cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/223849bf844c45599a9d6820eccd32ad
title: Litterfall in the Clearcut Site at Harvard Forest 2012

description: Clearcutting a forest ecosystem can result in a drastic reduction of the stand’s
 productivity. Despite the severity of this disturbance type, past studies have found that the
 productivity of young regenerating stands can quickly rebound, approaching that of mature
 undisturbed stands within a few years. One of the obvious reasons is increased leaf area with
 each year of recovery. However, a less obvious reason may be the variability in species
 composition and distribution during the natural regeneration process. The purpose of this study
 was to investigate to what extent the increase in GEP, observed during the first four years of
 recovery, in a naturally regenerating clearcut stand was due to 1) an overall expansion of leaf
 area, and 2) an increase in the canopy’s photosynthetic capacity stemming from either species
 compositional shifts or drift in physiological traits within species. We found that the
 multi-year rise in GEP following harvest was clearly attributed to the expansion of leaf area
 rather than a change in vegetation composition. Sizeable changes in relative abundance of
 species were masked by remarkably similar leaf physiological attributes for a range of
 vegetation types present in this early successional environment. Comparison of upscaled
 leaf-chamber to eddy-covariance-based light-response curves revealed broad consistency in both
 maximum photosynthetic capacity and quantum yield efficiency. The approaches presented here
 illustrate how chamber- and ecosystem-scale measurements of gas exchange can be blended with
 species-level leaf area data to draw conclusive inferences about changes in ecosystem processes
 over time in a highly dynamic environment.
DOI: 10.6073/pasta/a5aa971918bf932201bf9fe9404fb36e
title: Photosynthetically Active Radiation in the Clearcut Site at Harvard Forest 2013

description: The purpose of these measurements was to capture the light attenuation of the canopy throughout the growing season, which can help in determining leaf area/canopy openness at the site. Measurements were done along a 50-m-long transect (one of the ones used for line-intercept vegetation surveys, on the side of the EC-tower). Eight measurement locations were established along the transect, equidistant, and marked with a bright orange flag. Measurements were taken with a LI-COR LI-191 Line quantum sensor as described below. Before and after measurements along the transect, PAR-readings were taken in an open area (away from canopy cover) either at a log along the access road to the site or up on the fire tower nearby. At each location along the transect, two measurements were taken – one at the ground surface and one at a height of 1.3m (diameter at breast height).
DOI: 10.5281/zenodo.10265072
title: Sentinel-5P Methane Density at 2 km from 2021-12 to 2023-11 Monthly Aggregation Time-series Reconstructed

description: General Description
The monthly aggregated Methane Volume Mixing Ratio dataset is derived from Sentinel-5P to generate a time-series reconstructed monthly aggregated map. The dataset time spans from December 2021 to November 2023 and provides data that covers the entire globe. The mission is still underway and expected to update periodically.
For more info about the s5p Methane product see: https://maps.s5p-pal.com/ch4/.
The dataset can be used in many applications like emission tracing, livestock monitor, and greenhouse gas monitor.

Monthly time-series:
Methane monthly average value December 2021 – November 2023. Derived using the eumap and scikitmap package in Python . We derived three standard statistics: (1) 10th percentile (p10), median (p50), and 90th percentile (p90).
Data Details

Time period: December 2021 – November 2023
Type of data: Methane Volume Mixing Ratio (Unit: ppbv)
How the data was collected or derived: Derived from 2km Sentinel-5P Menthane using Python running in a local HPC. The time-series analysis were computed using the Scikit-map and eumap Python package.
Statistical methods used: percentiles 10, 50, and 90.
Limitations or exclusions in the data: The dataset is not completed gap-filled. Certain areas have no data in the whole time series
Coordinate reference system: EPSG:4326
Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -61.9966697, 180.0000072, 87.37000)
Spatial resolution: 1/60 d.d. = 0.016666667 (2km)
Image size: 21,600 x 8,962
File format: Cloud Optimized Geotiff (COG) format.
Support
If you discover a bug, artifact or inconsistency, or if you have a question please use some of the following channels:

Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues
General questions and comments: https://disqus.com/home/forums/landgis/
Name convention
To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:

generic variable name: ch4.vmr = methane density methane volume mixing ratio
variable procedure combination: m.seacov = monthly aggregated and gap filled by seasonal convolution
Position in the probability distribution / variable type: p10/p50/p90 = 10th/50th/90th percentile
Spatial support: 2km
Depth reference: a = above surface
Time reference begin time: 20211201 = 2021-12-01
Time reference end time: 20231131 = 2023-11-31
Bounding box: go = global (without Antarctica)
EPSG code: epsg.4326 = EPSG:4326
Version code: v20230628 = 2023-12-08 (creation date)
DOI: 10.5281/zenodo.10265073
title: Sentinel-5P Methane Density at 2 km from 2021-12 to 2023-11 Monthly Aggregation Time-series Reconstructed

description: General Description
The monthly aggregated Methane Volume Mixing Ratio dataset is derived from Sentinel-5P to generate a time-series reconstructed monthly aggregated map. The dataset time spans from December 2021 to November 2023 and provides data that covers the entire globe. The mission is still underway and expected to update periodically.
For more info about the s5p Methane product see: https://maps.s5p-pal.com/ch4/.
The dataset can be used in many applications like emission tracing, livestock monitor, and greenhouse gas monitor.

Monthly time-series:
Methane monthly average value December 2021 – November 2023. Derived using the eumap and scikitmap package in Python . We derived three standard statistics: (1) 10th percentile (p10), median (p50), and 90th percentile (p90).
Data Details

Time period: December 2021 – November 2023
Type of data: Methane Volume Mixing Ratio (Unit: ppbv)
How the data was collected or derived: Derived from 2km Sentinel-5P Menthane using Python running in a local HPC. The time-series analysis were computed using the Scikit-map and eumap Python package.
Statistical methods used: percentiles 10, 50, and 90.
Limitations or exclusions in the data: The dataset is not completed gap-filled. Certain areas have no data in the whole time series
Coordinate reference system: EPSG:4326
Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -61.9966697, 180.0000072, 87.37000)
Spatial resolution: 1/60 d.d. = 0.016666667 (2km)
Image size: 21,600 x 8,962
File format: Cloud Optimized Geotiff (COG) format.
Support
If you discover a bug, artifact or inconsistency, or if you have a question please use some of the following channels:

Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues
General questions and comments: https://disqus.com/home/forums/landgis/
Name convention
To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:

generic variable name: ch4.vmr = methane density methane volume mixing ratio
variable procedure combination: m.seacov = monthly aggregated and gap filled by seasonal convolution
Position in the probability distribution / variable type: p10/p50/p90 = 10th/50th/90th percentile
Spatial support: 2km
Depth reference: a = above surface
Time reference begin time: 20211201 = 2021-12-01
Time reference end time: 20231131 = 2023-11-31
Bounding box: go = global (without Antarctica)
EPSG code: epsg.4326 = EPSG:4326
Version code: v20230628 = 2023-12-08 (creation date)
DOI: 10.5281/zenodo.10301665
title: Electrospun Fiber Experimental Attributes Dataset (FEAD)

