPEI and scPDSI) derived from monthly meteorology data collected at USHCN stations in the northern Chihuahuan Desert of the United States, 1911-2021

Drought indices — Standardized Precipitation Evapotranspiration Index (SPEI) and the self-calibrating Palmer Drought Severity Index (scPDSI) —where derived from 9 United States Historical Climate Network (USHCN) stations on the Chihuahuan Desert in North America for this dataset. USHCN is a subset of the NOAA Cooperative Observer Program (COOP) Network, which consists of selected sites based on spatial coverages and completeness of data. Monthly precipitation depths, minimum, maximum and mean temperature were pulled from the dataset. These drought indices were derived using the SPEI package and scPDSI packages in R. Potential evapotranspiration was also calculated in R using the Thornthwaite method. All 9 sites are within the bounds of the Chihuahuan Desert in the state of New Mexico, with a single site (EL PASO) in the state of Texas.

Quadrat vegetation cover data on 1m x 1m plots from the long-term Small Mammal Exclusion Study (SMES) at Jornada Basin LTER, 1995-2020

This data package contains vegetation cover from plots with various levels of herbivore exclusion on the Jornada Experimental Range (JER) and Chihuahuan Desert Rangeland Research Center (CDRRC) in Dona Ana County, southern New Mexico, USA. Study sites were established in 1995; one in black grama grassland and the other in creosotebush shrubland to compare the impact of herbivores on ecosystem processes between these vegetation types. Parallel studies were established at the Sevilleta LTER site (New Mexico, USA) and Mapimi Biosphere Reserve (Durango, Mexico). Each study site is 1 km by 0.5 km in area. Four replicate experimental blocks were randomly located at the grassland study site to measure vegetation responses using exclusion treatments including a) all mammalian herbivores, including cattle, lagomorphs, and rodents, b) lagomorphs and cattle only, c) cattle only, and d) control accessible to all herbivores. Because grazing cattle are excluded from the entire creosote site, only three replicate experimental blocks were randomly located there including a) all mammalian herbivores, including lagomorphs, and rodents, b) lagomorphs only, and c) control accessible to all herbivores. Thirty-six sampling points were positioned at 5.8-meter intervals on a systematically located 6 by 6 point grid within each plot. A permanent one-meter by one-meter vegetation measurement quadrat is located at each of the 36 points. At each quadrat, percent cover by individual plant species is measured. Other measurements include height (cm) of each species in the quadrat, and plant condition (living or dead). Data were collected in the spring and fall of every year from 1995 to 2005. After 2005, sampling frequency changed to every 5 years in the fall. This study is ongoing.

Leaf litter cover data on 1m x 1m plots from the long-term Small Mammal Exclusion Study (SMES) at Jornada Basin LTER, 1995-2020

This data package contains leaf litter cover data from plots with various levels of herbivore exclusion on the Jornada Experimental Range. Study sites were established in 1995; one in black grama grassland and the other in creosotebush shrubland to compare the impact of herbivores on ecosystem processes between these vegetation types. Parallel studies were established at the Sevilleta LTER site (New Mexico, USA) and Mapimi Biosphere Reserve (Durango, Mexico). Each study site is 1 km by 0.5 km in area. Four replicate experimental blocks were randomly located at the grassland study site to measure vegetation responses using exclusion treatments including a) all mammalian herbivores, including cattle, lagomorphs, and rodents, b) lagomorphs and cattle only, c) cattle only, and d) control accessible to all herbivores. Because grazing cattle are excluded from the entire creosote site, only three replicate experimental blocks were randomly located there including a) all mammalian herbivores, including lagomorphs, and rodents, b) lagomorphs only, and c) control accessible to all herbivores. Thirty-six sampling points were positioned at 5.8-meter intervals on a systematically located 6 by 6 point grid within each plot. A permanent one-meter by one-meter vegetation measurement quadrat is located at each of the 36 points. Each year in spring and fall from 1995-2005, the total percent cover of leaf litter in each quadrat was estimated by summing the percent of each 10 cm square within a quadrat (including 100 10-cm squares) containing leaf litter (See methods for a detailed explanation). After 2005, sampling frequency changed to every 5 years. This study is ongoing.

