Find research datasets worth reusing

openOpenNov 2023View details →

Caribou-Poker Creeks Research Watershed: Daily Flow Rates for C2, C3, C4

Stream discharge during the ice-free season was measured from ca. 1997 to present in three streams draining sub-catchments of the Caribou-Poker Creeks Research Watershed, which drain catchments with underlying permafrost extents ranging from 3 to 53%.

Periphyton Net Primary Productivity and Respiration Rates from the Taylor Slough, just outside Everglades National Park (FCE), South Florida from December 1998 to December 2004

Periphyton metabolism is being measured at TS/Ph1b, TS/Ph2, TS/Ph3, TS/Ph4, and TS/Ph5 every 6-8 weeks during the wet season. We quantify metabolic rates of periphyton assemblages using standard oxygen change techniques in 300mL light and dark BOD bottles in triplicate. Using YSI dissolved oxygen probes and meters, we measure dissolved oxygen change as the difference of initial and final oxygen concentrations during a two hour incubation period. We calculate net primary productivity and respiration rates in units of oxygen and carbon, then normalize to the organic content (ash free dry weight) of the incubated periphyton.

Rubisco limited photosynthesis rates of Red mangrove leaves at Key Largo, Watson River Chickee, Taylor Slough, and Little Rabbit Key, South Florida (FCE) from July 2001 to August 2001

Determine the Rubisco limited carboxylation rates of red mangrove ( species Rhizophora mangle) leaves. This information will be used to model carbon sequestration by Red mangroves.

Light limited carboxylation rates of Red mangrove leaves at Key Largo, Watson River Chickee, Taylor Slough, and Little Rabbit Key, South Florida (FCE) from July 2001 to August 2001

Our goal is to determine light limited carboxylation rates of red mangrove (specie sRhizophora mangle) leaves. This information will be used to model carbon sequestration by Red mangroves.

Periphyton Net Primary Productivity and Respiration Rates from the Taylor Slough, just outside Everglades National Park, South Florida (FCE) from December 1998 to August 2002

Once per year, at TS/Ph-4 and TS/Ph-5 we incubate periphyton from each site in water from each site in a complete factorial design. We quantify metabolic rates of periphyton assemblages using standard oxygen change techniques in 300mL light and dark BOD bottles in triplicate. Using YSI dissolved oxygen probes and meters, we measure dissolved oxygen change as the difference of initial and final oxygen concentrations over a two hour incubation. We calculate net primary productivity and respiration rates in units of oxygen and carbon, then normalize to the organic content (ash free dry weight) of the incubated periphyton.

Greenhouse mixed culture experiment from August 2002 to April 2003 (FCE): Evaluate the effect of salinity and hydroperiod on interspecific mangrove seedlings growth rate (mixed culture) / Morphometric variables

A greenhouse experiment (mixed culture experiment) was performed for 8 months to evaluate the effect of salinity and hydroperiod on seedling growth rates of 2 mangrove species( Laguncularia racemosa and Rizhophora mangle). Data analyses are currently being performed.

openCustomJan 2020View details →

Concentrations, turnover rates and fluxes of polyamines in coastal waters of the South Atlantic Bight during April and October 2011

Polyamines are short-chain aliphatic compounds containing multiple amine groups. They are important components of the cytosol of eukaryotes and are present at mmol/L concentrations inside phytoplankton cells, while complex polyamines play a role in biosilica deposition. Concentrations of polyamines measured in seawater are typically in the sub nmol/L range, implying rapid and efficient uptake by osmotrophs, likely bacterioplankton. We measured turnover rates of three polyamines (putrescine, spermidine and spermine) using 3H-labeled compounds and determined their concentrations by HPLC to estimate polyamine contributions to dissolved organic matter and bacterioplankton carbon and nitrogen demand. These measurements were made on transects from the inner shelf to the Gulf Stream across the South Atlantic Bight (SAB) during April and October of 2011 and in salt marsh estuaries on the Georgia coast during August of 2011 and April of 2012. This data set includes measurements of water column variables (temperature, salinity, biogenic Silica), nutrients (nitrite, nitrate+nitrite, ammonium and dissolved inorganic nitrogen), and concentrations and turnover rates of polyamine compounds.

