Find research datasets worth reusing

opencc-zeroMay 2019View details →

IODP Expedition 369 Stratigraphic Correlation

Affine and splice files created for stratigraphic correlation.

opencc-by-4.0Jun 2023View details →

Simulated and real datacubes for developing and testing changepoint algorithms for spatially correlated, short and noisy time-series

A set of datacubes where z-dimension is time, thus each (x,y,.) is a timeseries. The idea is to detect sudden changes in each series, assuming 1) the series can be quite short and noisy 2) the change occurs in spatial patches. The set has synthetic examples with known change-events, and a real-world dataset with unknown change-events. The data files are related to the pape T Rajala, P Packalen, M Myllymäki, A Kangas (2023): Improving detection of changepoints in short and noisy time-series with local correlations: Connecting the events in pixel neighbourhoods, "Journal of Agricultural, Biological and Environmental Statistics", https://doi.org/10.1007/s13253-023-00546-1 More of the NFI data is available from Natural Resources Institute Finland, https://kartta.luke.fi/index-en.html See `data/00data_readme.txt` for further details.

opencc-by-nc-sa-4.0Jun 2023View details →

Molecular in situ monitoring of the pH-triggered response in adaptive polymers by two-dimensional Raman micro-correlation-spectroscopy - Raw Data

Raw data for publication Molecular in situ monitoring of the pH-triggered response in adaptive polymers by two-dimensional Raman micro-correlation-spectroscopy

opencc-zeroJun 2023View details →

Temporal correlations among demographic parameters are ubiquitous but highly variable across species

Temporal correlations among demographic parameters can strongly influence population dynamics. Our empirical knowledge, however, is very limited regarding the direction and the magnitude of these correlations and how they vary among demographic parameters and species' life-histories. Here, we use long-term demographic data from 15 bird and mammal species with contrasting pace of life to quantify correlation patterns among five key demographic parameters: juvenile and adult survival, reproductive probability, reproductive success and productivity. Correlations among demographic parameters were ubiquitous, more frequently positive than negative, but strongly differed across species. Correlations did not markedly change along the slow-fast continuum of life-histories, suggesting that they were more strongly driven by ecological than evolutionary factors. As positive temporal demographic correlations decrease the mean of the long-run population growth rate, the common practice of ignoring temporal correlations in population models could lead to the underestimation of extinction risks in most species.

opencc-zeroJun 2023View details →

Data from: Environmental correlates of molluscan predator-prey body size in the northern Gulf of Mexico

The Mississippi River watershed drains 40% of the continental United States, and the tremendous primary productivity in the adjacent north-central Gulf of Mexico has created one of the most extensive dead zones on Earth. In contrast, smaller watersheds deliver less nutrients to the northeastern Gulf, and consequently productivity is limited and hypoxia is uncommon. How has variation in primary productivity, oxygen availability, and sea surface temperature, affected coastal food webs? Here, we investigate environmental controls on the size of molluscan predators and prey in the northern Gulf of Mexico using Holocene death assemblages. Linear mixed models indicate that bivalve size and the frequency of drilling predation are affected by dissolved oxygen concentrations; drilling frequency declines with declining oxygen, whereas bivalve size increases. In contrast, sea surface temperature is positively associated with the size of molluscan predators and prey. Net primary productivity contributes relatively little to predator or prey size, and predator-to-prey size ratios do not vary consistently with environmental conditions across the northern Gulf. Larger bivalves in areas of oxygen limitation may be due to decreased predation pressure and consequently greater prey longevity. The larger size of bivalves and predatory gastropods in warmer waters may reflect enhanced growth under these conditions, provided dissolved oxygen concentrations exceed a minimum threshold. Holocene death assemblages can be used to test long-standing hypotheses regarding environmental controls on predator-prey body size distributions through geologic time and provide baselines for assessing the ongoing effects of anthropogenic eutrophication and warming on coastal food webs.

opencc-zeroJun 2023View details →

Ecological correlates of cranial evolution in the megaradiation of dipsadine snakes

