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986 results for “exchangeability”

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edi60/100

Net Carbon Exchange of an Old-Growth Hemlock Forest at Harvard Forest HEM Tower since 2000

This project estimates carbon exchange rates of multiple forest types at Harvard Forest (see HF072) and compares them to long-term ongoing carbon exchange measurements at the EMS, which is located in a mesic, 60-90 year old red oak and red maple dominated forest on abandoned farmland (HF004). Measurements in each forest type are used to investigate climatic influences on carbon exchange. This mesic hemlock-dominated forest with most trees 100-200 years old on undisturbed soils stored only about 3 Mg/ha of carbon in 2001, compared to over 4 Mg/ha in the 60-90 year old oak/maple stand. However, both sites stored more carbon in 2001 than was measured in the oak/maple stand in any previous year since 1991 (see HF004). The hemlock forest behaved very differently from the oak-maple stand in that the highest rates of carbon storage occurred in spring, while there was very little carbon storage in mid to late summer. Statistical models of carbon exchange in the hemlock forest showed that carbon storage was positively related to daily minimum air temperature in spring, but negatively correlated with soil temperature in the summer. The first effect was attributable to a positive influence of above-freezing minimum temperatures on photosynthesis by hemlock foliage. The negative relationship of soil temperature to carbon storage by hemlock forest in summer was due to exponentially increasing soil and ecosystem respiration, accompanied by a neutral or negative effect of high air temperature on photosynthesis by hemlock trees (see HF063). These effects indicate that carbon storage in the hemlock forest could be strongly affected by climate warming, but the effects will probably be in opposite directions in spring and summer.

openCC0Dec 2023View details →
edi60/100

Isotopic Composition of Net Ecosystem CO2 Exchange at Harvard Forest EMS Tower since 2011

This archive features long-term measurements of the eddy and storage fluxes of 16O12C16O, 16O13C16O, and 18O12C16O at the Harvard Forest EMS flux tower. Provided are the individual isotopologue fluxes, the total CO2 flux, the δ13C and δ18O isofluxes, and various ancillary flux and environmental data. The data are described in Wehr et al (2013), Long-term eddy covariance measurements of the isotopic composition of the ecosystem–atmosphere exchange of CO2 in a temperate forest, Agricultural and Forest Meteorology 181, 69–84. They are also analyzed in Wehr and Saleska (2015), An improved isotopic method for partitioning net ecosystem–atmosphere CO2 exchange, Agricultural and Forest Meteorology 214-215, 515–531, as well as in Wehr et al 2016, Seasonality of Temperate Forest Photosynthesis and Daytime Respiration, Nature (in press). The eddy (iso)fluxes were measured by eddy covariance (EC), with a 30- or 35-minute integration period on a 40- or 45-minute duty cycle (the precise duty cycle was changed during the record to accommodate various synergistic measurement campaigns). The storage fluxes were measured as the increase in storage below 29 m during the EC integration period, based on vertical integrations over 7 air sampling heights on the tower (0.2, 1.0, 7.5, 12.7, 18.1, 24.1, 29.0 m, prior to July 3, 2012), or over 6 air sampling heights on the tower (0.2, 1.0, 7.5, 12.7, 18.1, 29.0 m, after July 3, 2012). Some periods are missing at regular intervals because the system was being used for other measurements, not reported here. Corrected and uncorrected versions of the eddy (iso)fluxes are provided; the corrections account for high-frequency signal attenuation, and were made by comparing w-CO2 and w-T cospectra. The precise method is novel and complex and is described, along with all further details of the measurements, in Wehr et al (2013), Long-term eddy covariance measurements of the isotopic composition of the ecosystem–atmosphere exchange of CO2 in a temperat

openCC0Dec 2023View details →
edi60/100

Soil Gas Exchange in the Clearcut Site at Harvard Forest 2011-2013

Soil CO2 efflux was measured at the clear cut site beginning in 2011. That year a nearby spruce site was also measured for comparison. Soil respiration was measured in 2011 and 2012 with the LI-COR 6200 instrument and soil efflux was calculated later in the lab. In 2013 soil respiration was measured with the LI-COR 6400 instrument, which computed the fluxes internally. In 2012 three trenched plots were established at the clear cut site. Those were established by trenching a 2 x 2 meter perimeter to a depth of about 50 cm, severing any roots. The trenches were lined with heavy duty landscaping cloth and backfilled. Soil collars were installed in the middle of the trenched plots and measured in 2012 (1 large one used with the LI-6200 machine) and in 2013 (two smaller ones used with the LI-6400 machine). Sampling points were scattered around the site, along vegetation transects (near the EC tower, across the fire access road).

