Find research datasets worth reusing

opencc-by-4.0Sep 2024View details →

Experimentally constrained Reaction Rate of the 59Fe(n,g)60Fe reaction

Reaction rate for the reaction 59Fe(n,g)60Fe. The upper and lower limits were extracted from the experimental investigation of this reaction using the beta-Oslo method (publication in progress A. Spyrou et al, Nature Com. 2024). In addition, the average of the upper and lower limits is given as a recommended rate.

opencc-by-4.0Oct 2024View details →

Artifact of the paper: Scheduling with lightweight predictions in power-constrained HPC platforms

Please refer to the <a href="https://zenodo.org/records/13961003/files/artifact-overview.pdf?download=1&preview=1">artifact-overview.pdf</a> file in this dataset for instructions to reproduce the experiments we have conducted for this article, or for more context about the article.

opencc-zeroDec 2016View details →

Data from: Natural history constrains the macroevolution of foot morphology in European plethodontid salamanders

The natural history of organisms can have major effects on the tempo and mode of evolution, but few examples show how unique natural histories affect rates of evolution at macroevolutionary scales. European plethodontid salamanders (Plethodontidae: Hydromantes) display a particular natural history relative to other members of the family. Hydromantes commonly occupy caves and small crevices, where they cling to the walls and ceilings. On the basis of this unique and strongly selected behavior, we test the prediction that rates of phenotypic evolution will be lower in traits associated with climbing. We find that, within Hydromantes, foot morphological traits evolve at significantly lower rates than do other phenotypic traits. Additionally, Hydromantes displays a lower rate of foot morphology evolution than does a nonclimbing genus, Plethodon. Our findings suggest that macroevolutionary trends of phenotypic diversification can be mediated by the unique behavioral responses in taxa related to particular attributes of their natural history.

opencc-zeroDec 2017View details →

Data from: Spatial heterogeneity in species composition constrains plant community responses to herbivory and fertilization

Environmental change can result in substantial shifts in community composition. The associated immigration and extinction events are likely constrained by the spatial distribution of species. Still, studies on environmental change typically quantify biotic responses at single spatial (time series within a single plot) or temporal (spatial beta-diversity at single time points) scales, ignoring their potential interdependence. Here, we use data from a global network of grassland experiments to determine how turnover responses to two major forms of environmental change – fertilization and herbivore loss – are affected by species pool size and spatial compositional heterogeneity. Fertilization led to higher rates of local extinction whereas turnover in herbivore exclusion plots was driven by species replacement. Overall, sites with more spatially heterogeneous composition showed significantly higher rates of annual turnover, independent of species pool size and treatment. Taking into account spatial biodiversity aspects will therefore improve our understanding of consequences of global and anthropogenic change on community dynamics.

opencc-zeroJun 2021View details →

A common measure of prey immune function is not constrained by the cascading effects of predators

Simultaneously defending against predators, stymieing competitors, and generating immune responses can impose conflicting demands for host species caught in the entanglement of a food web. Host immunity is not only shaped by direct interactions among species, but also many indirect cascading effects. By reducing competition, predators in particular can affect resource acquisition necessary for hosts to mount energetically costly immune responses. However, identifying the links between predators and host immune responses determined by resource acquisition is a complex affair, because predators can (i) reduce host density and thus competition among hosts, (ii) exert non-consumptive trait-mediated effects on host resource acquisition behavior, and (iii) generate natural selection on host resource acquisition behavior. To examine the relative contributions of these potential predator driven density- and trait-mediated effects on a key aspect of immune function (total phenoloxidase activity, total PO), we conducted mesocosm and field experiments with larval damselflies ( Enallagma signatum ) and their dominant fish predator ( Lepomis macrochirus ). Although we expected to observe declines in total PO activity with increases in damselfly density, we found no relationship between density and total PO activity. We also found no support for the prediction that total PO activity would vary as a result of either non-consumptive trait-mediated effects or selection on damselfly foraging activity underlying resource acquisition. Despite the lack of trait- or densitymediated effects, we did find that total PO activity increased with damselfly prey density among lakes, implying resource limitation for this aspect of immune function. These unexpected results point to the need to better understand the ecological conditions whereby predators and competitors constrain immune functions necessary for species to defend themselves in complex food webs.

openmit-licenseSep 2021View details →

16S processed data and shell processing scripts for "Epithelial-myeloid exchange of MHCII constrains immunity and microbiota composition"