Background Dipsadine snakes represent one of the most spectacular vertebrate radiations that have occurred in any continental setting, with over 800 species in South and Central America. Their species richness is paralleled by stunning ecological diversity, ranging from arboreal snail-eating and aquatic eel-eating specialists to terrestrial generalists. Despite the ecological importance of this clade, little is known about the extent to which ecological specialization shapes broader patterns of phenotypic diversity within the group. Here, we test how habitat use and diet have influenced morphological diversification in skull shape across 160 dipsadine species using micro-CT and 3-D geometric morphometrics, and we use a phylogenetic comparative approach to test the contributions of habitat use and diet composition to variation in skull shape among species. Results We demonstrate that while both habitat use and diet are significant predictors of shape in many regions of the skull, habitat use significantly predicts shape in a greater number of skull regions when compared to diet. We also find that across ecological groupings, fossorial and aquatic behaviors result in the strongest deviations in morphospace for several skull regions. We use simulations to address the robustness of our results and describe statistical anomalies that can arise from the application of phylogenetic generalized least squares to complex shape data. Conclusions Both habitat and dietary ecology are significantly correlated with skull shape in dipsadines; the strongest relationships involved skull shape in snakes with aquatic and fossorial lifestyles. This association between skull morphology and multiple ecological axes is consistent with a classic model of adaptive radiation and suggests that ecological factors were an important component in driving morphological diversification in the dipsadine megaradiation.

opencc-by-4.0Apr 2023View details →

Migration of mechanical perturbations estimated by seismic coda wave interferometry during the 2018 pre-eruptive period at Kīlauea volcano, Hawaii : Noise Cross-correlation Functions, Seismic catalog, and GNSS data

ARCHIVE_NCFs_KILAUEA_2018.zip : Compress folder with (1) the daily noise cross-correlation functions (in MSEED format) of the station pairs used in the paper and (2) the one hour noise cross-correlation functions (in H5 format) of the station pairs used in the figure 9 of the paper. Code_Data_HVO.ipynb : Code to download the seismic data, available on IRIS, used in this paper. GPS_data_AHUP.zip : Compress folder with the daily GPS data of the station AHUP used in the paper [Year, Month, Day, Day_of_the_year, Second_of_the_day, East_comp(mm), North_comp(mm), Vertical_comp(mm), Sig_East_comp, Sig_North_comp, Sig_Vertical_comp]. Radial_tilt_UWD.txt : Daily radial tilt measurement of the tiltmeter UWD [Year, Month, Day, Radial_tilt(µrad)]. Seismic_stations_Kilauea.txt : Name code and location of the seismic stations used in the paper [Station_code, Longitude, Latitude]. Seismicity_Catalog_Kilauea_2018_USGS.txt : Seismic catalog from USGS used in the paper [Date_Time, Latitude, Longitude, Depth, Magnitude].

Evolutionary Mismatch and depression: the correlation between omega-3 fatty acid intake from fish in diet and the prevalence of depression among university students

This compressed file contain the data used for the Bachelor project "Evolutionary Mismatch and depression: the correlation between omega-3 fatty acid intake from fish in diet and the prevalence of depression among university students" by Ziyao Zhang from the University of Amsterdam.

opencc-zeroJul 2023View details →

Tree biomass does not correlate with soil carbon stocks in forest-tundra ecotones along a 1100 km latitudinal gradient in Norway

Due to climate warming, forests are expanding to higher elevations and latitudes at the expense of tundra vegetation. While the subsequent increase in aboveground biomass is well-documented, there is much speculation regarding the effects on soil organic carbon (SOC) stocks. To provide insight into the consequences of tree encroachment into treeless tundra, we sampled SOC stocks across 36 forest-tundra ecotones along a 1100 km latitudinal gradient in Norway. Our results show that SOC stocks vary greatly within, as well as among treeline ecotones, and that SOC stocks do not correlate with tree biomass and tree species. SOC stocks do increase with temperature, and vary with slope steepness, slope aspect, and soil parent material. Applying a 'space-for-time substitution' perspective, our findings suggest that tree encroachment into tundra is unlikely to have immediate consequences for SOC stocks.