Rabbit survey data on creosotebush and grassland routes from the long-term Small Mammal Exclusion Study at Jornada Basin LTER, 1996-ongoing

This data package contains rabbit survey data from grassland and creosote shrubland habitats on Jornada Experimental Range (JER) and Chihuahuan Desert Rangeland Research Center (CDRRC) lands. Two survey routes were established along Jornada Basin roads in 1996; one in black grama grassland and the other in creosotebush shrubland. Quarterly surveys are conducted on these roads at or near the full moon to measure the density of rabbits in the two vegetation types. Each route is about 6 miles long. Parallel studies were established at the Sevilleta LTER site (New Mexico, USA) and Mapimi Biosphere Reserve (Durango, Mexico). Data collection began in April 1996 and includes date and time lagomorphs are spotted, species identification, habitat type, distance/direction from vehicle, and comments on the weather, moon, and anything unusual. This study is ongoing with new data collected quarterly.

Soil disturbance cover data on 1m x 1m plots from the long-term Small Mammal Exclusion Study (SMES) at Jornada Basin LTER, 1995-2020

This data package contains soil disturbance data from plots with various levels of herbivore exclusion on the Jornada Experimental Range. Study sites were established in 1995; one in black grama grassland and the other in creosotebush shrubland to compare the impact of herbivores on ecosystem processes between these vegetation types. Parallel studies were established at the Sevilleta LTER site (New Mexico, USA) and Mapimi Biosphere Reserve (Durango, Mexico). Each study site is 1 km by 0.5 km in area. Four replicate experimental blocks were randomly located at the grassland study site to measure vegetation responses using exclusion treatments including a) all mammalian herbivores, including cattle, lagomorphs, and rodents, b) lagomorphs and cattle only, c) cattle only, and d) control accessible to all herbivores. Because grazing cattle are excluded from the entire creosote site, only three replicate experimental blocks were randomly located there including a) all mammalian herbivores, including lagomorphs, and rodents, b) lagomorphs only, and c) control accessible to all herbivores. Thirty-six sampling points were positioned at 5.8-meter intervals on a systematically located 6 by 6 point grid within each plot. A permanent one-meter by one-meter vegetation measurement quadrat is located at each of the 36 points. Each year in spring and fall from 1995-2005, various forms of disturbances (human, rabbit, cow, antelope, rodent, etc) were measured by depth . After 2005, sampling frequency changed to every 5 years. This study is ongoing.

openCC (other)Dec 2022View details →

Long-term fish abundance data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012

This dataset describes long-term (1944-2012) variations in the relative abundance of fish populations representing nine species in Wisconsin lakes. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894

openCC (other)Dec 2022View details →

Long-term fish size data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012

This dataset describes long-term (1944-2012) variations in individual fish total lengths from Wisconsin lakes. The dataset includes information on 1.9 million individual fish, representing 19 species. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894

openCC0Oct 2023View details →

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.

openCustomMay 2022View details →

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

opencc-by-4.0May 2020View details →

Genome-Wide DNA Methylation in Peripheral Blood and Long-Term Exposure to Source-Specific Transportation Noise and Air Pollution: The SAPALDIA Study (Supplementary Data)

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). The description of the files are noted below: 1. Readme File for SAPALDIA Noise and Air Pollution EWAS Single Exposure.zip This zip file contains all the results of the association between source-specific transportation noise (aircraft, railway and road traffic), air pollution (NO2 and PM2.5), and genome-wide DNA methylation, derived from multi-exposure models. SAPALDIA_EWAS_SingleExposure_AircraftLden.txt contains the results for aircraft noise SAPALDIA_EWAS_SingleExposure_RailwayLden.txt contains the results for railway noise SAPALDIA_EWAS_SingleExposure_RoadtrafficLden.txt contains the results for road traffic noise SAPALDIA_EWAS_SingleExposure_NO2.txt contains the results for nitrogen dioxide SAPALDIA_EWAS_SingleExposure_PM25.txt contains the results for fine particulate matter   General footnote for all files: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. NO2: nitrogen dioxide. PM2.5: 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/m3 increase in NO2 or PM2.5. 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. 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.   2. Readme File for SAPALDIA Noise and Air Pollution EWAS Multi Exposure.zip This zip file contains all the results of the association between source-specific transportation noise (aircraft, railway and road traffic), air pollution (NO2 and PM2.5), and genome-wide DNA methylation, derived from multi-exposure models. SAPALDIA_EWAS_MultiExposure_AircraftLden.txt contains the results for aircraft noise SAPALDIA_EWAS_MultiExposure_RailwayLden.txt contains the results for railway noise SAPALDIA_EWAS_MultiExposure_RoadtrafficLden.txt contains the results for road traffic noise SAPALDIA_EWAS_MultiExposure_NO2.txt contains the results for nitrogen dioxide SAPALDIA_EWAS_MultiExposure_PM25.txt contains the results for fine particulate matter General table footnotes: 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. NO2: nitrogen dioxide. PM2.5: 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/m3 increase in NO2 or PM2.5. 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, NO2 and PM2.5) 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.