Main repo for MHCII on IECs 16S processing code.

opencc-zeroSep 2021View details →

Social groups constrain the spatiotemporal dynamics of wild sifaka gut microbiomes

Primates acquire gut microbiota from conspecifics through direct social contact and shared environmental exposures. Host behavior is a prominent force in structuring gut microbial communities, yet the extent to which group or individual-level forces shape the long-term dynamics of gut microbiota is poorly understood. We investigated the effects of three aspects of host sociality (social groupings, dyadic interactions, and individual dispersal between groups) on gut microbiome composition and plasticity in 58 wild Verreaux's sifaka (Propithecus verreauxi) from six social groups. Over the course of three dry seasons in a five-year period, the six social groups maintained distinct gut microbial signatures, with the taxonomic composition of individual communities changing in tandem among co-residing group members. Samples collected from group members during each season were more similar than samples collected from single individuals across different years. In addition, new immigrants and individuals with less stable social ties exhibited elevated rates of microbiome turnover across seasons. Our results suggest that permanent social groupings shape the changing composition of commensal and mutualistic gut microbial communities and thus may be important drivers of health and resilience in wild primate populations.

opencc-by-4.0Sep 2021View details →

Constrained Fitness Landscape Analysis of Vehicle Routing Problems

The repository is a set of Jupyter notebooks and datasets used in the article titled: “Constrained Fitness Landscape Analysis for Capacitated Vehicle routing problems.” The main objective is to give the tools for reproducing the results shown in the paper.

opencc-by-4.0Sep 2021View details →

Dataset for "Constraining the response factors of an extractive electrospray ionization mass spectrometer for near-molecular aerosol speciation"

Online characterization of aerosol composition at the near-molecular level is key to understanding chemical reaction mechanisms, kinetics, and sources under various atmospheric conditions. The recently developed extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) is capable of detecting a wide range of organic oxidation products in the particle phase in real time with minimal fragmentation. Quantification can sometimes be hindered by a lack of available commercial standards for aerosol constituents, however. Good correlations between the EESI-TOF and other aerosol speciation techniques have been reported, though no attempts have yet been made to parameterize the EESI-TOF response factor for different chemical species. Here, we report the first parameterization of the EESI-TOF response factor for secondary organic aerosol (SOA) at the near-molecular level based on their elemental composition. SOA was formed by ozonolysis of monoterpenes or OH-oxidation of aromatics inside an oxidation flow reactor (OFR) using ammonium nitrate as seed particles. A Vocus proton-transfer reaction mass spectrometer (Vocus-PTR) and a high-resolution aerosol mass spectrometer (AMS) were used to determine the gas phase molecular composition and the particle phase bulk chemical composition, respectively. The EESI response factors towards bulk SOA coating and the inorganic seed particle core were constrained by intercomparison with the AMS. The highest bulk EESI response factor was observed for SOA produced from 1,3,5-trimethylbenzene, followed by those produced from d-limonene and o-cresol, consistent with previous findings. The near-molecular EESI response factors were derived from intercomparisons with Vocus-PTR measurements, and were found to vary from 103 to 106 ions s-1 ppb-1, mostly within ±1 order of magnitude of their geometric mean of 104.6 ions s-1 ppb-1. For aromatic SOA components, the EESI response factors correlated with molecular weight and oxygen content, and inversely correlated with volatility. The near-molecular response factors mostly agreed within a factor of 20 for isomers observed across the aromatics and biogenic systems. Parameterization of the near-molecular response factors based on the measured elemental formulae could reproduce the empirically determined response factor for a single VOC system to within a factor of 5 for the configuration of our mass spectrometers. The results demonstrate that standard-free quantification using EESI-TOF is possible.