Correlates of Political Correctness and Woke Ideology (data and codebook)

This study explored the different correlates of political correctness and Woke philosophy. Based on the existing literature, a new 42-item questionnaire called Controversial Beliefs was created and completed by 400 adults. Participants were invited to write comments on each item and had the option to not respond (Prefer not to say). Various factors were devised based on a content analysis conducted around the themes of Race, Gender and Feminism, which had acceptable internal reliability.

opencc-by-4.0Mar 2023View details →

Table 3 for the Study: "Correlation Study: Triggering and Magnitude of Earthquakes in Italy (≥M4.3) in Relation to the Positions and Gravitational Forces of the Sun, Moon, and Planets Relative to Earth."

Study of σFR gravitational forces exerted by the angular distances of all 9 S.S. celestial bodies toward Earth: L'Aquila Earthquake, 06.04.2009, Italy, M6.1 for Section 2.9.In case of display problems or missing data, the file <a href="https://zenodo.org/api/files/30eebe1b-d3a1-406a-975c-23ad8abe143f/Table_3_FR_L'Aquila_2009.xlsx?versionId=c778a73b-60c6-4504-a178-8edb1471e98c">Table_3_FR_L'Aquila_2009.xlsx</a> on Drive available for consultation is this one: <a href="https://docs.google.com/spreadsheets/d/1QteARypgGETNkbLB_O0VNWfmrYa0saFWvX0z1EacDEo/edit?usp=sharing">https://docs.google.com/spreadsheets/d/1QteARypgGETNkbLB_O0VNWfmrYa0saFWvX0z1EacDEo/edit?usp=sharing</a>The URL of the Calculation Excel sheet that allows calculation of the resulting gravitational force σFR in the month of the 2009 L'Aquila earthquake, M6.1 (n. 85), in the study "Correlation Study: Triggering and Magnitude of Earthquakes in Italy (≥M4.3) in Relation to the Positions and Gravitational Forces of the Sun, Moon, and Planets Relative to Earth", Section 2.9.

Table 8 for the Study: "Observation of correlation between earthquake triggering of M>4.3 and specific Sun-Moon-Planets positions in the Solar System, from 1600 in Italy."

Graphs of the data distributions, with the R2 regression values of σFR and the corresponding functions. In case of display problems or missing data, the file <a href="https://zenodo.org/api/files/305630bd-70bd-4347-99eb-bfd6fb3af726/Table%208_SFR_distributions_curves.xlsx">Table 8_SFR_distributions_curves.xlsx</a> on Drive available for consultation is this one: <a href="https://docs.google.com/spreadsheets/d/17UGHZlvZ2N-g6TOzgqpIe248_IitBmka8DbGWsv-lKQ/edit?usp=sharing">https://docs.google.com/spreadsheets/d/17UGHZlvZ2N-g6TOzgqpIe248_IitBmka8DbGWsv-lKQ/edit?usp=sharing</a>  URL of the plots of the data distributions, with the R2 regression values of σFR at the time of the triggering of the 200 earthquakes analyzed in the paper "Observation of correlation between earthquake triggering of M>4.3 and specific Sun-Moon-Planets positions in the Solar System, from 1600 in Italy", paragraph 3.02.

opencc-zeroJul 2023View details →

Reduced parental brood visit rate in wild zebra finches (Taeniopygia castanotis) is correlated with high maximum daily ambient temperature

As a result of a warming global climate, understanding how organisms adjust their behaviour to environmental thermal conditions has become an increasingly important question in animal biology. Temperature-driven adjustments in parental care are potentially important given the repercussions on offspring size, quality and survival. In 2015 and 2016, we monitored 83 zebra finch (Taeniopygia castanotis) breeding attempts in the wild. We recorded the frequency of parental visits to the nest together with mean maximum ambient temperature experienced between day 7 and 14 of the nestling period. We found that for each increase of 1 °C in the daytime temperature there was a 0.91% reduction in the hourly rate of parental visits, whilst also accounting for other variables such as nestling age, time of season, and wind speed. Our data suggest that nestlings may receive less food under thermally challenging conditions, which is consistent with recent studies that demonstrate offspring are smaller when reared during periods of high temperature. Understanding the behavioural drivers that may contribute to the production of smaller offspring in extreme heat conditions could prove useful to forecast long-term consequences for fitness triggered by climate change.

