Find research datasets worth reusing

openCC (other)Jan 2026View details →

Secchi depth data and discrete depth profiles of water temperature, dissolved oxygen, conductivity, specific conductance, photosynthetic active radiation, oxidation-reduction potential, and pH for Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2013-2025

Discrete depth profiles of water temperature, dissolved oxygen, oxidation-reduction potential, conductivity, specific conductance, and pH were collected with multiple handheld water quality probes and discrete depth profiles of photosynthetically active radiation (PAR) were collected with a LI-COR underwater light meter from 2013 to 2025 in five drinking water reservoirs in southwestern Virginia, USA. These reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Falling Creek Reservoir (Vinton, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the Town of Pulaski, Virginia. All discrete depth profiles were collected on approximately 1-meter intervals. The data package consists of two datasets: 1) Secchi depth data; and 2) discrete depth profiles of multiple water quality variables measured by handheld sensors. The Secchi data and discrete depth profiles were measured at the deepest site of each reservoir adjacent to the dam, as well as other in-reservoir sites. Handheld sensor measurements were also collected at a gauged weir on the primary inflow tributary, other inflows, and outflows at Falling Creek Reservoir; inflows and outflows at Beaverdam Reservoir; and inflows at Carvins Cove Reservoir. In 2021, YSI handheld data were also collected from a littoral site in Beaverdam Reservoir. In 2025, YSI handheld data were collected monthly from June to October from nine littoral sites around the perimeter of Falling Creek Reservoir. From 2024 - 2025, additional within-reservoir depth profiles were collected in Carvins Cove Reservoir and multiple sites. Data were collected approximately fortnightly in the spring months (March - Ma

openCC (other)Jan 2026View details →

Time series of high-frequency profiles of depth, temperature, dissolved oxygen, conductivity, specific conductance, chlorophyll a, turbidity, pH, oxidation-reduction potential, photosynthetically active radiation, colored dissolved organic matter, phycocyanin, phycoerythrin, and descent rate for Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2013-2025

Depth profiles of water biogeochemical properties were collected with SeaBird Electronics (SBE) Conductivity, Temperature, and Depth (CTD) profilers from 2013-2025 at five drinking water reservoirs in southwestern Virginia, USA. The study reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Falling Creek Reservoir (Vinton, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the town of Pulaski, Virginia. The dataset consists of CTD depth profiles measured at the deepest site of each reservoir adjacent to the dam as well as other upstream reservoir sites. The profiles were collected approximately fortnightly in the spring months, weekly in the summer and early autumn, and monthly in the late autumn and winter. Beaverdam Reservoir, Carvins Cove Reservoir, and Falling Creek Reservoir were sampled every year in the dataset (2013-2025); Spring Hollow Reservoir was only sampled 2013-2017 and 2019; and Gatewood Reservoir was only sampled in 2016. Data availability differs across years due to additional sensors that have been added or replaced over time. From 2013-2016, profiles were taken with a CTD equipped with an SBE 43 Dissolved Oxygen sensor and an ECO FLNTU sensor for turbidity and chlorophyll. From 2017-2025, profiles were taken with a CTD equipped with an SBE 43 Dissolved Oxygen sensor, an ECO FLNTU sensor for turbidity and chlorophyll, a PAR-LOG ICSW sensor for photosynthetically active radiation, and a SBE 27 pH and ORP (oxidation-reduction potential) sensor. In 2022 and 2023, profiles were also taken with an additional CTD equipped with an SBE 43 Dissolved Oxygen sensor; an ECO Triplet Scattering Fluorescence sensor for

openCC (other)Dec 2021View details →

Nitrogen mineralization potential in soils collected from the Jornada Basin LTER-I transect and extracted at field collection time, 1989

