Find research datasets worth reusing

X-ray scattering datasets associated with the publication "Side chain length dependent dynamics and conductivity in self assembled ion channels"

X-ray scattering datasets for samples described in the 2022 publication "Side chain length dependent dynamics and conductivity in self assembled ion channels". This dataset includes both raw and processed X-ray scattering data for samples ILC8, ILC10, ILC12, ILC14 and ILC16 alongside background measurement files (BKG).

opencc-by-4.0Jan 2023View details →

Impact of climate warming on phenological asynchrony of plankton dynamics across Europe

This dataset includes all data as well as the scripts used to produce figures from both the main text and the supporting information from Gronchi et al. (2023) "Impact of climate warming on phenological asynchrony of plankton dynamics across Europe".   Detailed description: Data_TDM_Validation.csv: file containing observed versus simulated timings of TDM for 18 lakes of western Europe. result_full_reference.mat and result_full_const_4C_warming.mat contain the 31-years medians of the lake phenologies for the 16 different simulated lake types and for both the reference and the constant +4°C climate scenario. The two scripts Plot_Fig_1_and_3.m and Plot_Supplement.m are responsible for the production, after treatment of the data, of figures 1 and 3 from the main text and most of the supplement respectively. These scripts include a detailed description of the variables used for producing these figures. Figures 2, 4, S1, S2 and S3 were done with R. For each of these figures there is their respective R script and the reorganized dataset used to produce them.   Because of the large size of the inputs and outputs files analyzed in this study we only included the scripts (Output_Generation_Example.m and lakelayerdepth_1.m) and the data ( Meteo_Era_52.5_13.5.dat; Z30_ST00_KW06_52.5_13.5.LST Z30_ST00_KW06_52.5_13.5.Temp) necessary for generation of the phenology medians for one specific lake type at the reference scenario as example. The full dataset (~4To) can be given upon request. Ackowledgment: This product includes color specifications and designs developed by Cynthia Brewer (http://colorbrewer.org/)

Dynamical simulation of EBSD master pattern of chi-phase in steel

Dynamical simulation of an electron backscatter diffraction (EBSD) master pattern of a chi-phase (Fe36Cr15Mo7) in steel (I$\bar{4}$3m, a = 8.854 Å) (see Kasper [1954], doi:<a href="https://doi.org/10.1016/0001-6160(54)90066-8">10.1016/0001-6160(54)90066-8)</a>. The master pattern was simulated with EMsoft v5.0. The HDF5 file includes master patterns of the upper and lower hemispheres, in both the stereographic projection and the square Lambert projection, of accelerating voltages from 10 to 20 kV with an increment of 1 kV. The HDF5 file can be opened with any HDF5 reader, e.g. the applications HDFView and HDFCompass or the Python library h5py. The file can also be read and plotted with the Python library kikuchipy (https://kikuchipy.org). Assuming Python and the library is installed, the stereographic projection of the master pattern with all energies can be read and plotted with the following commands: <pre><code class="language-python">import kikuchipy as kp s = kp.load("/path/to/steel_chi_mc_mp_20kv.h5") s.plot()</code></pre> The PNG files show the stereographic projection of the upper and lower hemispheres of the master pattern from 20 kV. The remaining files are input and output files to the EMsoft programs EMmkxtal (output: steel_chi.xtal), EMMCOpenCL (input: steel_chi.xtal, mcopencl.nml; output: steel_chi_mc_mp_20kv.h5) and EMEBSDmaster (input: BetheParameters.nml, ebsdmaster.nml, steel_chi_mc_mp_20kv.h5; output: added to existing steel_chi_mc_mp_20kv.h5).

Dynamical simulation of EBSD master pattern of aluminium

Dynamical simulation of an electron backscatter diffraction (EBSD) master pattern of aliminium (Fm$\bar{3}$m, a = 4.04 Å). The master pattern was simulated with EMsoft v4.1. The HDF5 file includes master patterns of the upper and lower hemispheres, in both the stereographic projection and the square Lambert projection, of accelerating voltages from 10 to 20 kV with an increment of 1 kV. The HDF5 file can be opened with any HDF5 reader, e.g. the applications HDFView and HDFCompass or the Python library h5py. The file can also be read and plotted with the Python library kikuchipy (https://kikuchipy.org). Assuming Python and the library is installed, the stereographic projection of the master pattern with all energies can be read and plotted with the following commands: <pre><code class="language-python">import kikuchipy as kp s = kp.load("/path/to/am_mc_mp_20kv.h5") s.plot()</code></pre> The PNG file shows the stereographic projection of the upper hemisphere of the master pattern from 20 kV. The al.xtal file was produced with the EMsoft program EMmkxtal, contains a complete description of the crystal structure, and is used as input to the EMsoft programs EMMCOpenCL and EMEBSDmaster. The latter two programs produced the al_mc_mp_20kv.h5 file.