openCC0Dec 2023View details →
edi60/100

Leaf Gas Exchange in the Clearcut Site at Harvard Forest 2010-2012

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.

openCC0Dec 2023View details →
edi60/100

Canopy-Atmosphere Exchange of Carbon, Water and Energy at Harvard Forest EMS Tower since 1991

The tower-based CO2 measurements and key meteorological drivers are intended to examine how regional and ecosystem level processes in a mid-latitude forest contribute to global carbon cycling. Specifically, we endeavor to understand quantitatively how and why forested ecosystems take up or release carbon, on time scales from hours to decades, and to elucidate responses to climate changes and management interventions. The tower was installed 1989 and the resulting eddy-flux measurements constitute the longest running record of the net-ecosystem carbon exchange in a North American Forest. The resulting long-term record of Net Ecosystem Exchange (NEE) has shown the effects of climate anomalies on carbon fluxes for seasonal and annual time scales. For example, reduced soil frost allows greater respiration in the winter leading to lower C sequestration. Cumulative gross photosynthesis depends on when the canopy emerges in the spring. Warmer springtime temperatures lead to greater uptake of C. As the NEE record is extended and augmented by supporting ecological measurements, we can further identify longer-term effects of climate perturbations on carbon fluxes and further define the relationship between stand history and carbon sequestration. Climatic anomalies in one season or year may have a longer-term effect on the sequestration of carbon in subsequent seasons or years. The flux and ecological measurements are coordinated with studies at other sites through the AmeriFlux network. By examining the relationships between carbon fluxes and the driving physical and biological variables across a range of sites we are enhancing understanding of the processes that control NEE.

openCC0Mar 2024View details →
edi60/100

Physiological Model of CO2 Exchange by Hemlock Forests at Harvard Forest 1996-2000

A physiological model of carbon (C) exchange for a mature hemlock forest was developed, with separate component models for net photosynthesis (Pn), leaf respiration (Rl) , woody tissue respiration (Rw) and soil respiration (Rs). The model estimated that about 1.2 Mg C/ha was stored above and below ground between November 1, 1997 and October 31, 1998. This was generally a wet year with a wet and cloudy summer, except during August, which probably influenced the model output significantly. The whole-forest C exchange model estimated that most C storage in the forest occurred in spring. Warm temperatures with high soil moisture caused whole-forest respiration to exceed Pn during the summer, leading to a net C loss from the ecosystem. Leaf-level light-saturated Pn reached a maximum at about 20 deg C, then remained stable up to about 30 deg C, but at lower light levels Pn decreased above 20 deg C. This contributed to the lack of carbon storage during the summer, when the warmest days reached 30 to 32 deg C. Soil respiration was estimated at 60 to 75% of total ecosystem respiration, and during summer Rs increased exponentially with soil temperature with a Q10 of 3.8, so that from July through September, monthly Rs alone was 73 to 88% of total canopy Pn (Estimated monthly Rs ranged from 1.14 to 1.68 Mg/ha and estimated monthly Pn was 1.29 to 2.06 Mg/ha in July through September). A second major control on carbon storage by the hemlock forest was daily minimum temperature in spring and fall. There was no measurable Pn after daily minimum temperatures of -5 deg C or lower, although no effect of minimum temperature on Pn was observed for temperatures above 0 deg C.

openCC0Dec 2023View details →
edi60/100

Concentrations and Surface Exchange of Air Pollutants at Harvard Forest EMS Tower since 1990

In North America, anthropogenic activities such as fossil fuel combustion and high-intensity agriculture have increased the inputs of nitrogen oxides in the atmosphere far above natural, biogenic inputs. The effect of this excess N depends on how it is distributed through the environment. If fixed N is deposited as nitrate in forests, it may act as a "fertilizer", stimulating growth and thus enhancing carbon sequestration. But when accumulated deposition exceeds the nutritional needs of the ecosystem, nitrogen saturation may result. Soil fertility declines due to leaching of cations and thus, carbon uptake diminishes. The balance between fertilization and saturation depends on the spatial and temporal extent of nitrogen deposition. Measurements of nitrogen oxide concentrations and fluxes made at Harvard Forest are intended to quantify the deposition of nitrogen oxides and to examine the rates for oxidation and deposition of reactive nitrogen that are critical in controlling how far the influence of nitrogen oxide emission sources extends. Measurements made to date indicate that dry deposition of NOy to the Harvard Forest canopy is controlled by advection from source regions, vertical mixing, and chemical reaction. The input is about equally divided between wet and dry deposition depending on the amount of precipitation. Southwesterly winds bring air from the major urban areas along the mid-Atlantic coast, whereas northwesterly wind bring air from less populated regions of northern New England and Canada. As a result, southwesterly winds transport higher concentrations and fluxes of NOx and NOy than northwesterly winds. In the summer, aerodynamically rough forests intercept NOx and emit reactive hydrocarbons that accelerate the oxidation of NOx to rapidly depositing species. As a result, much of the NOx emitted by North America is retained by the region in the summer. This deposition leads to a summertime decrease in reactive nitrogen concentrations and fluxes relati