openCC (other)Sep 2024View details →

MCR LTER: Coral Reef: Rates of benthic coral reef community metabolism from 2007 ongoing

Rates of community primary production and respiration can be calculated from the data presented here. For two sites on the north shore of Moorea, these measurements are sampled upstream and downstream: dissolved oxygen, water column velocity (speed and direction), in situ light (PAR) levels, incident solar radiation, temperature, wind speed, and transect length. Estimates are made yearly across approximately 160 m of the backreef community. This material uses data collected by the U.S. National Science Foundation's (NSF) Moorea Coral Reef Long Term Ecological Research (MCR LTER) site under Grant No. OCE 2224354 (and earlier awards). Additional financial support to the MCR LTER site was provided through a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2024).

openCC (other)Aug 2023View details →

Phytoplankton growth and microzooplankton grazing rates from NES-LTER transect cruises, ongoing since 2018.

Phytoplankton growth and microzooplankton grazing rates were measured from incubation experiments using the dilution method in the framework of the Northeast U.S. Shelf Long-Term Ecological Research project. The data set includes plankton population dynamics rates obtained during 12 cruises from winter 2018 (EN608) to summer 2022 (EN687) along a north/south transect from Martha’s Vineyard to the shelf-break. Phytoplankton growth and microzooplankton grazing rates were measured for the total phytoplankton community (chl-a concentrations) and for size fractions (chl-a size fractionation) less than and greater than 10 µm. Phytoplankton growth and microzooplankton grazing rates, the first trophic interaction between primary producers and higher trophic levels, are essential parameters to assess the cycling and export of carbon in the ocean and to better understand marine food webs.

openCC (other)Jan 2025View details →

Estimates of nitrogen and phosphorus excretion rates in individual marine and estuarine animals

This dataset contains nitrogen and phosphorus excretion rate, as well as dry biomass, estimates for individual vertebrate and invertebrate animals in marine and estuarine environments. This dataset is a product of an LTER Synthesis Working Group aimed at evaluating the spatiotemporal variability in consumer nutrient dynamics in the wake of global change across eight long-term ecological research projects. These projects include seven long-term ecological research programs (LTER) funded by the National Science Foundation: (1) California Current Ecosystem, (2) Florida Coastal Everglades, (3) Moorea Coral Reef, (4) Northern Gulf of Alaska, (5) Plum Island Ecosystems, (6) Santa Barbara Coastal, and (7) Virginia Coast Reserve LTER projects. Additionally, the dataset includes data from (8) The Partnership for Interdisciplinary Science of Coastal Oceans (PISCO) research program. The temporal coverage of each time series data varies among projects, with the earliest record in 1997 and the most recent in 2023. This data package also includes two folders of R scripts used for data harmonization, identical to those in the LTER Synthesis Working Group: Consumer-Mediated Nutrient Dynamics Project, v2.0.0. You can find the release in GitHub here: https://github.com/lter/lterwg-marine-cnd/releases/tag/v2.0.0

openCustomOct 2023View details →

Erosion Rates, Soil Core Descriptions and Organic Matter on the Virginia Coast

These data include stratigraphic, organic matter, and organic carbon analyses of sediment cores, as well as values used to calculate the time-averaged carbon erosion rate for the central 10 islands of the Virginia Barrier Island chain.

openCustomNov 2023View details →

Lateral and vertical forest retreat rate in the mid-Atlantic sea-level rise hotspot

Ghost forests consisting of dead trees adjacent to marshes are striking indicators of climate change. Here we quantify both the lateral and vertical rate of coastal forest retreat between 1984 and 2020 along the US mid-Atlantic coast. The study region includes areas between 0-5 m above sea level across the Chesapeake Bay and the adjacent Delaware Bay. Specifically, the data package includes 2 shapefile datasets derived from four decades of Landsat satellite observations of coastal treeline dynamics. The two datasets are generated on the same spatial-scale and have the same spatial resolution (0.075 km2), both stored as hexagon grids with a side length 170 m. Here we define "forest retreat" (as shown in the datasets as positive values) as the migration of coastal treeline landwards (lateral retreat) or upslope (vertical retreat), whereas "forest advance" (negative values) refers to treeline migration seawards (lateral advance) or downslope (vertical advance). The number '999999' in both datasets indicates areas of stable coastal treelines (i.e. no change) between 1984 and 2020.