The MAMMAMIA project: A multi-scale multi-method approach to understand runoff-induced changes in the subglacial environment and consequences for surge dynamic in Kongsvegen glacier, Svalbard

Data set at a 3h resolution of all the data used in the study ''The MAMMAMIA project: A multi-scale multi-method approach to understand runoff-induced changes in the subglacial environment and consequences for surge dynamic in Kongsvegen glacier, Svalbard', Coline bouchayer, Ugo Nanni, Pierre-Marie Lefeuvre, John Hulth, Louise Schmidt, Jack Kohler, Francois Renard and Thomas V. Schuler. The mansucript is in prepartaion to be submitted to The Crysophere (@ Add DOI when submitted).

opencc-zeroFeb 2023View details →

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Data for: Amazonian birds in more dynamic habitats have less population genetic structure and higher gene flow

Understanding the factors that govern variation in genetic structure across species is key to the study of speciation and population genetics. Genetic structure has been linked to several aspects of life history, such as foraging strategy, habitat association, migration distance, and dispersal ability, all of which might influence dispersal and gene flow. Comparative studies of population genetic data from species with differing life histories provide opportunities to tease apart the role of dispersal in shaping gene flow and population genetic structure. Here, we examine population genetic data from sets of bird species specialized on a series of Amazonian habitat types hypothesized to filter for species with dramatically different dispersal abilities: stable upland forest, dynamic floodplain forest, and highly dynamic riverine islands. Using genome-wide markers, we show that habitat type has a significant effect on population genetic structure, with species in upland forest, floodplain forest, and riverine islands exhibiting progressively lower levels of structure. Although morphological traits used as proxies for individual-level dispersal ability did not explain this pattern, population genetic measures of gene flow are elevated in species from more dynamic riverine habitats. Our results suggest that the habitat in which a species occurs drives the degree of population genetic structuring via its impact on long-term fluctuations in levels of gene flow, with species in highly dynamic habitats having particularly elevated gene flow. These differences in genetic variation across taxa specialized in distinct habitats may lead to disparate responses to environmental change or habitat-specific diversification dynamics over evolutionary time scales.

Collective Decision-Making and Change Detection with Bayesian Robots in Dynamic Environments

The following folder structure holds all research data of my conducted experiments(h5-logfiles and plots). The Python-Script "show_h5.py" can be used to read out the logfile in h5-format ($python3 show_h5.py expample_logfilename.h5). However, this shouldn't be necessary because all plots are already generated. To find the results you want to see, this is a small guide through the structure: <ol> <li> First the trials are divided into the respective methods (PELT, DBB, DBBCPD). In the folders you find the experiments for the specific method. </li> <li> In the folder of PELT you find the results for the different feedback types and their combinations. The id for each feedback is noted in parentheses (e.g. XX_(id)_feedback_description). Feedback combinations have their ids added up (e.g. XX_(id1+...+idn)_feedback_description). </li> <li> In the folder to each feedback type the different test trials can be found. This means varying environment difficulties and parameter settings. In the name of the folders this information can be found (e.g. XX_method_environmentdifficulty_parametersetting). </li> </ol> All experiments follow the same procedure as long as it is stated otherwise. Each trial consists of 20 individual runs with a duration of 6000 seconds. At half time (3000 s) a change to the opposite fill ratio occurs (fill ratio of 1.0 defines a completely white and one of 0.0 a completely black environment). Environment difficulty <ul> <li> 0901 --> easy environment, fill ratio changed from 0.9 to 0.1 </li> <li> 0703 --> easy environment, fill ratio changed from 0.7 to 0.3 </li> <li> 0604 --> easy environment, fill ratio changed from 0.6 to 0.4 </li> <li> 055045 --> easy environment, fill ratio changed from 0.55 to 0.45 </li> </ul> Parameter Setting The setting is in the name of the folder composed of: feedbackID: intervalLength amountNeighbors <ul> <li> 3c:50s3n --> feedback 3c with a 50s interval and 3 neighbors </li> </ul> In these folders all plots of the respective runs can be found showing a Boxplot of all 20 runs and for each run the swarm belief, the decision distribution and the reset histogram (before/after the change)

opencc-zeroFeb 2023View details →

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Data from: Perturbations highlight importance of social history in parakeet rank dynamics