A case study for measuring the relativistic dipole of a galaxy cross-correlation with the Dark Energy Spectroscopic Instrument: Data Repository

This repository contains the synthetic catalogue for the DESI Bright Galaxy Survey produced wit the N-body code gevolution, which is analysed in the manuscript "<a href="https://arxiv.org/abs/2306.04213">A case study for measuring the relativistic dipole of a galaxy cross-correlation with the Dark Energy Spectroscopic Instrument</a>", as well as the raw data of the analysis results. The catalogue "catalogue.csv.bz2" is in the CSV format and can be directly read using the pandas library of python, for example. The columns in the catalogue contain the following information: 0. Column index 1. Comoving coordinate x (in units of Mpc/h) 2. Comoving coordinate y (in units of Mpc/h) 3. Comoving coordinate z (in units of Mpc/h) 4. Observed redshift 5. Cosine of the observed polar angle measured with respect to the axis pointing in the direction (1,1,1) along the box diagonal (the original comoving coordinate system has been rotated with an intrinsic z-y-z Euler rotation, first rotating along the z-axis with \(\phi_1 = \pi/4\), then rotating along the new y axis with \(\theta_2 = \mathrm{arccos}(1/\sqrt{3})\) and setting the final rotation angle to zero, \(\phi_3 = 0\); hence to get the unperturbed mu and phi coordinates, one needs to rotate the comoving x, y and z coordinates with the corresponding inverse Euler rotation matrix) 6. Observed azimuthal angle phi measured with respect to axis pointing in the direction (1,1,1) along the box diagonal (the original comoving coordinate system has been rotated with an intrinsic z-y-z Euler rotation, first rotating along the z-axis with \(\phi_1 = \pi/4\), then rotating along the new y axis with \(\theta_2 = \mathrm{arccos}(1/\sqrt{3})\) and setting the final rotation angle to zero, \(\phi_3 = 0\); hence to get the unperturbed mu and phi coordinates, one needs to rotate the comoving x, y and z coordinates with the corresponding inverse Euler rotation matrix) 7. Logarithm of the luminosity in units of solar luminosity \(L_\odot\) 8. Observed flux (in units of \(L_\odot/\mathrm{Mpc}^2\)) 9. Number of particles in each object, plus a uniform noise between 0 and 1. This quantity is the proxy of the mass that was used to assign luminosity to the objects. 10. Flag that identifies the selected objects within each redshift bin. The Flag is 0 for objects not included in the catalogue, and equal to the mean redshift of the bins \(\bar{z} = 0.25, 0.35, 0.45\) for the selected objects.  11. Flag that identifies the bright and faint objects for case 1 (50% bright, 50% faint, no flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects. 12. Flag that identifies the bright and faint objects for case 2 (90% bright, 10% faint, no flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects. 13. Flag that identifies the bright and faint objects for case 3 (50% bright, 50% faint, with flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects. 14. Flag that identifies the bright and faint objects for case 4 (90% bright, 10% faint, with flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects. The example script "example-script.ipynb" demonstrates how to query the catalogue to extract e.g. the redshift distribution of the objects for the different cases considered in Table 4 of the manuscript. Additionally, the measured dipole data vectors with the jackknife covariance matrices (\(\mathrm{cov}^\mathrm{JK}_{ij}\)), as well as the theoretical data vectors with the theoretical measurement covariance (\(\mathrm{cov}^\mathrm{th}_{ij}\)) and the theoretical prediction covariance (\(\mathrm{cov}^\mathrm{pred}_{ij}\)) are provided within this repository: <ul> <li>In the measurements.tar.gz archive, the measured data for the flux-limited case can be found in the /flux-limit subdirectory, while the data for the case without flux-limit is in /no-flux-limit. The data vectors are named "dipole_<redshift bin>_<% of bright galaxies>.txt. The first column in each of those files is the separation bin \(d\) in \(\mathrm{Mpc}/h\), the second column is the mean two-point correlation function dipole of the 100 jackknife subsamples, and the third column is the square root of the diagonal part of the jackknife covariance matrix (\(\mathrm{cov}^\mathrm{JK}_{ij}\)). The corresponding jackknife covariance matrices are named "cov_<redshift bin>_<% of bright galaxies>.txt.</li> <li>In the theory.tar.gz archive, the theoretical predictions are found in /flux-limit for the case with flux limit and in /no-flux-limit for the case without flux limit. The theoretical data vectors are named "dipole_<% of bright galaxies>B_z<redshift bin>_gevol.dat". The first column in each of those files is the separation bin \(d\) in \(\mathrm{Mpc}/h\), the second column the theoretical two-point correlation function dipole and the third column is the square root of the diagonal part of the theoretical prediction covariance matrix (\(\mathrm{cov}^\mathrm{pred}_{ij}\)) . The theoretical measurement covariance matrices are named "covariance_Lp6_<% of bright galaxies>B_z<redshift bin>_gevol.dat", and the theoretical prediction covariance matrices are named "covtheo_<% of bright galaxies>B_z<redshift bin>_gevol.dat". </li> </ul> The example script also demonstrates how to use these data files to reproduce plots of the dipole measurement vs the theoretical prediction like in Figures 6, 7, C1 and C2. The archives need to be unpacked before using the example script to access the data.