openCC0Dec 2023View details →
edi60/100

Net Carbon Exchange of a Young Upper-Slope Deciduous Forest at Harvard Forest LPH Tower 2002-2010

This data set contains sensible heat exchange, water vapor exchange and carbon exchange as well as environmental data for a deciduous forest dominated by red oak (Quercus rubra). It is 1.1 km WNW of the EMS tower where continuous eddy covariance measurements began in 1992 (see HF004). The High Deciduous site is about 385 m a.s.l., or 35 m higher in elevation than the EMS, which is situated in a relatively low area near a stream. The forest near this eddy covariance tower is broadly similar in species composition to the EMS site, but it is younger and shorter in stature. The site was cleared for pasture, but not deeply plowed or planted, in the 18th and19th centuries. Agriculture on the site was abandoned near the end of the 19th century. The forest within 200 to 300 m of the eddy covariance tower to the NW, W, SW, and S burned in an intense fire in 1957, which left few or no surviving trees.

openCC0Dec 2023View details →
edi60/100

Water samples collected for dissolved inorganic carbon and nutrient analysis during tidal creek lateral exchange measurements approximately every 15 minutes from beginning of flood tide to the following low tide, Rowley, MA, PIE LTER.

Measurement of the lateral exchange of nutrients, sediment, and carbon in tidal creek systems draining predominantly low-elevation marsh dominated by Spartina alterniflora and high-elevation marsh dominated by Spartina patens located in Rowley, MA.

openCC (other)Jun 2025View details →
zenodo52/100

Dataset of "Activity-stability relationship in magnetron co-sputtered bimetallic catalysts for proton exchange membrane fuel cells"

<p>In the present study, magnetron sputtered PtxM100-x (M = Co, Cu, Y; x = 25, 50, 75 and 100) bimetallic alloys were investigated as PEMFC cathodes. &nbsp;Accurate composition control enabled a systematic study of the correlation between alloy composition, activity, and stability. The catalysts underwent thorough characterization, employing a diverse portfolio of characterization techniques such as scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy and cyclic voltammetry. The activity of all investigated alloys was tested directly in a fuel cell device, while stability was assessed through potentiodynamic cycling in a half-cell.&nbsp;<br>The activity-stability index, considering experimental results for both activity and stability, was calculated and compared for all investigated catalysts. All alloys exhibited a volcano-type trend in activity-stability index as a function of the concentration of alloying element with peaks observed at Pt50Co50, Pt50Cu50 and Pt75Y25 for respective alloys, surpassing that of monometallic platinum. Overall, Pt50Co50 emerged as a catalyst with the highest activity-stability ratio.</p>

opencc-by-4.0Apr 2024View details →
zenodo52/100

Dataset of "MoO3-xNiMoO4 nanorods synthetized using NiO nanoparticles for hydrogen evolution in anion exchange membrane water electrolysis"

<p>Novel method of Mo-Ni catalyst for hydrogen evolution reaction in anion exchange membrane water electrolysis was used. Complete physico-chemical and electrochemical characterization was done. Prepared material showed enhanced performance when compared to the similar Ni based materials. Physico-chemical characterization showed, that final material is formed by NiMoO4 nanorods coverd on the surface by the layer of the MoO3-x.</p>

opencc-by-4.0Oct 2024View details →
zenodo52/100

GHG Dataset for the frontiers publication "Soil Nitrous Oxide Emission and Methane Exchange from Diversified Cropping Systems in Pannonian Region"

<p>GHG Dataset used in the Frontiers Publication &quot;Soil Nitrous Oxide Emission and Methane Exchange from Diversified Cropping Systems in Pannonian Region&quot;. Additionally including CO2 besides N2O and CH4. Includes 3 cropping seasons.</p> <p>The data is also available online on the GHG flux visualisation and calculation tool &quot;gasflxvis&quot;: https://sae-interactive-data.ethz.ch/gasflxvis/</p> <p>Further details on the calulation are provided both on gasflxvis and the Frontiers publication. Calculation procedure according the following PLOS ONE publication: http://dx.doi.org/10.1371/journal.pone.0200876</p>

opencc-by-4.0Mar 2022View details →
zenodo52/100

Dataset of "Microporous electrode binders for anion exchange membrane water electrolyzers"