opencc-by-4.0Nov 2020View details →

IPBES Data Management Tutorials - Session 3.7: Data management report details: Long-term storage details

The IPBES data management tutorials 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. The IPBES data management reports chapter provides an overview and discussion of specific elements of IPBES data management reports. This session, Data management report details: Long-term storage details, explores the reasons why IPBES recommends Zenodo as a long-term repository.

opencc-by-4.0Dec 2023View details →

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 2021View details →

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

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.

opencc-by-4.0Feb 2022View details →

Data associated with "A weakened recurrent circuit in the hippocampus of Rett syndrome mice disrupts long-term memory representations"

Datasets used in A weakened recurrent circuit in the hippocampus of Rett syndrome mice disrupts long-term memory representations. Datatypes: <ol> <li>Multi-index pandas dataframe (.pkl)</li> <li>Numpy array (.npy)</li> <li>Collection of numpy arrays (.npz)</li> <li>Python dictionary objects (.pkl)</li> </ol> Datasets: alignments.pkl: A dataframe containing numpy arrays of image displacements for each mouse in each memory context. This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id. The columns are ['T', 'F1', 'N1', 'F2', 'N2'] for the training, recall 1-hour, neutral, recall 1-day, neutral day 2 memory contexts respectively. Each element of this dataframe is a numpy array of shape  images x 2 that hold x and y image displacements respectively. These alignments are computed after the inscopix software motion correction and are used in Supplemental Figure 2 of the paper. behavior_df.pkl: A dataframe of behavior readouts recorded by a camera positioned above the mice in each context chamber. This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id.The columns are; sample times (*_time), freezing boolean arrays (*_freeze), x-positions in context chamber (*_x) and y-positions in the context chamber (*_y) for each context (*) in ('Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'). correlated_pairs_df.pkl: A dataframe containing arrays of neuron indices that have a correlation in activity pattern > 0.3. This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment ('NA'). The columns contain ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing each memory context. Each element of the dataframe is a numpy array with three columns. The first two columns are the neuron indices that are correlated and the last column is the strength of the correlation. dredd_freezes_df.pkl: A dataframe containing freezing percentages for SOM-Cre and RTT-SOM-Cre mice treated with DREADDS. This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment (mcherry, hm3d, hm4d). The columns contain one of ['Neutral', 'Fear', 'Fear_2']. Each element of the dataframe is a freezing percentage for a single mouse. This dataframe is built from reading the dredd_behavior.xlsx excel file. This is used to generate figure 5E of the paper. high_degree_df.pkl: A dataframe containing list of high degree neuron indices. This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment ('NA'=not applicable since no DREADD used). The columns contain ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing each memory context. Each element of the dataframe is a list of neuron indices that are high-degree cells. N006_wt_basis.npz: a dict containing three numpy arrays representing the basis images for mouse N006 of genotype wild-type. This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 220 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Supplemental Figure 2 of the paper. N006_wt_cxtbasis.pkl: A dictionary containing arrays for basis images and singular values for each context. This dictionary has keys, ['Train', 'Fear', 'Neutral', 'Fear_2',  'Neutral_2'] representing the memory contexts. Each value is a 2 element list containing the U-basis images as column vectors and singular values, one per basis image in U. The shape of the basis images is the same shape stored in N006_wt_basis.pkl. This dataset is used in Supplementary Figure 2 to track cells across contexts of the CFC task (see also N006_wt_cxtsources.pkl) N006_wt_cxtsources.pkl: A dictionary containing the independent component source images computed from the basis images for automatically identifying regions of interest (ROIs).  The dictionary is keyed on  ['Train', 'Fear', 'Neutral', 'Fear_2',  'Neutral_2'] contexts. Each value in the dictionary at a given key is a 3-D numpy array of shape sources x height x width. These data were used to construct the source images and max intensity projection image of the sources in Supplemental Figure 2F-J of the paper. N006_wt_rois.pkl: A dictionary containing the boundaries and annuli coordinates of all rois for mouse N006 of genotype wild-type. This dictionary is keyed on ['boundaries', 'annuli'] contexts and each value is a 179 element list of arrays of boundary line coordinates or annulus point coordinates one  per ROI detected for this mouse. N006_wt_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N006 of genotype wild-type. This numpy array has shape n x height x width where n=205 source images, height=517 pixels and width=704 pixels. This data was used to construct Supplemental Figure 3F. N019_wt_basis.npz: a dict containing three numpy arrays representing the basis images for mouse N019 of genotype wild-type. This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 220 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Figure 1C of the paper. N019_wt_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N019 of genotype wild-type. This numpy array has shape n x height x width where n=204 source images, height=516 pixels and width=698 pixels. This data was used to construct Figure 1C of the paper. P80_animals.pkl: A pandas multi-index object containing