opencc-by-4.0Mar 2023View details →

Table 4 for the Study: "Observation of correlation between earthquake triggering of M>4.3 and specific Sun-Moon-Planets positions in the Solar System, from 1600 in Italy."

Video explaining the determination of the Resulting Gravitational Force sigma for the 2009 L'Aquila earthquake. For subtitles in English: <a href="https://youtu.be/-OVk2r8U8QA?t=563">https://youtu.be/-OVk2r8U8QA?t=563</a> --> URL of the video explaining the determination of the Resulting Gravitational Force sigma for the 2009 L'Aquila earthquake, for the article "Correlation observation between the triggering of M>4.3 earthquakes and specific Sun-Moon-Planet positions in the Solar System since 1600 in Italy" , SUBTITLES IN EN, paragraph 2.16.

Psycho-physio-neurological correlates of qualitative attention, emotion and flow experiences in a close-to-real-life extreme sports situation: low- and high-altitude slackline walking

It has been indicated that extreme sport activities, despite providing fear, stress and anxiety, also result in a highly rewarding experience. Studies have related this experience to the concept of flow, a positive feeling that individuals undergo when they are completely immersed in an activity. However, little is known about the exact nature of these experiences, and, there are still no empirical results to characterize the brain dynamics during extreme sport practice. This work aimed at investigating changes in psychological responses while recording physiological (heart rate – HR, and breathing rate – BR) and neural (electroencephalographic – EEG) data of eight volunteers, during outdoors slackline walking in a mountainous environment at two different altitude conditions (1 m – low-walk – and 45 m – high-walk – from the ground). Low-walk showed a higher score on flow scale, while high-walk displayed a higher score in the negative affect aspects, which together point to some level of flow restriction during high-walk. The order of task performance was shown to be relevant for the physiological and neural variables. The brain behavior during flow, mainly considering attention networks, displayed the stimulus-driven ventral attention network – VAN, regionally prevailing (mainly at the frontal lobe), over the goal-directed dorsal attention network – DAN. Therefore, we suggest an interpretation of flow experiences as an opened attention to more changing details in the surroundings, i.e., configured as a ‘task-constantly-opened-to-subtle-information experience’, rather than a ‘task-focused experience’.