<p>Membranes made of SEBS/DABCO/PIM-1 blends were prepared and characterized. In the next step, the several blends were used as polymer binder's of the catalysts layers. Characterization of SEBS-DABCO/PIM-1 blends in the form of the catalyst layer revealed the significance of the catalyst layer porosity, which controls the permeation of gasses.</p>

opencc-by-4.0Apr 2024View details →
zenodo52/100

S42 | HDXNOEX | Hydrogen Deuterium Exchange (HDX) Standard Set

<p>This is the collection associated with list S42 HDXNOEX on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S42</p> <p>HDXNOEX</p> <p><strong>Hydrogen Deuterium Exchange (HDX) Standard Set</strong></p> <p>HDXNOEX <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/HDXNOEX_14022019.xlsx">XLSX</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/HDXNOEX_14022019.csv">CSV</a> (14/02/2019)<br> CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/hdxnoex">HDXNOEX List</a><br> CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/hdxexch">HDXEXCH List</a></p> <p>HDXNOEX <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/HDXNOEX_InChIKeys_14022019.txt">InChIKeys</a> (14/02/2019)</p> <p>Environmental standard set used to investigate hydrogen deuterium exchange in small molecule HRMS (Ruttkies et al. accepted). <a href="https://comptox.epa.gov/dashboard/chemical_lists/hdxexch">HDXEXCH</a> list also contains observed deuterated species.&nbsp;</p>

opencc-by-4.0Feb 2019View details →
edi52/100

Gas exchange velocities (k600), gas exchange rates (K600), and hydraulic geometries for streams and rivers derived from the NEON Reaeration field and lab collection data product (DP1.20190.001)

This dataset contains estimates of gas exchange velocity, gas exchange rate, and hydraulic parameters for streams calculated from tracer-gas experiments and conservative tracer injections collected by the National Ecological Observatory Network (NEON). All input data were collected by NEON and is available on the NEON data portal at https://data.neonscience.org. Specifically, the NEON Reaeration field and lab collection data product (DP1.20190.001) was used to calculate these estimates. Gas exchange was estimated in two ways: first, following an unpooled frequentist approach and second, following a partially pooled Bayesian approach. In addition, a salt-correction was applied to gas exchange estimates for sites where it was possible and necessary. All estimates of gas exchange are included in the file gasExchange_ds.csv. A recommended selection of these estimates is included in the dataset (best_k600_mPerDay and best_K600_mPerDay). The stanfit objects used for the partially pooled Bayesian approach are also included as site-specific model objects for gas exchange velocities and rates. In addition, water velocity was calculated from conservative tracer injections, and mean water depth was calculated from these water velocity estimates and measurements of wetted width and water discharge. All hydraulic parameters are included in the file hydraulics_ds.csv. All processing code is available in the reaRates R package. NEON is sponsored by the National Science Foundation (NSF) and operated under cooperative agreement by Battelle. This material is based in part upon work supported by NSF through the NEON Program.

openCC (other)Oct 2024View details →
edi52/100

Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE): Salt Exchangeable Cation Extractions from Hubbard Brook and Bartlett sites

Soil element concentrations (Na, Mg, K, Ca, Al, Mn, Fe, Si, Sr, and Ba) were measured in salt exchangeable extracts of soil samples taken in July 2017 in the MELNHE study, specifically in Bartlett stands C1-C9 and Hubbard Brook stands HBM and HBO. Additional detail on the MELNHE project, including a datatable of site descriptions and a pdf file with the project description and diagram of plot configuration can be found in this data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=344. Additional analysis data on these samples can be found in the dataset "Soil properties in the MELNHE study at Hubbard Brook Experimental Forest, Bartlett Experimental Forest and Jeffers Brook, central NH USA, 2009 - present" (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=165). This work is a contribution of the Hubbard Brook Ecosystem Study. Hubbard Brook is part of the LTER network, which is supported by the US National Science Foundation. The Hubbard Brook Experimental Forest is operated and maintained by the US Department of Agriculture, Forest Service, Northern Research Station.

openCC (other)Jan 2025View details →
edi52/100

Carbon exchange responses of rehydrated and incubated biological soil crust samples from White Sands National Park in 2020-2022