description: Understanding the intricate relationships between the structure and properties of electrospun PVDF fibers is crucial for designing effective devices. However, the complexity of these relationships makes them challenging to model through traditional trial-and-error experiments. Machine learning has emerged as a powerful tool for modeling multidimensional relationships, but it requires a diverse and high-quality training dataset to effectively learn these intricate connections.
To comprehensively investigate the intricate relationships between the structure and properties of electrospun PVDF fibers, a high-quality training dataset is imperative, encompassing a wide array of experimental conditions. There is no single comprehensive source that provides a holistic understanding of how the various parameters impact the PVDF fiber properties. Existing studies predominantly present results from a limited range of parameter variations, necessitating the creation of a meta dataset for robust insights. In response to this gap, we introduce the "Electrospun Fiber Experimental Attributes Dataset (FEAD)," a meticulously curated compilation that collates data from diverse literature sources and complements with our own experimental findings and computations.
FEAD comprises a total of 565 data points sourced from 48 distinct references. This dataset encapsulates a broad spectrum of solvents and solvent systems, including DMAC, DMF, DMAC/Acetone, DMSO/Acetone, DMF/Acetone, NMP/Acetone, among others. While certain parameters such as polymer concentration, solvent ratio, feed rate, and applied voltage were directly extracted from the literature, other critical factors like solubility parameters, interaction radii, and RED were computed.
Experiments involving the use of surfactants and electrolytes to alter polymer viscosity and electrical conductivity are not included in the dataset. This exclusion ensures a focus on understanding the genuine impact of polymer concentration and solubility factors on the resulting fibers. The development of this novel materials database is an ongoing effort, and we anticipate its continual improvement over time as more datapoints are incorporated.
DOI: 10.6073/pasta/da42492838cd562bba014e7fdcbc7415
title: Seedlings and Saplings in the Clearcut Site at Harvard Forest 2010-2013

description: The aim of this dataset is to quantify the changes over time in woody seedling and sapling abundances and sizes at a site that was clearcut in the fall of 2008, followed by natural regeneration. It evidences successional changes in recruitment, survival, and associated self-thinning, and can be used to quantify accumulation of aboveground live biomass post-clearing with corresponding allometric relationships.
DOI: 10.6073/pasta/6bdf9ebdaa102cbefa32efa57a69b2d0
title: Eddy Covariance and Meteorological Data in the Clearcut Site at Harvard Forest 2009-2012

description: Clearcutting and other forest disturbances perturb carbon, water, and energy balances in significant ways, with corresponding influences on Earth’s climate system through biogeochemical and biogeophysical effects. Observations are needed to quantify the precise changes in these balances as they vary across diverse disturbances of different types, severities, and in various climate and ecosystem type settings. This dataset reports eddy covariance and micrometeorological measurements of surface-atmosphere exchanges that can be combined with related datasets from vegetation inventories and chamber-based estimates of soil respiration and leaf gas exchange to collectively quantify and understand how carbon, water, and energy fluxes changed during the first four years following forest clearing in a temperate forest environment of the northeastern US. Associated publications show rapid recovery with sustained increases in gross ecosystem productivity (GEP) over the first three growing seasons post-clearing, coincident with large and relatively stable net emission of CO2 because of overwhelmingly large ecosystem respiration. The rise in GEP was attributed to vegetation changes not environmental conditions (e.g. weather), but attribution to the expansion of leaf area versus changes in vegetation composition remains unclear. Soil respiration was estimated to contribute 44% of total ecosystem respiration during summer months and coarse woody debris accounted for another 18%. Evapotranspiration also recovered rapidly and continued to rise across years with a corresponding decrease in sensible heat flux. Gross shortwave and longwave radiative fluxes were stable across years except for strong wintertime dependence on snow covered conditions and corresponding variation in albedo. Overall, these findings underscore the highly dynamic nature of carbon and water exchanges and vegetation composition during the regrowth following a severe forest disturbance, and sheds light on both the magnitude of such changes and the underlying mechanisms with a unique example from a temperate, deciduous broadleaf forest.
DOI: 10.6073/pasta/826498f813973eea1bd386b2e14283a7
title: Imputed Forest Composition Map for New England Screened by Species Range Boundaries 2001-2006

description: Initializing forest landscape models (FLMs) to simulate changes in tree species composition requires accurate fine-scale forest attribute information mapped contiguously over large areas. Nearest-neighbor imputation maps have high potential for use as the initial condition within FLMs, but the tendency for field plots to be imputed over large geographical distances results in species frequently mapped outside of their home ranges, which is problematic. We developed an approach for evaluating and selecting field plots for imputation based on their similarity in feature-space, their species composition, and their geographical distance between source and imputation to produce a map that is appropriate for initializing an FLM. We applied this approach to map 13m ha of forest throughout the six New England states (Rhode Island, Connecticut, Massachusetts, New Hampshire, Vermont, and Maine). The map itself is a .img raster file of FIA plot CN numbers. To access FIA data from this map, one has to link the mapcodes in this map to FIA data supplied by USDA FIA database (https://apps.fs.usda.gov/fia/datamart/datamart.html). Due to plot confidentiality and integrity concerns, pixels containing FIA plots were always assigned to some other plot than the actual one found there.
DOI: 10.6073/pasta/5a75f8c54be833bc3b615975e0668b60
title: Estimating Forest Productivity Using Carbon Isotopes in Tree Rings at Harvard Forest 1992-2010