opencc-by-4.0Oct 2021View details →

[Re] An anatomically constrained neural network model of fear conditioning

The results contained within this archive correspond to the Python re-implementation of the computation model and replication of the classical conditioning experiment described in Armony et al. (1995). The data was generated by running the program using the 14th frequency as the Conditioned Stimulus (CS_IDX = 13) and setting the random seed for the <a href="https://numpy.org/">Numpy</a> library to 3 (NUMPY_SEED = 3). During the pre- and post-conditioning testing phases, the activation values of all the neurons in the model have been recorded in different <a href="https://pandas.pydata.org/">pandas.DataFrames</a>. At the end of the experiment, those DataFrames have been written to disk using the <a href="https://hdfgroup.org/">HDF5</a> file format. It should be noted that although this might have been unnecessary given the size of the final dataset, the file has been further compressed to save on space. The HDF5 file format works similarly to dictionaries in Python, or Maps in other programming languages. That is, the data is organized into tables/arrays each associated with a unique key. In the case of the current dataset, the keys are the name of the different layers in lowercase (i.e.: mgm, mgv, cortex, and amygdala). Then, the array corresponding to each of those key includes the layer's neural activities for all frequencies, and for both the pre- and post-conditioning phases. The columns making up each table are: <ul> <li>The "Frequency" index with values in the range [1-15],</li> <li>One column for storing the activity of each unit ("Unit 1", ..., "Unit N", where N = 3 or N = 8 depending on the layer),</li> <li>The last column, entitled "Phase", contains string representations of the phase during which the activity was recorded (either "Pre-conditioning" or "Post-conditioning").</li> </ul> The data included in the archive can be retrieved and stored in a dictionary for further processing using the following Python script: <pre><code class="language-python">import pandas as pd # DATA_PATH is the absolute path to the file containing the hdf5 formated data data = {k: pd.read_hdf(DATA_PATH, key=k) for k in ['mgm', 'mgv', 'cortex', 'amygdala']}</code></pre>

opencc-zeroOct 2021View details →

Evolutionary divergence of potential drought adaptations between two subspecies of an annual plant: Are trait combinations facilitated, independent, or constrained?

Premise: Whether drought-adaptation mechanisms tend to evolve together, evolve independently, and/or evolve constrained by genetic architecture is incompletely resolved, particularly for water relations traits besides gas exchange. We addressed this issue in two subspecies of Clarkia xantiana (Onagraceae), California winter annuals that separated approximately 65,000 years ago and are adapted, partly by differences in flowering time, to native ranges differing in precipitation. Methods: In these subspecies and in recombinant inbred lines (RILs) from a cross between them we scored traits related to drought adaptation (timing of seed germination and of flowering; succulence; pressure-volume curve parameters) in common environments. Results: The subspecies native to more arid environments (parviflora) exhibited slower seed germination in saturated conditions, earlier flowering, and greater succulence, likely indicating superior drought avoidance, drought escape, and dehydration resistance via water storage, respectively. The other subspecies (xantiana) had lower osmotic potential at full turgor and lower water potential at turgor loss, implying superior dehydration tolerance. Genetic correlations among RILs suggest facilitated evolution of some trait combinations and independence of others. Where genetic correlations exist, subspecies differences fell along them, with the exception of differences in succulence and turgor loss point. In that case, subspecies difference overcame genetic correlations, possibly reflecting strong selection and/or antagonistic genetic correlations with other traits. Conclusions: Clarkia xantiana subspecies' differ in multiple mechanisms of drought adaptation. Genetic architecture generally does not seem to have constrained the evolution of these mechanisms, and it may have facilitated the evolution of some of trait combinations.

opencc-zeroNov 2022View details →

Efficient Real-Time Selective Genome Sequencing on Resource-Constrained Devices

This dataset contains the curated nanopore raw signal data in <a href="https://www.nature.com/articles/s41587-021-01147-4">BLOW5 format </a>used to benchmark <a href="https://github.com/beebdev/HARU/">Hardware Accelerated Read Until (HARU)</a>. This dataset was created by using the publicly available datasets: <a href="https://community.artic.network/t/links-to-raw-fast5-fastq-data-for-artic-protocol/17">SARS-CoV-2 SP1</a> (1.382M reads) and <a href="https://ncbi.nlm.nih.gov/sra/SRX11368475">NA12878 PromethION subset</a> (500,000 reads). The tarball when extracted will have the following directory structure: haru-data ├── na12878-rfc1 │   ├── blow5-rawsignal │   │   ├── na12878_dna_0.blow5 │   │   ├── na12878_dna_100.blow5 │   │   ├── na12878_dna_101.blow5 │   │   ├── na12878_dna_102.blow5 │   │   ├── ... │   └── reference │       └── rfc1.fa └── SARS-CoV-2-sp1     ├── blow5-rawsignal     │   ├── readgroup0     │   │   ├── reads_0_0.blow5     │   │   ├── reads_0_10.blow5     │   │   ├── reads_0_11.blow5     │   │   ├── reads_0_12.blow5     │   │   ├── ...     │   └── readgroup1     │       ├── reads_1_0.blow5     │       ├── reads_1_10.blow5     │       ├── reads_1_11.blow5     │       ├── ....     └── reference         └── nCoV-2019.reference.fasta nCoV-2019.reference.fasta is the SARS-CoV-2 MN908947.3 reference genome. rfc1.fa is the genomic region hr4:39262456-39391375 extracted from hg38 human genome.