This dataset contains photosynthetic light response data from biological soil crusts collected from a gypsum sand sheet at White Sands National Park, NM, USA in three different seasons. This study aims to 1) assess the carbon fixation capacity of biocrust types; 2) assess biocrust carbon fixation response under varying incubation times; 3) and understand variability in carbon fixation response in different seasons. Sample collection occurred in July 2020 (summer), September 2021 (fall), and March 2022 (winter). The biocrust types of interest were light cyanobacterial, dark cyanobacterial, Peltula lichen, Clavascidium lichen, and moss crusts. Samples were collected with the intention of taking carbon fixation measurements after different incubation periods (30 min, 2 hr, 6 hr, 12hr, or 24 hr in 2020, and 30 min, 2 hr, 6 hr, 12hr, 24 hr, or 36 hr in 2021 and 2022). For each condition (biocrust type and incubation time) there were five replicates in 2020 (total n=125) and ten replicates in 2021 and 2022 (total n=300). After collection, the intact samples were re-wetted and subjected to their respective incubation period and measured for photosynthetic response. The resulting light response curves and photosynthetic information was be used for comparing biocrust type, incubation time response differences, and seasonal variation to understand variability of biocrust carbon flux response at a single site. This data set includes the light response curve values and photosynthetic data calculated from these curves and raw LICOR output files compiled into 3 spreadsheet files. The included 2020 data is also associated with the White Sands National Park data from Jornada Study 549. This dataset accompanies the in-press article by Hoellrich et al. (2023) cited below, and the study is now complete. Hoellrich, Mikaela R., Darren K. James, David Bustos, Anthony Darrouzet-Nardi, Louis S. Santiago, and Nicole Pietrasiak. "Biocrust carbon exchange varies with crust type and time on Ch

openCC (other)Apr 2023View details →
zenodo48/100

Data for: "Direct photochemical control of imine exchange reactions"