Dominance hierarchies can provide many benefits to individuals, such as access to resources or mates, depending on their ranks. In some species, rank can emerge as a product of a group's history of social interactions. However, it can be difficult to determine whether social history is critical to rank in observation-based studies. Here, we investigated rank dynamics in three captive groups of monk parakeets (Myiopsitta monachus). We used experimental social perturbations to test whether social history shapes rank emergence in these groups. Using targeted removals and reintroductions, we tested whether differently ranked individuals could re-take their ranks in hierarchies after reintroduction following their removal period from the group. We performed perturbations that consisted of an eight-day removal and an eight-day reintroduction period of 15 differently ranked focal birds. We found that no focal birds could regain their previous rank immediately following reintroduction and that the top-ranked birds showed greater relative rank loss than middle/low-ranked birds. We also found that morphology, specifically body weight, was unassociated with rank. Combined with previous results, this experiment supports the hypothesis that rank in monk parakeet dominance hierarchies is more likely to be an emergent outcome of past interactions and memory rather than based on individual characteristics. Gaining a better understanding of how individuals achieve and maintain rank can give insight into the role of cognition on rank acquisition, as rank position can have significant biological effects on individuals in hierarchically structured groups.

opencc-by-4.0Jul 2022View details →

Petrucco et al, Neural dynamics and architecture of the heading direction circuit in a vertebrate brain [Dataset]

Data supporting the paper <a href="https://www.biorxiv.org/content/10.1101/2022.04.27.489672v1.full">Neural dynamics and architecture of the heading direction circuit in a vertebrate brain</a>.

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Effects of temporal abiotic drivers on the dynamics of an allometric trophic network model

Current ecological research and ecosystem management call for improved understanding of the abiotic drivers of community dynamics, including temperature effects on species interactions and biomass accumulation. Allometric trophic network (ATN) models, which simulate material (carbon) transfer in trophic networks from producers to consumers based on mass-specific metabolic rates, provide an attractive framework to study consumer-resource interactions from organisms to ecosystems. However, the developed ATN models rarely consider temporal changes in some key abiotic drivers that affect e.g. consumer metabolism and producer growth. Here, we evaluate how temporal changes in carrying capacity and light-dependent growth rate of producers and in temperature-dependent mass-specific metabolic rate of consumers affect ATN model dynamics, namely seasonal biomass accumulation, productivity and standing stock biomass of different trophic guilds, including age-structured fish communities. Our simulations of the pelagic Lake Constance (LC) food web indicated marked effects of temporally changing abiotic parameters on seasonal biomass accumulation of different guild groups, particularly among the lowest trophic levels (primary producers and invertebrates). While the adjustment of average irradiance had a minor effect, increasing metabolic rate associated with 1–2˚C temperature increase led to a marked decline of larval (0-year age) fish biomass, but to a substantial biomass increase of 2- and 3-year-old fish that were not predated by ≥4-year-old top predator fish, European perch. However, when averaged across the 100 simulation years, the inclusion of seasonality in abiotic drivers caused only minor changes in standing stock biomasses and productivity of different trophic guilds. Our results demonstrate the potential of introducing seasonality in and adjusting the average values of abiotic ATN model parameters to simulate temporal fluctuations in food-web dynamics, which is an important step in ATN model development aiming to e.g. assess potential future community-level responses to ongoing environmental changes.

opencc-zeroMar 2023View details →

Pylake tutorial dataset: Population dynamics

A dataset of force data measured on a LUMICKS C-Trap. The system consists of a DNA hairpin construct tethered between two polystyrene beads held at a constant distance with the optical traps.

Silica in Silico: a Molecular Dynamics Characterization of the Early Stages of Protein Embedding for Atom Probe Tomography

The .zip archive contains the trajectories of all the simulations performed and analysed within the manuscript. The water molecules were removed for control systems.