This data package contains nitrogen mineralization data from soils collected along the Jornada Basin LTER (LTER-I) transects in southern New Mexico, USA. These transects are located in a livestock exclosure established in 1982 in the Chihuahuan Desert Rangeland Research Center (CDRRC) and run from the middle of the College Playa up to the foot of Mt. Summerford (2.7 km in length). Prior to the exclosure, the study site was moderately to heavily grazed for the past 100 years. The Treatment transect was treated annually with ammonium nitrate fertilizer (NH4NO3 at 10g N/m2/yr) until 1987. Along each transect, 91 stations, each with a plant intercept line, are spaced at 30 meter intervals. For this dataset, 60 soil samples (total) were collected along the control and fertilized treatment transects and mixed with potassium chloride solution (KCl) on Nov 27, 1989, then filter extracted the following day. The dataset contains a soil moisture correction factor, sample weights, total inorganic nitrogen (NO3+NO2-N), and nitrogen in ammonium (NH4-N) for Week F (field) of nitrogen mineralization potentials. The soil mineralization data complements the biomass harvest measurements that occurred in September 1989 (dataset knb-lter-jrn.210015001). This study is complete.

openCC (other)Dec 2021View details →

Nitrogen mineralization potential in soils collected from the Jornada Basin LTER-I transect and extracted at incubation time 0, 1989

This data package contains nitrogen mineralization data from soils collected along the Jornada Basin LTER (LTER-I) transects in southern New Mexico, USA. These transects are located in a livestock exclosure established in 1982 in the Chihuahuan Desert Rangeland Research Center (CDRRC) and run from the middle of the College Playa up to the foot of Mt. Summerford (2.7 km in length). Prior to the exclosure, the study site was moderately to heavily grazed for the past 100 years. The Treatment transect was treated annually with ammonium nitrate fertilizer (NH4NO3 at 10g N/m2/yr) until 1987. Along each transect, 91 stations, each with a plant intercept line, are spaced at 30 meter intervals. For this dataset, 60 soil samples (total) were collected along the control and fertilized treatment transects and mixed with potassium chloride solution (KCl) on Nov 27, 1989, then filter extracted four days later to give a time = 0 incubation value. The dataset contains a soil moisture correction factor, sample weights, total inorganic nitrogen (NO3+NO2-N), and nitrogen in ammonium (NH4-N) for Week 0 of nitrogen mineralization potentials. The soil mineralization data complements the biomass harvest measurements that occurred in September 1989 (dataset knb-lter-jrn.210015001). This study is complete.

opencc-by-4.0Jan 2020View details →

Rooftop photovoltaic (PV) potential data for the Swiss building stock

The provided dataset contains data for the PV potentials on building rooftops, evaluated for 9.6 M roof surfaces in Switzerland in an hourly temporal resolution. The methodology of the generation of the dataset is described in: Walch, Alina, Roberto Castello, Nahid Mohajeri, and Jean-Louis Scartezzini. “Big Data Mining for the Estimation of Hourly Rooftop Photovoltaic Potential and Its Uncertainty.” Applied Energy 262 (March 15, 2020): 114404. In the process of generating this dataset, the following aspects were included: <ul> <li>Meteorological conditions in Switzerland (solar radiation, temperature, snow cover)</li> <li>Local shading and sky coverage from surrounding buildings and trees (based on a Digital Surface Model)</li> <li>Obstruction of roof surface due to roof superstructures such as dormers and chimneys (estimated based on data from the canton of Geneva)</li> <li>The panel and inverter efficiencies, as a function of the solar radiation and temperature</li> </ul> Several aspects were estimated and hence include some uncertainty, due to the input datasets and the modelling methodology. For details on the sources of uncertainty and the limitations, please refer to the referenced article. Estimates for these uncertainties are provided alongside the variables. A description of the metadata is provided in the document rooftop_PV_CH_metadata_V1.pdf. Data description: The rooftop PV potential data has been computed at monthly-mean-hourly temporal resolution (i.e. 24 hours for each of the 12 months) for each individual roof surface, based on a national roof surface dataset created by SwissTopo (see https://www.uvek-gis.admin.ch/BFE/sonnendach/). The data given in this dataset is aggregated, in order to make the data easier to use for studies inside as well as outside Switzerland, to reduce the file size and to respect license agreements. Two types of aggregation are provided: <ol> <li>Aggregation per building, using the object ID of the SwissBuildings3D cadastre as identifier. </li> <li>Aggregation per roof type, separating between 4 categories: Tilt angle, aspect angle, roof area, altitude</li> </ol> If a different type of aggregation or the data per individual roof surface is required, please do not hesitate to get in touch with the authors directly.