opencc-by-4.0Dec 2020View details →

Data and code release for Carleton, Cornetet, Huybers, Meng & Proctor (PNAS, 2020), "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates"

This upload contains all replication material for "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates" (PNAS, 2020). Please note that previous versions of this upload provided data and code for the pre-print version of the article, which changed somewhat through the peer review process.  Authors: Tamma Carleton, Jules Cornetet, Peter Huybers, Kyle C. Meng, Jonathan Proctor. Code is located within CCHMP_covid_climate_code_release.zip, and is written in R, Stata, and Matlab. The working directory should be set to the repository folder at the top of each script (all other filepaths are relative). Please find the code needed to replicate the main findings of the paper described below: <ul> <li>Plots of data: R and Stata scripts to make figures 1B, 2A/B/C, S1, S2, and S3, can be found within “code/analysis/data_plots/”.</li> <li>Regression analysis: Stata scripts to run the distributed lag regressions and plot the results in figures 2, 3C, S5, S6, S7, S8, S10, and S14, as well as Table S1, can be found within “code/analysis/regressions/”. R scripts for data analysis and plotting for figures 3A/B and S9 are also within "code/analysis/regressions/".</li> <li>Seasonal simulations: R and Stata scripts to replicate the seasonal simulation shown in figures 4, S4 and S11 can be found within “code/analysis/seasonal_sim/”.</li> <li>SEIR simulations: Matlab scripts to replicate the SEIR simulations shown in figures S12 and S13 can be found within “code/analysis/SEIR/”.</li> </ul> Data are located within CCHMP_covid_climate_data_release.zip.