Research data supporting: "Innate dynamics and identity crisis of a metal surface unveiled by machine learning of atomic environments"

This repository contains the set of data shown in the paper "Innate dynamics and identity crisis of a metal surface unveiled by machine learning of atomic environments", published on The Journal of Chemical Physics (DOI:10.1063/5.0139010)

Accompanying empirical data for Kirchherr et al., 2023, "Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex"

This repository contains data accompanying: Kirchherr et al., 2023, "Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex". Data collection methods: Two adult female rhesus macaques (Macaca mulatta) trained on a reaching, and grasping, and placing task served as the subjects. The animal handling as well as surgical and experimental procedures complied with European guideline (2010/63/UE) and authorized by the French Ministry for Higher Education and Research (project # 2016112713202878) in force on the care and use of laboratory animals, and were approved by the ethics committee CELYNE (comité d’éthique Lyonnais pour les neurosciences expérimentale, C2EA 42). After initial training, we performed a sterile surgery to implant six floating multielectrode arrays (FMA, Microprobes for Life Science, Gaithersburg, MD, USA) in the right (monkey 1) or left (monkey 2) cortical hemisphere. Each array was comprised of 32 platinum/iridium electrodes (impedance 0.5 MΩ at 1 kHz) with lengths ranging from 1 to 6 mm, and with an inter-electrode spacing of 400 μm. One electrode array was implanted in the primary motor cortex (M1), two were implanted in the ventral premotor cortex (F5), one in the dorsal premotor cortex (F2), and two in the prefrontal cortex (45a and 46/12r), as estimated according to a previous magnetic resonance imaging scan. For the purposes of this study, we analyzed data from the M1 array of each monkey. The wideband neural signal (bandpass filtered at 0.1 to 7500 kHz) was recorded at 30 kS/s, and amplified and digitized (16-bit; 0.192 μV resolution) with an Intan Tech-based (Intan Technologies, Los Angeles, CA, USA) open source acquisition system (Open Ephys; Siegle et al. 2017). This system uses a 256-channel Intan RHD2000 series acquisition board and 32-channel headstages (RHD2132). Spike detection was performed offline using Trisdesclous (Garcia & Pouzat,2015). The common reference was removed to reduce ambient noise. Spikes were then detected from each electrode using a threshold of 2 times the median absolute deviation (MAD), and analyzed as multi-unit activity (MUA) in 10 ms bins. All electrodes in which at least one well-isolated spike waveform was detected were selected for the following analyses. We thus used a sample of 21 electrodes out of 32 for monkey 1, and 25 out of 32 electrodes for monkey 2. Custom made detection panels were used to record the moments when the monkey’s hand released the handle, the hand contacted the target object, and when the object was placed in the groove. An Omniplex 16-channel recording system (Plexon, Dallas, TX, USA) was used to simultaneously record these behavioral events. Trials were discarded if the response time (time between the go signal and handle release) was less than 100 or greater than 1500 ms, the reach duration (time between handle release and object contact) was less than 100 or greater than 1000 ms, or the placing duration (time between object contact and placing the object in the groove) was less than 100 or greater than 1200 ms, leaving 19 - 68 trials per day for monkey 1 (M = 43.9, SD = 15.46, N = 439; left: M = 14.8, SD = 5.74; center: M = 14.4, SD = 5.15; right: M = 14.7, SD = 7.73), and 23 - 49 per day for monkey 2 (M = 38.3, SD = 9.87, N = 383; left: M = 14.2, SD = 3.91; center: M = 10.8, SD = 3.55; right: M = 13.3, SD = 3.37). Abstract: Neural populations, rather than single neurons, may be the fundamental unit of cortical computation. Analyzing chronically recorded neural population activity is challenging not only because of the high dimensionality of activity in many neurons, but also because of changes in the recorded signal that may or may not be due to neural plasticity. Hidden Markov models (HMMs) are a promising technique for analyzing such data in terms of discrete, latent states, but previous approaches have either not considered the statistical properties of neural spiking data, have not been adaptable to longitudinal data, or have not modeled condition specific differences. We present a multilevel Bayesian HMM which addresses these shortcomings by incorporating multivariate Poisson log-normal emission probability distributions, multilevel parameter estimation, and trial-specific condition covariates. We applied this framework to multi-unit neural spiking data recorded using chronically implanted multi-electrode arrays from macaque primary motor cortex during a cued reaching, grasping, and placing task. We show that the model identifies latent neural population states which are tightly linked to behavioral events, despite the model being trained without any information about event timing. We show that these events represent specific spatiotemporal patterns of neural population activity and that their relationship to behavior is consistent over days of recording. The utility and stability of this approach is demonstrated using a previously learned task, but this multilevel Bayesian HMM framework would be especially suited for future studies of long-term plasticity in neural populations.