description: We investigated relationships between tree-ring δ13C and growth, and flux tower estimates of gross primary productivity (GPP) at Harvard Forest from 1992 to 2010. Seasonal variations of derived photosynthetic isotope discrimination (Δ13C) and leaf intercellular CO2 concentration (ci) showed significant increasing trends for the dominant deciduous and coniferous species. Δ13C was positively correlated to growing-season GPP and is primarily controlled by precipitation and soil moisture indicating that site conditions maintained high stomatal conductance under increasing atmospheric CO2 levels. Increasing Δ13C over the 1992-2010 period is attributed to increasing annual and summer water availability identified at Harvard Forest and across the region. Higher Δ13C is coincident with an enhancement in growth and ecosystem-level net carbon uptake. This work suggests that tree-ring δ13C could serve as a measure of forest GPP and be used to improve the calibration and predictive skill of ecosystem and carbon cycle models.
DOI: 10.6073/pasta/256969c14bf179f30bc8562f17c33f54
title: Vascular Flora of the Harvard Farm at Harvard Forest 2014

description: The objectives of this study were to: (1) Inventory the vascular flora of the
 former Petersham Country Club (now the Harvard Farm). (2) Collect voucher
 specimens for the HF Herbarium of species that were not previously documented
 from Harvard Forest, or that were found historically at HF but were not recorded
 in 2004–2007 by Jenkins et al. (2008). (3) Establish and sample a series of
 permanent plots to characterize current vegetation composition, and to enable
 evaluation of vegetation change over time. (4) Sample soils within permanent
 plots to document initial conditions and to facilitate future work on soil
 dynamics and ecosystem processes.
DOI: 10.5281/zenodo.10303381
title: IDCS S5 phénomène de FRET
DOI: 10.5281/zenodo.10303382
title: IDCS S5 phénomène de FRET
DOI: 10.6073/pasta/be69b1f46b57354a25d85a437c0679c8
title: Snowpack in Hemlock, Hardwood, and Open Sites at Harvard Forest 2008-2014

description: Sub-forest canopy meteorological forcing and snowpack properties can profoundly influence the winter ecology. 3/4 inch graduated PVC snow stakes and web cams were installed at three Harvard Forest (HF) sites including: mixed-hardwood at the Little Prospect Hill (LPH) Eddy Flux Tower, near the Hemlock walk-up tower, and at the open area behind Shaler Hall. Web cameras connected to a laptop at the three sites provided daily images of snowpack surface depth. Snow density profiles were recorded during weekly to monthly site visits from December 2008 to April 2014. Gaps in the datasets were primarily caused by laptop malfunctions and lack of site visits necessary to maintain equipment and to obtain density measurements of the snowpack. There were no site visits to record snowpack properties during the 2011-2012 snow season, a year of unusually low snowfall. These datasets would prove useful for research related to snow ecology and hydrology projects at Harvard Forest.
DOI: 10.6073/pasta/db61196696093ae6f9ed51efe5738cee
title: Survey of Native American Archaeological Sites in Massachusetts 12000-300 BP