opencc-by-4.0Jan 2019View details →

Simulated NGS read datasets for bacterial pathogenic potential prediction

## Predicting pathogenic potentials from NGS reads: novel bacterial species This repository contains simulated Illumina read datasets for bacterial pathogenic potential prediction and associated metadata extracted from the IMG Database (https://img.jgi.doe.gov/). The reads are 250bp long and were simulated with Mason (https://www.seqan.de/apps/mason/) from genomes downloaded from NCBI. The training-validation-test split was done on the species level to ensure "novelty" of validation and test species. The training sets contain 10 million reads per class, validation sets - 1.25 million reads per class, and test sets - 1.25 million paired reads per class. Additional, imbalanced training sets contain 2.5 million "nonpathogenic" and 17.5 million "pathogenic" reads, keeping the mean covarage constant for all species. The temporal benchmark test set contains reads from 3 additional pathogenic species in the Pantoea genus. ## Predicting pathogenic potentials from NGS reads: novel strains of known species The BacPaCS datasets contain reads simulated from the dataset compiled by Barash et al. (https://doi.org/10.1093/bioinformatics/bty928). It this case, the training-validation-test split was done on the strain level (so different strains of the same species may be present in all three sets).

opencc-by-4.0Apr 2024View details →

Dataset / Code: Targeted protein degradation in mycobacteria uncovers antibacterial effects and potentiates antibiotic efficacy

Targeted protein degradation in mycobacteria uncovers antibacterial effects and potentiates antibiotic efficacy   Harim I. Won1,#, Samuel Zinga1,#, Olga Kandror1, Tatos Akopian1, Ian D. Wolf1, Jessica T.P. Schweber1, Ernst W. Schmid2, Michael C. Chao1, Maya Waldor1, Eric J. Rubin1,*, Junhao Zhu1,3,*   1Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA. 2Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Blavatnik Institute, Boston, Massachusetts 02115, USA. 3CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China. #These authors contributed equally to this work. *Corresponding authors: <a href="mailto:zhujh@im.ac.cn">zhujh@im.ac.cn</a> (J.Z.), <a href="mailto:erubin@hsph.harvard.edu">erubin@hsph.harvard.edu</a> (E. J. R.)   Abstract Proteolysis-targeting chimeras (PROTACs) represent a new therapeutic modality involving selectively directing disease-causing proteins for degradation through proteolytic systems. Our ability to exploit targeted protein degradation (TPD) for antibiotic development remains nascent due to our limited understanding of which bacterial proteins are amenable to a TPD strategy. Here, we use a genetic system to model chemically-induced proximity and degradation to screen essential proteins in Mycobacterium smegmatis (Msm), a model for the human pathogen M. tuberculosis (Mtb). By integrating experimental screening of 72 protein candidates and machine learning, we find that drug-induced proximity to the bacterial ClpC1P1P2 proteolytic complex leads to the degradation of many endogenous proteins, especially those with disordered termini. Additionally, TPD of essential Msm proteins inhibits bacterial growth and potentiates the effects of existing antimicrobial compounds. Together, our results provide biological principles to select and evaluate attractive targets for future Mtb PROTAC development, as both standalone antibiotics and potentiators of existing antibiotic efficacy.