The 400 ns molecular dynamic (MD) trajectories for the wild type and mutant forms of the S. tuberosum eIF4E1 and eIF4E2

Truncated from the N termini models of the wild type and mutant forms of the S. tuberosum eIF4E1 and eIF4E2.  The molecular dynamic (MD) trajectories with 400-ns length for each wild type and mutant forms of the eIF4E in the water environment according to the standard MD procedure.

A Mixed-Flux-Based Nodal Discontinuous Galerkin Method for 3D Dynamic Rupture Modeling

This repository contains data produced by a mixed-flux-based discontinuous Galerkin method for 3D dynamic rupture modeling, using the software DRDG3D (<a href="https://github.com/wqseis/drdg3d">https://github.com/wqseis/drdg3d</a>). Input scripts for the SCEC/USGS dynamic rupture benchmark validation problems (<a href="https://strike.scec.org/cvws">https://strike.scec.org/cvws</a>) and other cases are hosted on DRDG3D's GitHub page. The preprint is published at ESS Open Archive (DOI: <a href="http://doi.org/10.1002/essoar.10512657.1">10.1002/essoar.10512657.1</a>).

opencc-by-4.0Jan 2021View details →

Dynamic and membrane DAC ramp dataset storage, raw and azimuthal integrated as xy.files.

The datasets are part of a project exploring the onset of phase transitions, the development of microstrain and the lattice parameters during fast compression rates, resembling those during e.g. propagation seismic shockwaves and large body impacts. The strain rate conditions achieved in our experiments are between those reached in the common static (DAC) and shock experiments. The current datasets contain a selected set of xy.files of azimuthal integrated 2D-diffraction images of either Mg0.2Fe0.8O sample or  Fe (Iron) - with and without Pt (platinum) pressure marker and with and without Ne (Neon) pressure transmitting medium (PTM). Diffraction images were collected at ambient temperature (in 2019 and 2020) at the ECB P02.2 Beamline, PETRA III, DESY, Germany using the piezo-driven dynamic diamond anvil cells (dDAC) in combination with fast LAMBDA GaAs 2M detectors at 25.6 keV (0.4843 Å). The datasets are provided as test datasets for machine learning application on spectra classification. The version will be further updated once the whole work has been published and the full datasets can be made available. Please find below the link towards the machine learning application. <a href="https://github.com/European-XFEL-examples/panosc-ml-spectra-classification">https://github.com/European-XFEL-examples/panosc-ml-spectra-classification</a>

The evolution of lexical semantics dynamics, directionality, and drift: S4

Supplementary Material (S4) for the study "The evolution of lexical semantics dynamics, directionality, and drift", for Frontiers in Communication, Special Issue "<a href="https://www.frontiersin.org/research-topics/38650/the-evolution-of-meaning-challenges-in-quantitative-lexical-typology?fbclid=IwAR3AeXx_11P-8CZG0UavlOgqvbVo6MlMxX8AejtPiREAKmeLyy3LIB3Ux24">The Evolution of Meaning: Challenges in Quantitative Lexical Typology</a>", ed. Gerd Carling & Annemarie Verkerk

Protected ultrathin cuprous oxide film for photocatalysis: Excitation and relaxation dynamics