opencc-zeroDec 2022View details →

Data for: Earlier leaf senescence dates are constrained by soil moisture

The unprecedented warming that has occurred in recent decades has led to later autumn leaf senescence dates (LSD) throughout the Northern Hemisphere. Yet, great uncertainties still exist regarding the strength of these delaying trends, especially in terms of how soil moisture affects them. Here we show that changes in soil moisture in 1982–2015 had a substantial impact on autumn LSD in one fifth of the vegetated areas in the Northern Hemisphere (>30° N), and how it contributed more to LSD variability than either temperature, precipitation or radiation. We developed a new model based on soil-moisture-constrained cooling degree days (CDDSM) to characterize the effects of soil moisture on LSD and compared its performance with the cooling degree days (CDD), Delpierre (DM) and spring-influenced autumn (SIAM) models. We show that the CDDSM model with inputs of temperature and soil moisture outperformed the three other models for LSD modelling and had an overall higher correlation coefficient (R), a lower root mean square error (RMSE) and lower Akaike information criterion (AIC) between observations and model predictions. These improvements were particularly evident in arid and semi-arid regions. We studied future LSD using the CDDSM model under two scenarios (SSP126 and SSP585) and found that predicted LSD was 4.1±1.4 days and 5.8±2.8 days earlier under SSP126 and SSP585, respectively, than other models for the end of this century. Our study therefore reveals the importance of soil moisture in regulating autumn LSD and, in particular, highlights how coupling this effect with LSD models can improve simulations of the response of vegetation phenology to future climate change.

Analysis of constrained simulations of the Coma cluster and of its surrounding cosmic web

The advent of wide-area spectroscopic galaxy surveys has allowed us to start investigating the properties of the filaments of the cosmic web. How filaments connect to clusters and how these connections impact cluster evolution is a hot topic in astrophysics, of interest for ongoing experiments and future facilities (from both the gas phase perspective, e.g. eROSITA, and the galaxy distribution, e.g. Euclid). The average connectivity (number of connected filaments) of a few observed and simulated clusters has been measured and it has been found that it scales with cluster mass. We applied a cosmic web detection algorithm (DisPerSE) to the Sloan Digital Sky Survey (SDSS) to detect the filaments from the galaxy distribution. We then detected three secure filaments connecting to the Coma cluster. This discovery lead to the developing of a further investigation based on constrained numerical simulations, which allow us to reproduce in detail a portion of the nearby Universe, recreating observed clusters including Coma. We analysed these simulations, with the aim of studying the evolution of the filaments around Coma throughout cosmic history and determining the impact of matter accretion channeled through these structures on the evolution of the Coma cluster. In this talk I will review our previous results and introduce the findings we obtained with the study of our constrained numerical simulations.

Constraining Bedrock Groundwater Residence Times in a Mountain System with Environmental Tracer Observations and Bayesian Uncertainty Quantification: Modeling and Data Package

Here we present field observations of dissolved noble gases (He, Ne, Ar, Kr, and Xe), Chloroflourcarbons (CFCs), Sulfurhexaflouride (SF6), and tritium (3H) sampled from the PLM1, PLM6, and PLM7 wells in the East River Colorado (USA) sampled in May, 2021. This observation dataset, along with the presented python modeling scripts to interpret the data, can aide in quantifying groundwater residence times and recharge conditions. The README files describes the directories and scripts.

opencc-zeroFeb 2023View details →

Habitat and not topographic heterogeneity constrains the range sizes of African mammals