opencc-by-4.0Mar 2024View details →

Rating curves based on satellite altimetry and in-situ discharge data

<h1>Context: </h1> The ESA river discharge Climate Change Initiative (CCI) project is a precursor study. It aims to derive long term climate data records (at least over 20-years) of river discharge for some selected river basins (and some locations in the river network) using satellite remote sensing observations (altimetry and multispectral images) and ancillary data. It aims to provide a proof-of-concept for the feasibility for a potential River Discharge ECV product to meet the requirements for the <a href="https://gcos.wmo.int/en/essential-climate-variables/rivers/" target="_blank" rel="noopener">Global Climate Observing System</a>. This project covers precursor activities towards the production of data products that address the GCOS-defined requirements for the River Discharge ECV. <h1>Data description :</h1> Just as in-situ stage measurements can be used to gauge river discharge, altimetry-derived water surface elevation (WSE) can serve as an alternative means of estimating river discharge when discharge time series data is available. Several methodologies have been documented for deriving discharge time series from multimission altimetry observations and supplementary data (Biancamaria et al., 2024). At least two approaches will be used, depending on the available in situ discharge and altimetry water surface elevation (WSE) time series: ⋅ Method 1: The preferred approach relies on the altimetry water surface elevation time series and in situ discharge time series to create a rating curve (RC) characterized by a power relationship between these two variables following a Bayesian approach (Rantz et al., 1982). However, this method necessitates a significant overlap period between discharge data and radar altimetry measurements (e.g., Biancamaria et al., 2011; Papa et al., 2012), or it requires the assumption that the rating curve remains valid and consistent when discharge data is only available prior to the altimetry observation period. ⋅ Method 2: The final option, in cases where there is no temporal overlap between in-situ or simulated discharge and water surface elevation data, assumes that the validity and stability of the rating curve persist across the various time periods covered by the two datasets. Both of these time periods should be sufficiently long to encompass a wide range of events. With this assumption, Tourian et al. (2013, 2017) introduced a method for calculating the rating curve, not based on the time series of discharge and water surface elevation, but on the distribution of their quantiles. This method has been adopted by a limited number of recent studies (e.g., Belloni et al., 2021). However, it’s important to note that this methodology naturally introduces higher errors when compared to the preferred approach. For this reason, this methodology will be validated over some stations with various hydrological dynamics and satisfying previous methods (overlap period exists between WSE and Q). <h1>Approaches to derive Rating Curve (RC) :</h1> <h2>Bayesian Approach :</h2> The Bayesian method is a robust statistical approach used for constructing a rating curve, frequently applied in the field of hydrology when the goal is to estimate unknown parameters from observed data, while taking into consideration the associated uncertainty in these estimates.  According to this, the estimation of the rating curve using the Bayesian method involves several steps: <ul> <li>The initial step entails defining a probabilistic model that describes the relationship between observed data and the parameters we aim to estimate. In many hydrological applications, the relationship between discharge data (Q) and water surface elevation data (WSE) is often expressed as a power function:</li> </ul>                                                                                       Q = a⋅(WSE-z0)b Here, a, z0 and b are the parameters of the rating curve. a, is a scaling coefficient governing the magnitude of the Q-WSE relationship, b, characterizes the nature of this relationship, and z0, represents the height of the free surface above the reference point, corresponding to the river bottom's altitude. The power relationship is especially pertinent due to its consistency with numerous hydrodynamic phenomena. The exponent b within the equation allows for the representation of distinctive flow characteristics, including factors like roughness and channel geometry. Moreover, it offers adaptability in modelling to accommodate variations in flow characteristics, whether they are turbulent or laminar. This relationship, despite its mathematical simplicity, facilitates the fine-tuning of model adjustments in accordance with observed data (Chow, 1959). <ul> <li>The second step involves the use of prior normal distributions, reflecting our prior knowledge about these parameters. These distributions can either be informative or uninformative, depending on our level of knowledge. The limits and ranges for a, z0 and b can vary depending on the specific context of the study, the dataset used, and the characteristics of the river or channel being analysed.</li> </ul> - Coefficient “a”:  adjustment parameter for the rating curve representing the scaling factor for discharge. Its value can significantly fluctuate based on various factors such as the characteristics of the river or channel, hydraulic conditions, and other influencing factors. Consequently, "a" must be non-negative and constrained within a sensible range specific to the system under study. Following the Manning equation, “a” must be equal to W/n*S1/2 (Chow et al., 1988) where W is the river’s width (m), n the Manning’s roughness coefficient and S the slope (m/m). Given the considerable variability in river width and slope across different stations, a feasible range for this coefficient can be considered as:                                                                                       a ∈ [0; 3000] - Coefficient “b”: adjustment parameter representing the exponent of the rating curve and indicating the hydraulic condition of the study site. Like "a," this value must comply with physical constraints and cannot be negative. Following the Manning equation, “b” must be equal to 5/3 for reference hydraulic condition (Rantz et al., 1982). To accommodate the variability in system characteristics across sites, the following range values can be considered for this coefficient:                                                                                       b ∈ [0; 5] - Coefficient “z0”: offset or the elevation at which discharge begins. It should be within the range of elevations relevant to your study. For this reason, the value cannot exceed the minimum value of water surface elevation (WSE) and the range value need to consider of the variability in term of water depth over the sites. A feasible range for this coefficient can be considered as:                                                                                       z0 ∈ [min(WSE)-30; min(WSE)] <ul> <li>The final step involves parameter estimation. The posterior distribution of the parameters yields probabilistic estimates of the rating curve parameters in the form of mean values (optimal values) and credibility intervals (95th percentiles). This accounts for the uncertainty associated with these parameters and is achieved through Markov Chain Monte Carlo (MCMC) sampling from the posterior distribution. Two commonly employed MCMC algorithms are "NUTS" (No-U-Turn Sampler) and "Metropolis-Hastings." The Metropolis-Hasting sampler "MH" algorithm, which is relatively simple and efficient where a balance between exploration and exploitation is desired. This algorithm can be adapted to sample from discrete state spaces.</li> </ul> <h2>Quantile approach : </h2> The Quantile approach employs statistical modelling using quantile functions to create a rating curve, eliminating the necessity for overlapping measurements. This algorithmic method enables the estimation of river discharge using satellite altimetry, even in instances where there are no in situ measurements within the altimeter's timeframe. This approach has undergone application and validation in diverse river basins spanning different climatic zones, such as the Amazon, Brahmaputra, Danube, Niger, and Ob (Tourian et al., 2013). Assuming a stationary flow behaviour and no modification in the river bathymetry both at the altimetry virtual station and at the in-situ gage, this approach ensures the utilization of historical in situ data in current applications. This method computes the quantile functions of the altimetry water surface elevation on one hand and of the discharge time series on the other hand. Then a scatter plot of these in-situ discharge quantiles versus altimetry water surface elevation quantiles is computed to establish the rating curve using the bayesian approach described previously. <h1>File description :</h1> <table> <tbody> <tr> <td>Column name</td> <td>Description</td> </tr> <tr> <td>basin-station</td> <td>Basin name in capital letters and Station name in capital letters separated by "_" and where spaces have been replaced by "-".</td> </tr> <tr> <td>lon</td> <td>Longitude in decimal degrees [-180,180] with 4 decimals - corresponding to the insitu discharge station.</td> </tr> <tr> <td>lat</td> <td>Latitude in decimal degrees [-90,90] with 4 decimals – corresponding to the insitu discharge station.</td> </tr> <tr> <td>a</td> <td>Adjustment parameter for the rating curve representing the scaling factor for discharge. Number with 3 decimals.</td> </tr> <tr> <td>b</td> <td>Adjustment parameter representing the exponent of the RC and indicating the hydraulic condition of the study site. Number with 3 decimals.</td> </tr> <tr> <td>z0</td> <td>Offset of the elevation at which discharge begins. Number with 3 decimals.</td> </tr> <tr> <td>a_sd</td> <td>Standard deviation of the coefficient "a". Number with 3 decimals.</td> </tr> <tr> <td>b_sd</td> <td>Standard deviation of the coefficient "b". Number with 3 decimals.</td> </tr> <tr> <td>z0_sd</td> <td>Standard deviation of the coefficient "z0". Number with 3 decimals.</td> </tr> <tr> <td>period</td> <td>Period used to compute the rating curve under the format %Y-%m-%d where the start and the end dates are separated by ":"</td> </tr> <tr> <td>nb</td> <td>Number of overlap dates to compute the rating curve.</td> </tr> <tr> <td>Methodology</td> <td>Methodology used to compute the rating curve. The first part describes the approach used to compute the RC and the second part, separated by “_”, describes the algorithm used. To avoid any issue for the reader the spaces have been replaced by “-”. At the end 2 approaches has been used: “Overlap-approach” or “Quantile-approach” and 2 algorithms: “Bayesian-algorithm” or “Multiple-algorithms” designed for Arctic rivers experiencing frozen periods. </td> </tr> <tr> <td>Source</td> <td>In-situ data sources to compute the rating curve. If multiple sources has been used, the sources are separate by "/"</td> </tr> </tbody> </table> --------- THE DATASET IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET.