<div>This dataset is all of the data produced which relates to the text "Direct photochemical control of imine exchange&nbsp;reactions"</div> <div>&nbsp;</div> <div>The data set is separated loosely into&nbsp;</div> <div>&nbsp;</div> <div>1) Computational-data : All of the simulated data<br>&nbsp;<br>2) Kinetics : All of the data which lead to the nmr-time monitored experiments, where samples were equilibrated then irradiated and heated<br>&nbsp;<br>3) Photophysical-characterisation : All UV-VIS spectra and luminance spectra<br>&nbsp;<br>4) Synthetic-data-and-charcterisation : the details of the synthetises, and the 1H NMR, 13C NMR, IR, Mass-Spec, and Elemental analysis data<br>&nbsp;<br>&nbsp;<br>Generally within these folders, subfolders, subsubfolders etc. the folders contain zipped HTML copies of the lab notebooks, images of the graphs which result from them, and code which has generated them. Within further folders will be data which produces these graphs.<br>&nbsp;<br>&nbsp;<br>The way to interact with the compressed HTML lab notebooks, is to unzip them, and then open the HTML files.<br>&nbsp;<br>&nbsp;<br>Warning: The code for generating the graphs has not been cleaned up; it is presented as it was at time of publication. It will take some time for you to follow it, not because it is complex, but because it includes a lot of unnecessary diversions. Often I was working out how to process the data as I programmed them.<br>&nbsp;</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>Where to find this data for each figure is given as follows:</div> <div>&nbsp;</div> <div>Figure 1: -not data-</div> <div>&nbsp;</div> <div>Figure 2: .\photophysical-characterisation\Imines-UV-VIS</div> <div>&nbsp;</div> <div>Figure 3: .\Kinetics\Kinetic-main</div> <div>&nbsp;</div> <div>Figure 4: .\Kinetics\Kinetic-temperature</div> <div>&nbsp;</div> <div>Figure 5: .\Computational-data</div> <div>&nbsp;</div> <div>Figure S1: .\photophysical-characterisation\Amines-UV-VIS</div> <div>&nbsp;</div> <div>Figure S2: .\Kinetics\Supplementary-kinetic</div> <div>&nbsp;</div> <div>Figure S3: .\Kinetics\Supplementary-temperature-kinetic</div> <div>&nbsp;</div> <div>Figure S4: .\Computational-data</div> <div>&nbsp;</div> <div>Figure S5: .\Kinetics\Kinetic-main\nmr\A-4-20-1mnova.mnova</div> <div>&nbsp;</div> <div>Figure S6: .\Kinetics\Kinetic-main\nmr\A-4-21-1mnova.mnova</div> <div>&nbsp;</div> <div>Figure S7: .\Synthetic-data-and-characterisation\[compound-data]\1H-NMR</div> <div>&nbsp;</div> <div>Figure S8: .\Synthetic-data-and-characterisation\[compound-data]\1H-NMR</div> <div>&nbsp;</div> <div>Figure S9: .\photophysical-characterisation\LED-Luminence</div> <div>&nbsp;</div> <div>Figure S10: .\photophysical-characterisation\LED-Luminence</div> <div>&nbsp;</div> <div>Figure S11: -not data-</div> <div>&nbsp;</div> <div>Figure S12: -not data-</div> <div>&nbsp;</div> <div>Figure S13: .\Synthetic-data-and-characterisation\Characterisation_A-imine\1H-NMR</div> <div>&nbsp;</div> <div>Figure S14: .\Synthetic-data-and-characterisation\Characterisation_MA-imine\1H-NMR</div> <div>&nbsp;</div> <div>Figure S15: .\Synthetic-data-and-characterisation\Characterisation_DMMA-imine\1H-NMR</div> <div>&nbsp;</div> <div>Figure S16: .\Synthetic-data-and-characterisation\Characterisation_FLUR-imine\1H-NMR</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>The dataset contains details of the following compounds:</div> <div>&nbsp;</div> <div>Article name: A-Imine</div> <div>&nbsp;</div> <div>IUPAC name: (E)-N-phenyl-1-(thieno[3,2-b]thiophen-2-yl)methanimine</div> <div>&nbsp;</div> <div>SMILES Code: C1(/N=C/C2=CC(SC=C3)=C3S2)=CC=CC=C1</div> <div>&nbsp;</div> <div>SLN: C[2](N=[S=I]CC[8]=CC(SC=C[16])=C@16S@9)=CC=CC=C@3</div> <div>&nbsp;</div> <div>InChI: 1S/C13H9NS2/c1-2-4-10(5-3-1)14-9-11-8-13-12(16-11)6-7-15-13/h1-9H/b14-9+</div> <div>&nbsp;</div> <div>InChI key: FMENSGUUZYOJTA-NTEUORMPSA-N</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>Article name: DMMA-Imine</div> <div>&nbsp;</div> <div>IUPAC name: (E)-N,N-dimethyl-4-((thieno[3,2-b]thiophen-2-ylmethylene)amino)aniline</div> <div>&nbsp;</div> <div>SMILES Code: CN(C)C1=CC=C(/N=C/C2=CC(SC=C3)=C3S2)C=C1</div> <div>&nbsp;</div> <div>SLN: CN(C)C[1]=CC=C(N=[S=I]CC[9]=CC(SC=C[17])=C@17S@10)C=C@2</div> <div>&nbsp;</div> <div>InChI: InChI=1S/C15H14N2S2/c1-17(2)12-5-3-11(4-6-12)16-10-13-9-15-14(19-13)7-8-18-15/h3-10H,1-2H3/b16-10+</div> <div>&nbsp;</div> <div>InChI key: XWBXQJOJYSCJCK-MHWRWJLKSA-N</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>Article name: FLUR-Imine</div> <div>&nbsp;</div> <div>IUPAC name: (E)-N-(9H-fluoren-2-yl)-1-(thieno[3,2-b]thiophen-2-yl)methanimine</div> <div>&nbsp;</div> <div>SMILES Code: C1(C=CC=C2)=C2C(C=CC(/N=C/C3=CC(SC=C4)=C4S3)=C5)=C5C1</div> <div>&nbsp;</div> <div>SLN: C[1](C=CC=C[13])=C@13C(C=CC(N=[S=I]CC[16]=CC(SC=C[23])=C@23S@16)=C[8])=C@9C@2</div> <div>&nbsp;</div> <div>InChI: InChI=1S/C20H13NS2/c1-2-4-17-13(3-1)9-14-10-15(5-6-18(14)17)21-12-16-11-20-19(23-16)7-8-22-20/h1-8,10-12H,9H2/b21-12+</div> <div>&nbsp;</div> <div>InChI key: RSZKSUGPHCONDB-CIAFOILYSA-N</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>Article name: MA-Imine</div> <div>&nbsp;</div> <div>IUPAC name: (E)-1-(thieno[3,2-b]thiophen-2-yl)-N-(p-tolyl)methanimine</div> <div>&nbsp;</div> <div>SMILES Code: CC1=CC=C(/N=C/C2=CC(SC=C3)=C3S2)C=C1</div> <div>&nbsp;</div> <div>SLN: CC[1]=CC=C(N=[S=I]CC[9]=CC(SC=C[17])=C@17S@10)C=C@2</div> <div>&nbsp;</div> <div>InChI: InChI=1S/C14H11NS2/c1-10-2-4-11(5-3-10)15-9-12-8-14-13(17-12)6-7-16-14/h2-9H,1H3/b15-9+</div> <div>&nbsp;</div> <div>InChI key: XARWNOWDJRWLJY-OQLLNIDSSA-N</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>IUPAC name: (E)-4-((thieno[3,2-b]thiophen-2-ylmethylene)amino)benzonitrile</div> <div>&nbsp;</div> <div>SMILES Code: N#CC1=CC=C(/N=C/C2=CC(SC=C3)=C3S2)C=C1</div> <div>&nbsp;</div> <div>SLN: N#CC[5]=CC=C(N=[S=I]CC[8]=CC(SC=C[16])=C@16S@9)C=C@6</div> <div>&nbsp;</div> <div>InChI: InChI=1S/C14H8N2S2/c15-8-10-1-3-11(4-2-10)16-9-12-7-14-13(18-12)5-6-17-14/h1-7,9H/b16-9+</div> <div>&nbsp;</div> <div>InChI key: MTCNXZQWYYVDCG-CXUHLZMHSA-N</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>IUPAC name: (E)-N-(4-methoxyphenyl)-1-(thieno[3,2-b]thiophen-2-yl)methanimine</div> <div>&nbsp;</div> <div>SMILES Code: COC1=CC=C(/N=C/C2=CC(SC=C3)=C3S2)C=C1</div> <div>&nbsp;</div> <div>SLN: COC[5]=CC=C(N=[S=I]CC[8]=CC(SC=C[16])=C@16S@9)C=C@6</div> <div>&nbsp;</div> <div>InChI: InChI=1S/C14H11NOS2/c1-16-11-4-2-10(3-5-11)15-9-12-8-14-13(18-12)6-7-17-14/h2-9H,1H3/b15-9+</div> <div>&nbsp;</div> <div>InChI key: WIBJKKCQZPFCIZ-OQLLNIDSSA-N</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>Article name: A-Amine</div> <div>&nbsp;</div> <div>IUPAC name: Benzenamine</div> <div>&nbsp;</div> <div>Common name: Aniline</div> <div>&nbsp;</div> <div>SMILES Code: NC1=CC=CC=C1</div> <div>&nbsp;</div> <div>SLN: NC[2]=CC=CC=C@3</div> <div>&nbsp;</div> <div>InChI: InChI=1S/C6H7N/c7-6-4-2-1-3-5-6/h1-5H,7H2</div> <div>&nbsp;</div> <div>InChI key: PAYRUJLWNCNPSJ-UHFFFAOYSA-N</div> <div>&nbsp;</div> <div>CAS no: 62-53-3</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>Article name: MA-Amine</div> <div>&nbsp;</div> <div>IUPAC name: 4-Aminotoluene</div> <div>&nbsp;</div> <div>Common name: p-toludine</div> <div>&nbsp;</div> <div>SMILES Code: NC1=CC=CC=C1</div> <div>&nbsp;</div> <div>SLN: NC[2]=CC=CC=C@3</div> <div>&nbsp;</div> <div>InChI: InChI=1S/C6H7N/c7-6-4-2-1-3-5-6/h1-5H,7H2</div> <div>&nbsp;</div> <div>InChI key: PAYRUJLWNCNPSJ-UHFFFAOYSA-N</div> <div>&nbsp;</div> <div>CAS no: 106-49-0</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>Article name: DMMA-Amine</div> <div>&nbsp;</div> <div>IUPAC name: N1,N1-dimethylbenzene-1,4-diamine</div> <div>&nbsp;</div> <div>SMILES Code: NC1=CC=C(N(C)C)C=C1</div> <div>&nbsp;</div> <div>SLN: NC[2]=CC=C(N(C)C)C=C@3</div> <div>&nbsp;</div> <div>InChI: InChI=1S/C8H12N2/c1-10(2)8-5-3-7(9)4-6-8/h3-6H,9H2,1-2H3</div> <div>&nbsp;</div> <div>InChI key: BZORFPDSXLZWJF-UHFFFAOYSA-N</div> <div>&nbsp;</div> <div>CAS no: 99-98-9</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>Article name: FLUR-Amine</div> <div>&nbsp;</div> <div>IUPAC name: 9H-fluoren-2-amine</div> <div>&nbsp;</div> <div>SMILES Code: NC1=CC(CC2=C3C=CC=C2)=C3C=C1</div> <div>&nbsp;</div> <div>SLN: NC[2]=CC(CC[8]=C[9]C=CC=C@9)=C(@10)C=C@3</div> <div>&nbsp;</div> <div>InChI: InChI=1S/C13H11N/c14-11-5-6-13-10(8-11)7-9-3-1-2-4-12(9)13/h1-6,8H,7,14H2</div> <div>&nbsp;</div> <div>InChI key: CFRFHWQYWJMEJN-UHFFFAOYSA-N</div> <div>&nbsp;</div> <div>CAS no: 153-78-6</div> <div>&nbsp;</div> <div>&nbsp;</div> <div>IUPAC name: 4-aminobenzonitrile</div> <div>&nbsp;</div> <div>SMILES Code: NC1=CC=C(C#N)C=C1</div> <div>&nbsp;</div> <div>SLN: NC[2]=CC=C(C#N)C=C@3</div> <div>&nbsp;</div> <div>InChI: InChI=1S/C7H6N2/c8-5-6-1-3-7(9)4-2-6/h1-4H,9H2</div> <div>&nbsp;</div> <div>InChI key: YBAZINRZQSAIAY-UHFFFAOYSA-N</div> <div>&nbsp;</div> <div>CAS no: 873-74-5</div> <div>&nbsp;</div>