the genotype, mouse_id and treatment of the top 80% behavioral performance animals. In this study, we drop the lowest 20% performing WT and RTT animals based on freezing percentage during the recall contexts. This multi-index is used to filter the data before each computation or plot in this study. So for example Figure 1B contains only the top 80% performing WT and RTT mice. pc_sipscs_amps.pkl: A dictionary containing the amplitudes of spontaneous IPSCs recorded in pyramidal cells of WT and RTT mice. This dictionary is keyed on ['wt', 'mecp2_pos', 'mecp2_neg'] representing whether the pyramidal cell was recorded from a wild-type mouse ('wt') or is an MeCP2 negative or MeCP2 positive RTT cell. This value under each key is an array of IPSC amplitudes, one per recorded cell. This data was used to construct Figure 4C in the paper. pc_sipscs_freqs.pkl: A dictionary containing the frequencies of spontaneous IPSCs recorded in pyramidal cells of WT and RTT mice. This dictionary is keyed on ['wt', 'mecp2_pos', 'mecp2_neg'] representing whether the pyramidal cell was recorded from a wild-type mouse ('wt') or is an MeCP2 negative or MeCP2 positive RTT cell. This value under each key is an array of IPSC frequencies, one per recorded cell. This data was used to construct Figure 4C in the paper. rois_df.pkl: A multi-index dataframe containing all ROI information for each non-DREADD treated cell in this study (Figures 1-3). This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0. The columns are ['centroid', 'cell_boundary', 'annulus_boundary']. The centroid for each cell is a 2-tuple of row, column pixel centroid coordinates. The cell_boundary is a two-column array of row, col boundary points for each ROI. The annulus_boundary is a two-column array of row, column interior points in the annulus. The annulus region  excludes points of overlap with nearby cell bodies (See STAR methods of the paper). signals_df.pkl: A multi-index dataframe containing calcium signals, inferred spikes and metadata for all Non-DREADD experiments used in this study (Figs 1-3). This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0 and going up to 5771 cells. The columns are ['channels', 'channel', 'num_pages', 'width', 'height', 'bits', 'Train_signals', 'Fear_signals', 'Neutral_signals', 'Cue_signals', 'Fear_2_signals', 'Neutral_2_signals', 'Cue_2_signals', 'Train_spikes', 'Fear_spikes', 'Neutral_spikes', 'Cue_spikes', 'Fear_2_spikes', 'Neutral_2_spikes', 'Cue_2_spikes', 'sample_rate']. The channels are all the recorded channels, the channels is the channel on which ROIs were detected, the width and height are the image dimensions, the bits is the image bit depth of the calcium movie. The *_signals' are the df/f signals for each cell in each context. Each signal is a numpy array with the first 800 samples have been set to NAN due to settling time of the miniscope. The '*_spikes' are the inferred spikes for each cell stored as an image index. This signal and spike indices can be converted to time using the sample column. This dataframe is used in the construction of Figures 1-3 in the paper. som_behavior_df.pkl: A dataframe of behavior readouts recorded by a camera positioned above the mice in each context chamber. This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id. The columns are; sample times (*_time), freezing boolean arrays (*_freeze), x-positions in context chamber (*_x) and y-positions in the context chamber (*_y) for each context in *=('Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'). This dataframe was not used in the paper but may still be useful for further analysis. som_sepsc_amplitudes: A dictionary containing the amplitudes of spontaneous EPSCs recorded in SOM cells of WT and RTT mice with and without MeCP2. A dictionary with keys ['som', 'som_rett_pos', 'som_rett_neg'] for WT SOM and RTT-SOM cells with and without MeCP2 respectively. Each value is a list of sEPSC amplitudes. This data was used in Figure 4E-G. som_sepsc_freqs: A dictionary containing the amplitudes of spontaneous EPSCs recorded in SOM cells of WT and RTT mice with and without MeCP2. A dictionary with keys ['som', 'som_rett_pos', 'som_rett_neg'] for WT SOM and RTT-SOM cells with and without MeCP2 respectively. Each value is a list of sEPSC frequencies. This data was used in Figure 4E-G. som_signals_df.pkl: A multi-index dataframe containing calcium signals, inferred spikes and metadata for all Non-DREADD SOM cell recordings used in this study (Figs 5). This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0 and going up to 710 cells. The columns are ['channels', 'channel', 'num_pages', 'width', 'height', 'bits', 'Train_signals', 'Fear_signals', 'Neutral_signals', 'Cue_signals', 'Fear_2_signals', 'Neutral_2_signals', 'Cue_2_signals', 'Train_spikes', 'Fear_spikes', 'Neutral_spikes', 'Cue_spikes', 'Fear_2_spikes', 'Neutral_2_spikes', 'Cue_2_spikes', 'sample_rate']. The channels are all the recorded channels, the channels is the channel on which ROIs were detected, the width and height are the image dimensions, the bits is the image bit depth of the calcium movie. The *_signals' are the df/f signals for each cell in each context. Each signal is a numpy array with the first 800 samples have been set to NAN due to settling time of the miniscope. The '*_spikes' are the inferred spikes for each cell stored as an image index. This signal and spike indices can be converted to time using the sample column. This data was used to construct Figure 5B-C. ssn33_sstcre_basis.npz: a dict containing three numpy arrays representing the basis images for mouse ssn33 of genotype sst-cre. This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 100 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Figure 5A of the paper. ssn33_sstcre_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N019 of genotype wild-type. This numpy array has shape n x height x width where n=86 source images, height=516 pixels and width=654 pixels. This data was used to construct Figure 5A of the paper.