opencc-by-4.0Sep 2024View details →

Current and future European potential vegetation types

This dataset contains Potential Natural Vegetation (PNV) estimates for the European continent at 1km grain size. Estimates are made for six different vegetation types following the MAES Ecosystem classification at level 1. The predictions have been made through an ensemble of Bayesian Habitat distribution models available through the ibis.iSDM package <a href="https://doi.org/10.1016/j.ecoinf.2023.102127" target="_blank" rel="noopener">(Jung 2023)</a>. For more information on the methodology, original data and used covariates, please see the accompanying preprint (<a href="https://doi.org/10.31223/X59H71">Jung 2024</a>). Uploaded are: <ul> <li>The most likely current PNV transition (see screenshot) as categorical raster (and screenshot, see png) (Classes: 1=Woodland.and.forest | 2=Heathland.and.shrub | 3=Grassland | 4=Sparsely.vegetated.areas | 5=Wetlands | 6=Marine.inlets.and.transitional.waters)</li> <li>Current PNV estimates as cloud-optimized geoTIFF ("COG") files (.tif)</li> <li>Future PNV estimates (zipped) for each considered SSP - GCM combination as geoTIFF (.tif).</li> </ul> Variable naming scheme: Current: "pnv_XX_laea_1km.tif" where XX represents the vegetation type Future: Here the hierachical organization scheme of Essential Biodiversity Variables (EBV) is followed where files are separated in folders by Scenario | metric | entity | time, so for example "SSP126-GFDL-ESM4/suitability_mean/grassland/" Filenames are labelled by the date (e.g. "2040.tif"). Metrics and layers names and their interpretation: For current: "mean" = Average Ensemble posterior prediction "sd" = Standard deviation of posterior prediction "q05" = Lower percentile (5%) of posterior prediction "q50" = Median or 50% percentile of posterior prediction "q95" = Upper percentile (95%) of posterior prediction "mode" = Most commonly encountered value of posterior prediction "cv" = Coefficient of variation of posterior prediction For future: "mean" = Average Ensemble posterior prediction "q05" = Lower percentile (5%) of posterior prediction "q50" = Median or 50% percentile of posterior prediction "q95" = Upper percentile (95%) of posterior prediction --- Data properties: <table> <tbody> <tr> <td>Shared Socioeconomic Pathways (SSP)</td> <td>SSP1-2.6, SSP2-4.5, SSP5-8.5</td> </tr> <tr> <td>General circulation models (GCMs)</td> <td>GFDL-ESM4,  IPSL-CM6A-LR,  MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL </td> </tr> <tr> <td>Spatial grain</td> <td>1 km²</td> </tr> <tr> <td>Geographic projection</td> <td>LAEA</td> </tr> <tr> <td>Temporal grain</td> <td>30 year climatologies</td> </tr> <tr> <td>Spatial extent</td> <td>Continental Europe including Turkey (see screenshot)</td> </tr> <tr> <td>Temporal extent</td> <td>1990 to 2020 (Current), 2020 - 2100 (Future)</td> </tr> <tr> <td>Number of variables/entities</td> <td>7</td> </tr> </tbody> </table> All files are provided as is and the author takes no responsibility for errors or misuse and misinterpretation.

opencc-by-4.0May 2024View details →

Appendix - Potential COVID-19 test fraud detection: Findings from a pilot study comparing conventional and statistical approaches

The methods and results of the publication "COVID-19 test fraud detection: Findings from a pilot study comparing conventional and statistical approaches" are described in more detail in this appendix. The R-syntax for the calculation is provided, as well as a pseudo data set with which the syntax can also be tested.

opencc-by-4.0Jul 2024View details →

Dataset of "Characterization of Silicon-based Fibers Prepared by Electrospinning for Potential Li-ion Battery Anodes"