Aim: The extinction risks of species are influenced by their geographical range sizes, as species with smaller ranges are more likely to go extinct following disturbance events. Theoretically, heterogeneous landscapes can maintain small-ranged species, because they facilitate the coexistence of taxa that are each constrained to distinct abiotic conditions. However, we do not fully understand whether this process is more attributable to variation in habitat types (habitat heterogeneity) or physical elevations (topographic heterogeneity) across landscapes. Here, we compare the influences of habitat versus topographic heterogeneity on mammalian range sizes in Africa. Location: Africa. Taxon: Mammalia. Methods: We built a phylogenetic generalized least squares model to predict the range sizes of 1033 mammalian species from their functional traits and abiotic conditions. We assessed how the performance of the model changed when we incorporated measures of the local habitat and, separately, topographic heterogeneity that species experience. Results: Habitat and topographic heterogeneity are weakly correlated across Africa (ρ = 0.14). Habitat heterogeneity is inversely related to range size (model coefficient = −0.88). Incorporating habitat heterogeneity significantly decreased the model's AICc, increased its likelihood and increased the proportion of variance that is explained in the range sizes of species. Conversely, topographic heterogeneity is not significantly related to range size and had no impact on the model's AICc, likelihood or predictive performance. This contrast between habitat and topographic heterogeneity is particularly prevalent in carnivorous mammalian clades. Results at multiple spatial resolutions differed minimally. Main Conclusions: Our findings advance ecological theory by demonstrating that a landscape's variation in habitats, and not in elevations, relates inversely with mammalian range size and therefore facilitates the coexistence of small-ranged mammals. Conservation efforts in regions of high habitat heterogeneity will be critical to prevent extinctions in Africa's biodiverse mammalian species.

Processed CMIP6 data for "The climate response to the Mt Pinatubo eruption does not constrain climate sensitivity"

Processed CMIP6 output for Pauling et al. "The climate response to the Mt Pinatubo eruption does not constrain climate sensitivity" submitted to Geophysical Research Letters in 2023. Download this code repository: https://doi.org/10.5281/zenodo.7553024 and follow the instructions in the README to download data and reproduce the results of the paper.

opencc-zeroMar 2023View details →

Seed limitation interacts with biotic and abiotic factors to constrain novel species' impact on community biomass and richness

Seed limitation can narrow down the number of coexisting plant species, limit plant community productivity, and can also constrain community responses to changing environmental and biotic conditions. In a 10-year full-factorial experiment of seed addition, fertilisation, warming, and herbivore exclusion, we tested how seed addition alters community richness and biomass, and how its effects depend on seed origin and biotic and abiotic context. We found that seed addition increased species richness in all treatments, and increased plant community biomass depending on nutrient addition and warming. Novel species, originally absent from the communities, increased biomass the most, especially in fertilised plots and in the absence of herbivores, while adding seeds of local species did not affect biomass. Our results show that seed limitation constrains both community richness and biomass, and highlight the importance of considering trophic interactions and soil nutrients when assessing novel species immigrations and their effects on community biomass.

opencc-zeroMar 2023View details →

Data from: Interactions between fitness components across the life cycle constrain competitor coexistence

Numerous mechanisms can promote competitor coexistence. Yet, these mechanisms are often considered in isolation of one another. Consequently, whether multiple mechanisms shaping coexistence combine to promote or constrain species coexistence remains an open question. Here, we aim to understand how multiple mechanisms interact within and between life stages to determine frequency-dependent population growth, which has a key role stabilizing local competitor coexistence. We conducted field experiments in three lakes manipulating relative frequencies of two Enallagma damselfly species to evaluate demographic contributions of three mechanisms affecting different fitness components across the life cycle: the effect of resource competition on individual growth rate, predation shaping mortality rates, and mating harassment determining fecundity. We then used a demographic model that incorporates carry-over effects between life stages to decompose the relative effect of each fitness component generating frequency-dependent population growth. This decomposition showed that fitness components combined to increase population growth rates for one species when rare, but they combined to decrease population growth rates for the other species when rare, leading to predicted exclusion in most lakes. Because interactions between fitness components within and between life stages vary among populations, these results show that local coexistence is population specific. Moreover, we show that multiple mechanisms do not necessarily increase competitor coexistence, as they can also combine to yield exclusion. Identifying coexistence mechanisms in other systems will require greater focus on determining contributions of different fitness components across the life cycle shaping competitor coexistence in a way that captures the potential for population level variation.