openCC0Sep 2022View details →

Global data set of long-term summertime vertical temperature profiles in 153 lakes

Climate change and other anthropogenic stressors have led to long-term changes in the thermal structure, including surface temperatures, deepwater temperatures, and vertical thermal gradients, in many lakes around the world. Though many studies highlight warming of surface water temperatures in lakes worldwide, less is known about long-term trends in full vertical thermal structure and deepwater temperatures, which have been changing less consistently in both direction and magnitude. Here, we present a globally-expansive data set of summertime in-situ vertical temperature profiles from 153 lakes, with one time series beginning as early as 1894. We also compiled lake geographic, morphometric, and water quality variables that can influence vertical thermal structure through a variety of potential mechanisms in these lakes. These long-term time series of vertical temperature profiles and corresponding lake characteristics serve as valuable data to help understand changes and drivers of lake thermal structure in a time of rapid global and ecological change.

openCC (other)Oct 2025View details →

Baltimore Ecosystem Study: Soil solution chemistry data from long-term study plots

The Baltimore Ecosystem Study (BES) has established a network of long-term permanent biogeochemical study plots. These plots will provide long-term data on vegetation, soil and hydrologic processes in the key ecosystem types within the urban ecosystem. The current network of study plots includes eight forest plots, chosen to represent the range of forest conditions in the area, and four grass plots. These plots are complemented by a network of 200 less intensive study plots located across the Baltimore metropolitan area. Plots are currently instrumented with lysimeters (drainage and tension) to sample soil solution chemistry, time domain reflectometry probes to measure soil moisture, dataloggers to measure and record soil temperature and trace gas flux chambers to measure the flux of carbon dioxide, nitrous oxide and methane from soil to the atmosphere. Measurements of in situ nitrogen mineralization, nitrification and denitrification were made at approximately monthly intervals from Fall 1998 - Fall 2000. Detailed vegetation characterization (all layers) was done in summer 1998. Data from these plots has been published in Groffman et al. (2006, 2009) and Groffman and Pouyat (2009). In November of 1998 four rural, forested plots were established at Oregon Ridge Park in Baltimore County northeast of the Gwynns Falls Watershed. Oregon Ridge Park contains Pond Branch, the forested reference watershed for BES. Two of these four plots are located on the top of a slope; the other two are located midway up the slope. In June of 2010 measurements at the mid-slope sites on Pond Branch were discontinued. Monuments and equipment remain at the two plots. These plots were replaced with two lowland riparian plots; Oregon upper riparian and Oregon lower riparian. Each riparian sites has four 5 cm by 1-2.5 meter depth slotted wells laid perpendicular to the stream, four tension lysimeters at 10 cm depth, five time domain reflectometry probes, and four trace gas flux chambers in the

openOpenOct 2003View details →

Tree regeneration after fire: Wickersham Dome long-term vegetation study, birch height data

These data represent the most recent set of observations (made in 2002 by J. Johnstone) for several long-term vegetation monitoring plots near Wickersham Dome that were set up by Les Viereck and Joan Foote following the 1971 wildfire and 1978 experimental burns. Earlier records are available in the BNZ long-term vegetation database. This dataset documents tree seedling/sapling and shrub measurements made in 2002. Lists heights of all individual paper birch (Betula papyrifera) present in a plot.

openOpenOct 2003View details →

Tree regeneration after fire: Wickersham Dome long-term vegetation study, bl. spruce height data