The rapid growth of electric vehicles (EVs) is driven by advances in lithium-ion batteries (LIBs), particularly in anode materials. Graphite electrodes, widely used for their high porosity, conductivity, low weight, and cost-effectiveness, face competition from monocrystalline silicon. Silicon anodes offer higher capacity and energy density, and they are safer because of their nonflammable nature. However, silicon's tendency to expand and contract during cycling presents challenges. This study explores the use of silicon nano- and microfibers to enhance battery stability, addressing these issues effectively. Monocrystalline silicon particles, obtained through milling and sieving, were used as the active component in the nanofibers. These particles, combined with organic precursors (PVP and TEOS), were processed using electrospinning to form fibers. The fibers were then annealed at 650 °C to remove the polymeric PVP component.  The results provide valuable insights into the properties and interactions of the silicon nanofibers, highlighting their potential in advanced energy storage devices.

opencc-by-4.0Jul 2024View details →

Unveiling the potential of redox chemistry to form size tunable, high index silicon particles

In the present work, the effect of changing the precursor ratio of silicon between sodium silicde and a hexacoordinated silicon complex to form various sizes of particles is studied. TEM images show the size difference between particles produced with different ratios. Particles produced with a 1:1 ratio are 45 nm in diameter and up to a 1:4 precursor ratio is used to make 230 nm particles. X-ray diffraction patterns confirm the presence of crystalline silicon for all sizes, while Raman spectroscopy shows how different degree of oxidation occurs thanks to different particle sizes, shifting the Raman peak. The surface chemistry is also studied to evidence the growth mechanism.

opencc-by-4.0Aug 2021View details →

Technical potential of ground-source heat pumps for Western Switzerland

This dataset contains an estimation of the technical potential of shallow ground-source heat pumps (GSHPs) for Western Switzerland, at a spatial resolution of 200 x 200 m2. The technical potential is hereby defined as the maximum energy that could be extracted from GSHP systems in case of their dense deployment, such as to avoid the over-exploitation of the heat capacity of the ground. We consider GSHPs with vertical closed-loop borehole heat exchangers (BHE) installed at depths of 50 - 200 m. The dataset covers around 80,000 property units (parcels) in the Swiss Cantons of Vaud and Geneva, excluding only the areas of the Alps and the Jura mountains. The estimated potential accounts for: <ul> <li>Norms for geothermal installations set by the Swiss Society of Engineers and Architects (SIA 384/6)</li> <li>Thermal interferences between neighbouring boreholes and their impact on the temperature change in the ground</li> <li>Topographic Landscape data to assess the available area for BHE installation</li> </ul> The methodology used to generate the data is described in: Walch, Alina, Nahid Mohajeri, Agust Gudmundsson, and Jean-Louis Scartezzini. ‘Quantifying the Technical Geothermal Potential from Shallow Borehole Heat Exchangers at Regional Scale’. Renewable Energy 165 (2021): 369–80. <a href="https://doi.org/10.1016/j.renene.2020.11.019">https://doi.org/10.1016/j.renene.2020.11.019</a>. Dataset description As the data is targeted to large-scale applications and potential studies, it is shared in the format of pixels of 200 x 200 m2. Upon request it can be provided at different aggregation levels, as it is generated at the resolution of individual building units (parcels). The potential is provided as annual values, and it can be converted to monthly values using the provided heating degree weights. For each pixel of 200 x 200 m2, we provide the following variables: <ul> <li>Annual  total technical heat extraction potential (in MWh)</li> <li>Potential heat delivered to buildings (heat pump output), assuming a heat pump performance (COP) of 4.5 (in MWh)</li> <li>Available area for GSHP installation (in m2)</li> <li>Number of installed boreholes </li> <li>Average heat extraction rate (in W/m)</li> <li>Average borehole depth (in m)</li> <li>Average borehole spacing within the parcels located in the pixel (in m)</li> <li>Heating degree weights (i.e. heat demand variation) for each month</li> </ul> A description of the metadata is provided in the document gshp_VD_GE_metadata_V1.pdf. This work is part of the PhD Thesis of Alina Walch.