opencc-by-4.0Nov 2024View details →
zenodo48/100

Net Ecosystem Exchange, Ecosystem Respiration and meteoclimatic data of Alpine grasslands at Nivolet Plain, Gran Paradiso National Park, Italy 2017-2023

<p>This dataset presents georeferenced measurements collected at the Nivolet Plain in Gran Paradiso National Park (GPNP), western Italian Alps. The dataset includes the Net Ecosystem Exchange (NEE), Ecosystem Respiration (ER) and meteo-climatic variables, i.e. air and soil temperature, air relative humidity, soil volumetric water content, atmospheric pressure and solar irradiance. The measurements were conducted between 2017 and 2023 at five different sites at an elevation of approximately 2550-2750 meters a.s.l.</p> <p>To estimate NEE and ER, we employed the flux chamber method, measuring the temporal variation of carbon dioxide (CO2) concentration inside the chamber over a period of about 90 seconds. We used a customized portable non-steady-state dynamic flux chamber, paired with an InfraRed Gas Analyzer (IRGA) and a portable weather station. Measurements were taken at around 20 points per site during the snow-free season, spanning from June to October.</p> <p>The dataset is provided in a comma-separated text file (.csv) format. Each record corresponds to a single measurement point, with semicolons used as separators. The "NA" notation indicates values that are not available or have been excluded during quality control processes (e.g., due to battery failure). We use point as decimal separator.</p> <p>The sign convention for the fluxes is: a negative value indicates a CO2 flux from the atmosphere to the ecosystem, while a positive value represents a CO2 flux from the soil/ecosystem to the atmosphere. Consequently, ER values are positive, while NEE values can be&nbsp;positive or negative. The units for NEE and ER fluxes are molCO2 m-2 day-1 and &mu;molCO2 m-2 second-1. The first values in each record of the dataset indicate the observation details (sampling date, site, etc.), followed by the corresponding measured or calculated variables. NEE and ER values were estimated from the slope of the linear regression of CO2 concentration over time (ppm s-1) using a laboratory calibration curve.</p> <p>The calibration curve was created by relating known and pre-set CO2 fluxes (within the range expected in the field) with the corresponding measured slopes. The flux values were then scaled up based on the area of the chamber base&nbsp;(0.036 m2) and adjusted using the ratio of atmospheric pressure and air temperature during the measurement to those recorded during the calibration in the laboratory.</p>

opencc-by-4.0Jan 2023View details →
zenodo48/100

S95 | PFASANEXCH | PFAS List from the NORMAN PFAS Analytical Exchange Activity

<p>This is the collection associated with list S95 PFASANEXCH on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>This is a list from the <a href="https://www.norman-network.net/sites/default/files/files/QA-QC%20Issues/2021%20NORMAN%20network%20PFAS%20Analytical%20Exchange%20Final%20Report%2014022022.pdf">PFAS Analytical Exchange Activity</a>, part of NORMAN Joint Programme of Activities (JPA) 2021 coordinated by UK Environment Agency. This activity aimed to gain an understanding of the current analytical capability of PFAS as Limit of Detection (LOD) in participating international laboratories.</p>

opencc-by-4.0Mar 2022View details →

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

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neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

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behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

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electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record