opencc-by-nc-4.0Aug 2024View details →

Data Files for Climate-based Maize Loss Rate Simulations

This archive contains data files from an <a href="../records/13356711">open source pipeline</a> looking at how crop insurance rates may change in the future within the US Corn Belt using <a href="https://www.sciencedirect.com/science/article/pii/S0034425715001637">SCYM</a> and <a href="https://www.chc.ucsb.edu/data/chc-cmip6">CHC-CMIP6</a>. These are available under a Creative Commons license. Unless otherwise specified, these report on SSP245. See README for more details including column-level description of each resource. Funded by the <a href="https://dse.berkeley.edu/">Eric and Wendy Schmidt Center for Data Science and Environment</a> at the University of California, Berkeley.

opencc-by-4.0Mar 2022View details →

Sub-10 nm size-distribution data for "What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?"

<pre>Size-Distribution data from the CERN CLOUD experiment (Kirkby et al., 2011) measured with a DMA-train (Stolzenburg et al., 2017) Data acquired during the CLOUD10 (Fall 2015) and CLOUD12 (Fall 2017) campaigns. Data associated with the publication Kontkane et al. (2022). File name indicates the Experiment number as specified in Table 3, Kontkanen et al. (2022) and the internal CLOUD run numbers as given in Table S1, Kontaknen et al. (2022). Concentration of precursor gases are also given in these two Tables. Exp. 8 only used data from NAIS and is not included in this repository. Header indicates the diameter at which the size-distribution is measured. First column is time column with areadable timestamp in the format %Y-%m-%d %H:%M:%S. Data is dN/dlog_10 dp in unit cm^(-3). Full size-distribution (up to 400 nm) can be obtained from the author upon request. References: Kontkanen et al. (2022), What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?, Environ. Sci.: Atmos., accepted. Kirkby et al. (2011), Role of sulphuric acid, ammonia and galactic cosmic rays in atmospheric aerosol nucleation, Nature, 476, 429-433, http://dx.doi.org/10.1038/nature10343 Stolzenburg et al. (2017), A DMA-train for precision measurement of sub-10nm aerosol dynamics, Atmos. Meas. Tech., 10, 1639-1651, http://www.atmos-meas-tech.net/10/1639/2017/ </pre>