Flow-field correction method to constrain AMOC in coupled model (IPSL-CM6A-LR)

We use the standard version of IPSL-CM6A-LR (Boucher et al., 2020). The ocean component of IPSL-CM6A-LR is the NEMO oceanic model Version 3.6. The dynhpg.F90 file is a modified version of the routine that implements the flow-field correction method, and the namelist_ORCA1_cfg is the modified namelist used to activate the flow field correction, set the parameters, and read the input temperature, salinity, and mask. The mask specifies the region where this method is applied. To implement this method using the IPSL coupled model, consider following these steps: 1. Install the standard configuration in a path 2. Copy the provided dynhpg.F90 in modipsl/modeles/NEMOGCM/CONFIG/ORCA1_LIM3_PISCES/MY_SRC 3. Compile 3. Copy and modify the namelist_ORCA1_cfg in modipsl/config/IPSLCM6/testffc/PARAM The input conservative temperature (in degC), absolute salinity (in g/kg), and mask are provided to the model for the flow field correction are available in the following files: data_sal_sa_1.5LFC.nc (salinity)  data_sal_sa_weak1.5LFC.nc (salinity) data_tem_bigthetao_1.5LFC.nc (temperature) data_tem_bigthetao_weak1.5LFC.nc (temperature) rhdmsk_data_bothBC2.v2.nc (mask) Note that data_sal_sa_1.5LFC.nc and data_tem_bigthetao_1.5LFC.nc are used to constrain the AMOC to the strong state in Jiang et al., (2023).  data_sal_sa_weak1.5LFC.nc and data_tem_bigthetao_weak1.5LFC.nc are used to constrain it to the weak state in Jiang et al., (2023). Finally, some of the main simulated outputs are given. All the outputs are the annual mean ensemble mean (3 members) data lasting for 100 years. The initial states of the 3 members are sampled in the years of 1850, 2000 and 2080 of the CMIP6 piControl simulation available on ESGF, corresponding to neutral, strong, and weak AMOC states respectively. heatc_strong_3runs_1Y.nc (ocean heat content in J/m2) sos_strong_3runs_1Y.nc (sea surface salinity in psu) tos_strong_3runs_1Y.nc (sea surface temperature in degC) precip_strong_3runs_1Y.nc (precipitation in kg/(s*m2)) diaptrW_strong_3runs_1Y.nc (meridional streamfunction in Sv) slp_strong_3runs_1Y.nc (sea level pressure in Pa) geop500_strong_3runs_1Y.nc (geopotential height at 500 hPa in m) nettop0_strong_3runs_1Y.nc (clear-sky solar radiation at the top of atmosphere in W/m2) nettop_strong_3runs_1Y.nc (net downward flux at the top of atmosphere in W/m2) t2m_strong_3runs_1Y.nc (air temperature at 2-m in K) vitu850_strong_3runs_1Y.nc (zonal wind at 850 hPa in m/s) cld_strong_3runs_1Y.nc (low-level and high-level cloudiness, unitless) tauuo_strong_3runs_1Y.nc (surface downward stress in the x-direction in N/m2) tauvo_strong_3runs_1Y.nc (surface downward stress in the y-direction in N/m2) The files listed above are the results from simulations where the AMOC is constrained to the strong state (i.e., using the input data_tem_bigthetao_1.5LFC.nc and data_sal_sa_1.5LFC.nc). Similarly, the same fields from simulations where the AMOC is constrained to the weak state (i.e., using the input data_tem_bigthetao_weak1.5LFC.nc and data_sal_sa_weak1.5LFC.nc) are: heatc_weak_3runs_1Y.nc (ocean heat content in J/m2) sos_weak_3runs_1Y.nc (sea surface salinity in psu) tos_weak_3runs_1Y.nc (sea surface temperature in degC) precip_weak_3runs_1Y.nc (precipitation in kg/(s*m2)) diaptrW_weak_3runs_1Y.nc (meridional streamfunction in Sv) slp_weak_3runs_1Y.nc (sea level pressure in Pa) geop500_weak_3runs_1Y.nc (geopotential height at 500 hPa in m) nettop0_weak_3runs_1Y.nc (clear-sky solar radiation at the top of atmosphere in W/m2) nettop_weak_3runs_1Y.nc (net downward flux at the top of atmosphere in W/m2) t2m_weak_3runs_1Y.nc (air temperature at 2-m in K) vitu850_weak_3runs_1Y.nc (zonal wind at 850 hPa in m/s) cld_weak_3runs_1Y.nc (low-level and high-level cloudiness, unitless) tauuo_weak_3runs_1Y.nc (surface downward stress in the x-direction in N/m2) tauvo_weak_3runs_1Y.nc (surface downward stress in the y-direction in N/m2)