opencc-by-4.0Jul 2022View details →

Alpha-2 Adrenoreceptor Antagonist Yohimbine Potentiates Consolidation of Conditioned Fear (Open Data and Open Materials)

Open Data and Open Materials of: Sperl, M. F. J., Panitz, C., Skoluda, N., Nater, U. M., Pizzagalli, D. A., Hermann, C., & Mueller, E. M. (2022). Alpha-2 adrenoreceptor antagonist yohimbine potentiates consolidation of conditioned fear. International Journal of Neuropsychopharmacology, 25(9), 759–773. Background: Hyperconsolidation of aversive associations and poor extinction learning have been hypothesized to be crucial in the acquisition of pathological fear. Previous animal and human research points to the potential role of the catecholaminergic system, particularly noradrenaline and dopamine, in acquiring emotional memories. Here, we investigated in a between-participants design with 3 groups whether the noradrenergic alpha-2 adrenoreceptor antagonist yohimbine and the dopaminergic D2-receptor antagonist sulpiride modulate long-term fear conditioning and extinction in humans. Methods: Fifty-five healthy male students were recruited. The final sample consisted of n = 51 participants who were explicitly aware of the contingencies between conditioned stimuli (CS) and unconditioned stimuli after fear acquisition. The participants were then randomly assigned to 1 of the 3 groups and received either yohimbine (10 mg, n = 17), sulpiride (200 mg, n = 16), or placebo (n = 18) between fear acquisition and extinction. Recall of conditioned (non-extinguished CS+ vs CS−) and extinguished fear (extinguished CS+ vs CS−) was assessed 1 day later, and a 64-channel electroencephalogram was recorded. Results: The yohimbine group showed increased salivary alpha-amylase activity, confirming a successful manipulation of central noradrenergic release. Elevated fear-conditioned bradycardia and larger differential amplitudes of the N170 and late positive potential components in the event-related brain potential indicated that yohimbine treatment (compared with a placebo and sulpiride) enhanced fear recall during day 2. Conclusions: These results suggest that yohimbine potentiates cardiac and central electrophysiological signatures of fear memory consolidation. They thereby elucidate the key role of noradrenaline in strengthening the consolidation of conditioned fear associations, which may be a key mechanism in the etiology of fear-related disorders.

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SALTEx soil oxidation-reduction (redox) potential measurements from the GCE LTER Seawater Addition Long-Term Experiment (SALTEx) between July 2016 and March 2017

SALTEx (Seawater Addition Long-Term Experiment) is a field experiment designed to simulate saltwater intrusion in a tidal freshwater wetland to predict how chronic (Press) and acute (Pulse) salinization will affect this and other tidal freshwater ecosystems. The SALTEx experiment was initiated in 2012 and consists of 31 field plots , each 2.5 m on a side. There are three treatments (Press, Pulse, and Fresh) and two types of controls (with and without sides), each consisting of six replicates. The Press treatment plots receive regular (4 times each week) additions of a mixture of seawater and fresh river water. Pulse plots receive the same mixture of seawater and river water during September and October, which is historically a time of low flow in the river when natural saltwater intrusion occurs. The Fresh treatment plots receive regular additions of fresh river water. Treatment water is added during low tide to facilitate its infiltration into the soil, and all plots are inundated by astronomical tides at high tide. Response measurements include porewater chemistry, specifically concentrations of chloride, sulfate, sulfide, dissolved organic carbon (DOC), ammonium-N, nitrate/nitrite-N, dissolved reactive phosphorus, total phosphorus, total nitrogen, organic nitrogen, carbon:nitrogen ratio, organic-carbon:organic-nitrogen ratio, and pH.

openCC (other)Jan 2020View details →

opencc-by-4.0Apr 2017View details →

Derived Data supporting "On the Seasonal Cycles of Tropical Cyclone Potential Intensity" (Gilford et al. 2017, JoC)