The main data analysis was done with Wavemetrics Igor Pro 7.08 using user-defined macros. Data files given in *.itx format are human-readable text files that can be opened in Igor Pro. User-defined macros are available from the authors upon reasonable request. Static spectra are measured with the proprietary "Croissant" software for the channeltron analyzer and saved in human-readable *.plsp format, or SpecsLab Prodigy 4.60.1 for the 2D analyzer and saved in the proprietary SPECS *.sle format or exported into *.itx format. Time-resolved spectra are measured with a proprietary LabView program and exported in the binary HDF5 *.h5 file format. ******************************************** Fig. 1 LEED and He Ia ARPES ******************************************** LEED images taken with SBIG STF-8300 CCD Camera, the SBIG format is a 16-bit grayscale bitmap with metadata. Fig. 1a: LEED image at 120 eV LEED220210_120eV_Cu111_hBN_Cu2O.SBIG Fig. 1b: LEED image at 48 eV LEED220210_048eV_Cu111_hBN_Cu2O.SBIG other energies (not shown in the figure): see Fig. S2/S3 Fig. 1c: He Ia spectrum second derivative as function of parallel momentum and binding energy Spectra measured with VG ESCALAB 220 channeltron hemispherical analyzer by tilting the sample at two fixed azimuthal angles and using a Gammadata VUV 5050 monochromated helium lamp. The azimuthal angles correspond to the M and K directions, respectively, as determined by x-ray photoelectron diffraction of the Cu(111) surface. Measurement parameters are given in the files. Positive parallel momentum: M direction, VG2Z220628N015.plsp Negative parallel momentum: K direction, VG2Z220628N016.plsp Combined ARPES spectrum as a function of parallel momentum and binding energy: VG2Z220628N015_N016.itx Second derivative along energy direction: VG2Z220628N015_N016d.itx; smoothed: VG2Z220628N015_N016d_smth.itx ************************************************ Fig. 2 He IIa ARPES ************************************************ All ARPES spectra measured with SPECS Phoibos 150 WAL hemispherical analyzer (2D detector) using SpecsLab Prodigy software and non-monochromated helium lamp. Fig. 2a: Detail of He IIa spectrum measured on h-BN/Cu(111) Full angle-resolved spectrum (intensity as function of angular coordinate and kinetic energy), summed over all energy channels and scans, exported from SpecsLab Prodigy as Igor Text. All measurement parameters are given in the file. Spectrum HeIIa h-BN Cu111 20201023.itx Spectrum scale converted into parallel momentum and binding energy: Spectrum HeIIa h-BN Cu111 20201023 k2.itx Fig. 2b: Detail of He IIa spectrum measured on h-BN/Cu2O/Cu(111) Full angle-resolved spectrum (intensity as function of angular coordinate and kinetic energy), summed over all energy channels and scans, exported from SpecsLab Prodigy as Igor Text. All measurement parameters are given in the file. Spectrum HeIIa h-BN Cu2O Cu111 20220211.itx Spectrum scale converted into parallel momentum and binding energy: Spectrum HeIIa h-BN Cu2O Cu111 20220211 k2.itx Fig. 2c: Spectra integrated over given parallel momentum range Intensity as a function of binding energy Spectrum HeIIa h-BN Cu111 20201023 k2 042_092.txt Spectrum HeIIa h-BN Cu2O Cu111 20220211 k2 041_091.txt ************************* Fig. 3 2PPE spectra of Cu(111), h-BN/Cu(111) and h-BN/Cu2O/Cu(111) ************************* Spectra measured with SPECS Phoibos 150 WAL hemispherical analyzer (2D detector) using SpecsLab Prodigy software and exported as Igor Text. All measurement parameters are given in the files. 