description: The interpretation that pre-contact Native American land-use played an increasing role in landscape dynamics through the Holocene is prevalent in historical, scientific and popular literature. This exerts a strong influence on modern conservation practices especially the use of prescribed fire (Cronon 1984, Abrams 2002, Pyne 1984, Mann 2002) and yet there has never been a robust analysis of relevant archaeological and paleoecological data on the subject. This data is used in the archaeological component of a larger National Science Foundation (NSF)-funded research project intended to analyze the triggers and drivers of ecosystem dynamics. More specifically, the research aims to determine the role of human activity (fire, land clearance, horticulture) in shaping vegetation dynamics. Some of the alternative hypotheses examined in the archaeological analysis include: (1) do we see progressively intensive cultural development and increasingly intensive land use throughout the pre-Contact period?; (2) do we see cultural continuity with fairly passive responses to environmental change and minimal ecological impact of people?; or (3) is cultural adaptation environmental and/or cultural specific, with clear influence of human agency?
 Our collaborative ecological and social research (Duranleau 2009, Foster and Aber 2004, Chilton et al. 2010) position us to undertake such a regional synthesis as one critical element of the proposed study on ecological dynamics and regime shifts. This synthesis will allow us to consider basic ecological questions concerning interactions among climate, disturbance and human activity in ecosystem dynamics; provide a landscape and regional test of a hypothesis put forth by Munoz et al. (2010) concerning the link between environmental change and cultural development in northeastern North America; and position our archaeological community to apply new ecological perspectives to their research.
 The archaeological component is derived from intensive state-wide site file research of recorded pre-contact sites across Massachusetts.
DOI: 10.6073/pasta/31521f24eeeb8dc01999dadfad2c3bd0
title: Ants in the New York Pine Barrens 2012-2013

description: Ants are ecologically important, environmentally sensitive, widespread, and abundant, yet ant assemblages of many habitats remain poorly understood. This is true of inland pine barrens in the northeastern United States. In the Northeast, such barrens support uncommon ant species and high species density for the region. Ants in inland barrens of New York State barely have been studied. To increase knowledge of these assemblages, I systematically collected ants from three NY inland barrens and investigated how hiking trails--a common man-made disturbance--may be impacting ant assemblages in these early-successional, disturbance-dependent ecosystems. My data strongly indicate uncommonly high densities of ant species in NY pine barrens, including the most northern known occurrences of two species, and show that hiking trails alter ant assemblage composition and species density.
DOI: 10.58153/q301j-y9n92
title: State-Level Implicit and Explicit Bias Index Against Asian Americans Across the United States

description: Using national data from Project Implicit, the authors examine state-level variations in implicit and explicit bias against Asian Americans held by non–Asian Americans (n = 196,678) from 2018 to 2022. The findings reveal considerable heterogeneity in implicit and explicit bias across states.
DOI: 10.58153/x62z7-6sj68
title: State-Level Implicit and Explicit Bias Index Against Asian Americans Across the United States

description: Using national data from Project Implicit, the authors examine state-level variations in implicit and explicit bias against Asian Americans held by non–Asian Americans (n = 196,678) from 2018 to 2022. The findings reveal considerable heterogeneity in implicit and explicit bias across states.
DOI: 10.6073/pasta/1509bae16189cae4a2f63e639067b0fc
title: Water Chemistry and Well Levels at Upper Bigelow Brook, Harvard Forest 2009-2010

description: This dataset is the result of a full year of high-resolution monitoring of hydrologic event-driven export of stream dissolved organic matter from the Upper Pipe site on Bigelow Brook at Harvard Forest. In situ measurements of fluorescent dissolved organic matter (FDOM) and conductivity were collected from October of 2009 to December of 2010. Grab samples of water were collected during hydrological events and analyzed using standard methods for dissolved organic matter, nitrate, ammonium, and dissolved organic nitrogen. Water level measurements in wells at this study site were initiated in May of 2010.
DOI: 10.5281/zenodo.10213745
title: Summary statistic of a Trans ancestry multi-trait GWAS

description: Trans ancestry multi-trait GWAS by adapting the omnibus test to the trans ancestry setting. 
Genome wide summary statistics for 19 blood count traits were retrieved from Chen et al paper and were downloaded from the GWAS Catalog (https://www.ebi.ac.uk/gwas/publications/32888493#study_panel)
They curated using the JASS (Joint Analysis of Summary Statistics) pipeline https://gitlab.pasteur.fr/statistical-genetics/jass_suite_pipeline
See Troubat et al preprint for all details on the obtention of this dataset https://doi.org/10.1101/2023.06.23.546248