opencc-by-4.0Sep 2021View details →

Data for "Modelling soil carbon stocks following reduced tillage intensity: a framework to estimate decomposition rate constant modifiers for RothC-26.3, demonstrated in north-west Europe"

Dataset of paired observations of conventional tillage (CT) with no tillage (NT) and reduced tillage (RT) from studies in temperate oceanic regions of Western Europe, extracted from a recent systematic review (Jordon et al. preprint, see DOI below). R code of modelling framework to estimate tillage rate modifiers (TRM) for simulating adoption of RT and NT using RothC-26.3, and meta-estimates of TRM across studies.

opencc-by-4.0Oct 2022View details →

Ensemble of NEMO present-day (1989-2009) and future (2080-2100 under RCP8.5) ocean properties and ice shelf melt rates in the Amundsen Sea

Model outputs used in <a href="https://www.essoar.org/doi/10.1002/essoar.10511482.3">Jourdain et al. (GRL, 2022)</a> The output files consist of monthly climatologies over either 1989-2009 or 2080-2100. The file names have the form: climato_monthly_AMUXL12-GNJ002_<simu>_<group>_1989_2009.nc, where : <ul> <li><simu> is either : <ul> <li>"BM02MAR" (ensemble member A, present-day),</li> <li>"BM03MAR" (ensemble member B, present-day),</li> <li>"BM04MAR" (ensemble member C, present-day),</li> <li>"BM02MARrcp85" (ensemble member A, future for both surface and lateral boundaries),</li> <li>"BM03MARrcp85" (ensemble member B, future for surface BUT NOT for lateral boundaries),</li> <li>"BM03MARrcBDY" (ensemble member B, future for both surface and lateral boundaries),</li> <li>"BM04MARrcp85" (ensemble member C, future for both surface and lateral boundaries),</li> </ul> </li> <li><group> is either : <ul> <li>"SBC" (surface boundary conditions),</li> <li>"icemod" (sea ice variables),</li> <li>"gridT" (temperature, salinity),</li> <li>"gridU" (zonal velocities),</li> <li>"gridV" (meridional velocities).</li> </ul> </li> </ul> Grid information in: <ul> <li>mesh_mask_AMUXL12_BedMachineAntarctica-2019-05-24.nc (ensemble member A),</li> <li>mesh_mask_AMUXL12_BedMachineAntarctica-2020-07-15_v02_ICB380.nc (ensemble members B & C).</li> </ul> where: <ul> <li>glamt : longitude</li> <li>gphit: latitude</li> <li>e1t, e2t, e3t_0 : mesh size (in meters) along x, y, z</li> <li>tmask = 1 for ocean mesh, = 0 otherwise (land, continental ice).</li> </ul>   Acknowledgments: This work was granted access to the HPC resources of CINES (occigen) under the allocation A0100106035 attributed by GENCI.

opencc-by-sa-4.0Oct 2017View details →

X-rays across the galaxy population: The distribution of AGN accretion rates as a function of stellar mass and redshift

We provide measurements of the probability distribution function of specific black hole accretion rates within a sample of galaxies of a given stellar mass and redshift, \(p(\log \lambda_{sBHAR} | M_*,z)\). Measurements are provided for all galaxies, star-forming galaxies and quiescent galaxies. We also provide estimates of the AGN duty cycle, \(f(\lambda_{sBHAR} >0.01)\) i.e. the fraction of galaxies with an AGN above a given limit in specific accretion rate, based on the probability distribution functions. Full details are provided in Aird et al. (2018, MNRAS, 474, 1225); please cite this publication if you use these measurements.