3eV wavelength was 412nm, p-polarized. A -10V bias voltage was applied to the sample. Cu(111) 2PPE: P=1mW, Cu111 2022-04-14_20h47m45s.itx Cu(111) 3PPE: P=3mW, Cu111 2022-04-14_21h09m29s.itx h-BN/Cu(111) 2PPE: P=1.0mW, 0.5mm entrance slit, Cu111 hBN Spectrum3eV_2B_ppol.itx h-BN/Cu(111) 3PPE: P=0.4mW, 3.0mm entrance slit, Cu111 hBN Spectrum3eV_4B_ppol.itx h-BN/Cu2O/Cu(111) 2PPE: P~0.1mW, Cu111 hBN Cu2O 2022-02-11_17h07m34s.itx h-BN/Cu2O/Cu(111) 3PPE: P~0.1mW, Cu111 hBN Cu2O 2022-02-11_16h58m49s.itx ************************************************* Fig. 4 Delay Scan ************************************************* Delay scans are recorded with proprietary LabView software and saved in binary HDF5 format as a 3D stack of detector images (intensity as function of angular coordinate and kinetic energy) as a function of pump-probe delay. Bias -5V, 3eV=413nm 0.3mW p-pol, 6eV=208nm 1nA p-pol, Ekin=11.9eV, Epass=20eV, 1mm slit, exposure 10x500ms, 20 scans, 10fs steps Raw data, 256 angular pixels x 348 energy pixels x 201 delays x 20 scans: WAL_20220215_UZH_JB_dscan_040_0to9.h5 WAL_20220215_UZH_JB_dscan_040_10to19.h5 Sum of all scans (transposed): WAL_20220215_UZH_JB_dscan_040_sum.h5 Cropped to active detector window and applied distortion correction and correct scaling: dscan_20220215_040_data3Dcorrected.h5 Integrated over +-10° angular window: dscan040.itx Background averaged over delay positions 0-19 subtracted and energy and delay scales corrected: Fig. 4a: dscan040bgi0.itx Delay scan with 50ps range and 0.2ps steps, not shown in figure but analyzed the same way: dscan041bgi.itx Fig. 4b: Intensity as a function of intermediate state energy 50 fs, delay positions 40-50: dscan040t0mbg.txt 1 ps, delay positions 140-150: dscan040t2mbg.txt 10 ps, delay positions 55-65 in dscan041bgi: dscan041t3mbg.txt Fig. 4c: detector image obtained by averaging images at delay positions 40-50 and subtracting the background image, then correcting the angular distortion by normalizing the intensity at the Fermi energy dscan040image_diff0.itx ************************************************* Fig. 5 Fit Curves ************************************************* The Igor Pro batch fitting procedure was used with a custom fitting function to fit the delay scan dscan040bgi binned in 0.1 eV intervals with index 0 at -0.3 eV. The displayed curves have index 13 (1.0 eV), 6 (0.3 eV) and 4 (0.1 eV). Binned delay scan: dscan040bgi_pix.itx Fit parameters: dscan040bgi_fitparams.txt Fit result: dscan040bgi_pixRateFits.itx Fast component: dscan040bgi_FastComponent.itx Slow component: dscan040bgi_SlowComponent.itx ******************************************* Fig. 6 Fit Results Comparison ******************************************* Relaxation times are extracted from the batch fit results of different delay scans. 3 nJ pump: dscan040bgi_fitparams.txt dscan_220215_040 20 nJ pump: dscan010_BG4_fitparams.txt dscan220703_010 (data: dscan010_BG4.itx, binned: dscan010_BG4_pix.itx, fits: dscan010_BG4_pixFit.itx) h-BN/Cu(111): dscan033_fitparams.txt (binned data: dscan044_hBN_side_pix1.itx, fits: dscan033_hBN_side_pixFits.itx) Lisowski et al.: LifetimesLisowski.txt (data from M. Lisowski, P. A. Loukakos, U. Bovensiepen, and M. Wolf, Femtosecond Dynamics and Transport of Optically Excited Electrons in Epitaxial Cu Films on Si(111)-7 x 7, Appl. Phys. A 79, 739 (2004)) Extrapolation: fit_LifetimesLisowski.txt, using power law tau=0.054797*E^(-1.1419) **************************************************** Fig. S1 XPS **************************************************** Preparation 1 before oxidation:        Preparation 1 after oxidation:        Preparation 2 after oxidation: B 1s: VG2Z220209N005.pesp        B 1s: VG2Z220210N023.pesp        B 1s: VG2Z220628N002.pesp N 1s: VG2Z220209N006.pesp        N 1s: VG2Z220210N024.pesp        N 1s: VG2Z220628N003.pesp C 1s: VG2Z220209N007.pesp        C 1s: VG2Z220210N025.pesp        C 1s: VG2Z220628N004.pesp O 1s: VG2Z220209N008.pesp        O 1s: VG2Z220210N026.pesp        O 1s: VG2Z220628N005.pesp Cu 2p:VG2Z220209N009.pesp        Cu 2p:VG2Z220210N027.pesp        Cu 2p:VG2Z220628N006.pesp **************************************************** Fig. S2 LEED ****************************************************   48 eV: LEED220209_048eV_Cu111_hBN.SBIG   70 eV: LEED220209_070eV_Cu111_hBN.SBIG 100 eV: LEED220209_100eV_Cu111_hBN.SBIG not shown in figure:   40 eV: LEED220209_040eV_Cu111_hBN.SBIG 120 eV: LEED220209_120eV_Cu111_hBN.SBIG **************************************************** Fig. S3 LEED ****************************************************   40 eV: LEED220210_040eV_Cu111_hBN_Cu2O.SBIG   48 eV: LEED220210_048eV_Cu111_hBN_Cu2O.SBIG   70 eV: LEED220210_070eV_Cu111_hBN_Cu2O.SBIG 100 eV: LEED220210_100eV_Cu111_hBN_Cu2O.SBIG 120 eV: LEED220210_120eV_Cu111_hBN_Cu2O.SBIG 150 eV: LEED220210_150eV_Cu111_hBN_Cu2O.SBIG **************************************************** Fig. S4 LEED ****************************************************   40 eV: LEED220628_040eV_Cu111_hBN_Cu2O.SBIG   48 eV: LEED220628_048eV_Cu111_hBN_Cu2O.SBIG   70 eV: LEED220628_070eV_Cu111_hBN_Cu2O.SBIG 100 eV: LEED220628_100eV_Cu111_hBN_Cu2O.SBIG 110 eV: LEED220628_110eV_Cu111_hBN_Cu2O.SBIG 140 eV: LEED220628_140eV_Cu111_hBN_Cu2O.SBIG 180 eV: LEED220628_180eV_Cu111_hBN_Cu2O.SBIG not shown in figure: 120 eV: LEED220628_120eV_Cu111_hBN_Cu2O.SBIG 150 eV: LEED220628_150eV_Cu111_hBN_Cu2O.SBIG ********************************************* Fig. S5 Work function maps ********************************************* Ekin=10.4eV, Epass=20eV, 6eV=208.5nm 0.2nA p-pol, 1mm slit, -5V bias, exposure 1x500ms, 0.1mm steps, 81x81 pixels Preparation 1: Data cube after lens correction and correct scaling: dscan_220221_003_data3Dcorrected.h5 Integrated over all angles and brought into raster format: dscan_220221_003_raster.h5 Work function map: dscan_220221_003_rasterWF.itx Inset: Photograph of the sample after preparation 1, "Photograph hBN Cu2O Cu.png" Preparation 2: Data cube after lens correction and correct scaling: dscan_220704_011_data3Dcorrected.h5 Integrated over all angles: dscan_220704_011_raster.h5 Work function map: dscan_220704_011_rasterWF.itx ************************************************ Fig. S6 Delay Scans ************************************************ See description of Fig. 4 for processing details. Pump power was measured with a thermal powermeter and probe power was measured with a Thorlabs SM05PD7A GaP-photodiode with 14.4 mA/W sensitivity at 208nm. The thermal powermeter has an accuracy of +-0.1mW. 1.0mW pump power at 100kHz repetition rate equals 10nJ pulse energy. 1.0nA photodiode current equals approximately 70nW probe power or 0.7 pJ probe energy. Common settings: Bias voltage -5V, exposure time 20x500ms, 20 scans, 20fs steps, entrance slit size 1mm, Pass energy 20eV, Kinetic energy 11.9 eV, pump wavelength 413nm, pump and probe beam are p-polarized. Differing settings are written for each dataset. Fig. S6a: dscan_220215_040/dscan040bgi0.itx, processed raw data: dscan_220215_040_data3Dcorrected.h5 0.3mW pump, 208nm 1.0nA probe, 10fs steps, exposure 10x500ms Fig. S6b: dscan_220217_045/dscan045bgi.itx, processed raw data: dscan_220217_045_data3Dcorrected.h5 0.3mW pump, 208nm 1.0nA probe, 10fs steps, Ekin=13.3eV, Epass=30eV, 7mm slit, 50 scans Fig. S6c: dscan_220216_041/dscan041bgi.itx, processed raw data: dscan_220216_041_data3Dcorrected.h5 0.3mW pump, 208nm 1.0nA probe, 200fs steps, exposure 10x500ms, 10 scans ************************************************ Fig. S7 Delay Scans ************************************************ Common settings: same as in Fig. S6 Fig. S7a: dscan_220701_020/dscan020bgi0.itx, processed raw data: dscan_220701_020_data3Dcorrected.h5 0.5mW pump, 209nm 0.9nA probe, 3mm slit Fig. S7b: dscan_220712_029/dscan029_BG.itx, processed raw data: dscan_220712_029_data3Dcorrected.h5 1.5mW pump, 209nm 1.0nA probe Fig. S7c: dscan_220703_010/dscan010_BG4.itx, processed raw data: dscan_220703_010_data3Dcorrected.h5 2.0mW pump, 209nm 0.9nA probe Fig. S7d: dscan_220417_033/dscan033_hBN_side0.itx, processed raw data: dscan_220417_033_data3Dcorrected.h5 1.0mW pump, 208nm 0.1nA probe, Ekin=11.8eV, 40 scans *********************************************** Fig. S8 Beam profiles *********************************************** 8-bit CCD images were acquired with a Basler puA1280-54um CCD camera and Basler pylonViewer 5.0 acquisition software. Sensor resolution: 1280x960, pixel size: 3.75x3.75 micrometers 3 eV image: 10 microseconds exposure time, Spot3eV 10us 20mm.bmp 6 eV image: 100 milliseconds exposure time, Spot6eV 100ms 20mm.bmp