Psycho-physio-neurological correlates of qualitative attention, emotion and flow experiences in a close-to-real-life extreme sports situation: low- and high-altitude slackline walking

Real-space correlation functions of nearest-neighbour spin ice

Real-space correlation functions \(\langle \sigma_i\sigma_j\rangle\) of the nearest-neighbour spin ice Hamiltonian \(H = J \sum_{\langle ij\rangle} \sigma_i\sigma_j\) on the pyrochlore lattice, from Monte Carlo simulations on 128×128×128 cubic unit cells. File formats The file name indicates the temperature (in units of J) where the measurement was taken. Files with extension .dat contain the correlation functions as double-precision real floats. Files with extension .err contain the standard errors as single-precision real floats. Both file types contain an array of shape (4, 4, 4, 128, 128, 128). The meaning of the indices (from major to minor): <ul> <li>sublattice index μ</li> <li>sublattice index ν</li> <li>index λ of FCC lattice point in the cubic unit cell</li> <li>offset of cubic lattice cells</li> </ul> Entry [μ, ν, λ, x, y, z] corresponds to the correlator \(\langle \sigma(\vec R + \vec r^\mathrm{FCC}_\lambda + \vec r_\mu)\sigma(\vec r_\nu)\rangle\), where \(\vec R = [xyz] \), the FCC index λ corresponds to the lattice points \(\vec r^\mathrm{FCC}_0 = [000], \vec r^\mathrm{FCC}_1=[011]/2, \vec r^\mathrm{FCC}_2=[101]/2,\vec r^\mathrm{FCC}_3 = [110]/2\), and the sublattice  indices correspond to the site offsets \(r_0 = [111]/8, r_1=[1\bar1\bar1]/8, r_2=[\bar11\bar1]/8,r_3=[\bar1\bar1 1]/8\) from the nearest FCC lattice point. Coordinates x,y,z run between 0 and 127 in periodic boundary conditions. The utility loader.py loads the files, shapes them in the correct array format, and extracts individual correlators. Details of data generation We performed Monte Carlo simulations of nearest-neighbour spin ice using the efficient loop-string algorithm introduced in <a href="https://doi.org/10.1103/PhysRevB.90.220406">Phys. Rev. B 90, 220406(R)</a>. For nonzero temperatures, correlations between different strings were excluded, which effectively averages all spin configurations compatible with a given loop graph. We ran 32 independent Markov chains at each temperature point and obtained 4096 Monte Carlo samples in each. The reported standard errors are the error on the mean of the 32 Markov chains.

Reciprocal-space correlation functions of nearest-neighbour spin ice

Reciprocal-space correlation functions \(\langle \sigma_\mu(k)\sigma_\nu(-k)\rangle\) of the nearest-neighbour spin ice Hamiltonian \(H = J \sum_{\langle ij\rangle} \sigma_i\sigma_j\) on the pyrochlore lattice, from Monte Carlo simulations on 128×128×128 cubic unit cells. File formats The file name indicates the temperature (in units of J) where the measurement was taken. Files with extension .dat contain the correlation functions as double-precision complex floats. Files with extension .err contain the standard errors as single-precision real floats. Both file types contain an array of shape (4, 4, 128, 256, 256). The meaning of the indices (from major to minor): <ul> <li>sublattice index μ</li> <li>sublattice index ν</li> <li>wave vector components in units of \(2\pi/(128a_0)\)</li> </ul> The sublattice indices correspond to the site offsets \(r_0 = [111]/8, r_1=[1\bar1\bar1]/8, r_2=[\bar11\bar1]/8,r_3=[\bar1\bar1 1]/8\) from the nearest FCC lattice point. For the Fourier transforms, all sublattices are shifted to these lattice points, so the correlators stored in the files are periodic with respect to the FCC reciprocal lattice. The wave vector range covered is \(0\le k_x< 2\pi/a_0, 0\le k_y,k_z< 4\pi/a_0\), an (unconventional) reciprocal-space unit cell of the FCC pyrochlore lattice. The utility loader.py loads the files, shapes them in the correct array format, and extracts single k-points. Details of data generation We performed Monte Carlo simulations of nearest-neighbour spin ice using the efficient loop-string algorithm introduced in <a href="https://doi.org/10.1103/PhysRevB.90.220406">Phys. Rev. B 90, 220406(R)</a>. For nonzero temperatures, correlations between different strings were excluded, which effectively averages all spin configurations compatible with a given loop graph. We ran 32 independent Markov chains at each temperature point and obtained 4096 Monte Carlo samples in each. The reported standard errors are the error on the mean of the 32 Markov chains.