Derived monthly mean tropical cyclone potential intensities (and associated variables) using the Bister and Emanuel 2002 PI algorithm, ftp://texmex.mit.edu/pub/emanuel/TCMAX; from MERRA2 (averaged over 1980-2016) and ERA-I data (averaged over 1980-2013), on 2.5x2.5 degree grids and with the ERA-I land-sea mask already applied. This data supported the publication of Gilford et al. (2017, JoC). When using this data, please include the citation: Daniel M. Gilford, Susan Solomon, and Kerry Emanuel, 2017: On the Seasonal Cycles of Tropical Cyclone Potential Intensity. J. Climate, 30, 6085–6096. doi: <a href="http://journals.ametsoc.org/doi/10.1175/JCLI-D-16-0827.1">10.1175/JCLI-D-16-0827.1</a>.

opencc-by-4.0Jun 2020View details →

Historical Tropical Cyclone Along-track Potential Intensity (and Derived Quantities) for Six Ocean Basins from Reanalyses

Supporting derived data for Shields et al. (2020, GRL). Derived tropical cyclone potential intensities and associated variables across the North Atlantic (NA), Eastern North Pacific (EP), North Indian (NI), South Indian (SI), South Pacific (SP), and Western North Pacific (WP) ocean basins, from MERRA2, ERA-I, and MERRA2 with SSTs replaced by HadISSTs. NA/WP basins also have potential and observed intensities calculated with NCEP/NCAR and ERA-20C reanalyses over 1950-2016 and 1950-2010, respectively. All files are netcdf format, organized by basin, with suffixes on data variables to indicate reanalysis: <ul> <li>"_m": MERRA2 (Gelaro et al. 2017)</li> <li>"_h": MERRA2-HadISSTs (Rayner et al. 2003)</li> <li>"_e": ERA-I (Dee et al. 2011)</li> <li>"_n": NCEP/NCAR (Kalnay et al. 2016)</li> <li>"_c": ERA-20C (Stickler et al. 2014)</li> </ul> When using this data, please include the citation: <blockquote> Shannon Shields, Allison Wing, and Daniel M. Gilford, 2020: A Global Analysis of Interannual Variability of Potential and Actual Tropical Cyclone Intensities. Geophys. Res. Lett. </blockquote> Potential intensities calculated with the Bister and Emanuel (2002) algorithm (pcmin.m) by Kerry Emanuel (revised by Daniel Gilford, Gilford et al. 2019), available freely at: ftp://texmex.mit.edu/pub/emanuel/TCMAX MERRA2, ERA-I, and MERRA2 with SSTs replaced by HadISSTs calculations were performed by Daniel Gilford; NCEP/NCAR and ERA-20C calculations were performed by Dr. Suzana Camargo (many thanks!). Please direct any questions or comments to daniel[dot]gilford[at]rutgers[dot]edu.

opencc-by-4.0Jan 2021View details →

Extreme Precipitation Potential and Slow-moving Extreme Precipitation Potential

Extreme Precipitation Potential (EPP) and Slow-moving Extreme Precipitation Potential (SEPP) are described in Kahraman et al. paper "Quasi-stationary intense rainstorms spread across Europe under climate change".   File names as "identifier+YYYY+MM+.nc".   EPP count per month for current (identifier=hvpraj) and future (identifier=hvprak) climate. SEPP count per month for current (identifier=hvprslowaj) and future (identifier=hvprslowak) climate. YYYY=simulation year MM=simulation month ".nc"=netcdf extension

opencc-by-4.0Jan 2021View details →

Data for: The circular economy potential of urban organic waste streams in low- and middle-income countries

This dataset includes the research data and supporting information for the publication "The circular economy potential of urban organic waste streams in low- and middle-income countries" which was published in the Journal of Environment, Development and Sustainability (DOI: 10.1007/s10668-021-01487-w). This dataset and the associated publication are the basis upon which the REVAMP (Resource Value Mapping) tool has been developed. See more info about the REVAMP tool here: https://www.sei.org/revamp