cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23664630.v3
title: <strong>Individual variation in habitat selection and movement of feral cats in agroecosystems</strong>

description: Data files 
Cat_data_NZTM.csv GPS data for each of the Cats (labeled by id)
Catdata.csv Home range sizes for each cat, Cat = cat id, sex is male or female, weight is in kg, 95KDE is the 95% kernal density estimate, 50KDE is the 50% kernel density estimate, rabbit level was low, medium, high based on
Modified Mclean's scale
run1.land.csv Landscape level characteristics calculated by fragstats
run2.class.csv Class level characteristics calculated by fragstats
<br>
Code - all code is for R
<br>
Home range.R Code calculates home range and compares 95% KDE for males and females and 50% KDE for males and females Code used to calculate home range requires file CleanGPSdata Code used to compare home range size between males and females uses data Catdata.csv
<br>
Factors that affect habitat selection.R Requires data from Simple_landuse file and Cat_data_NZTM.csv for habitat selection Calulates habitat selection for all cats and for each individual cat Following calculating habitat selection the coeffiecients are used from the individual models with Catdata.csv, run1.land.csv and run2.class.csv to examine factors that affect habitat selection
<br>
All cats model validation.R Provides code for model validation of habitat selection models for all cats
<br>
Individual cat model validation.R Provides code for model validation of habitat selection models for each of the individual cats
<br>
Tortuosity veg vs pasture.R Provides code in caluculating tortuosities for paths in pasture and woody vegetation and code to determine if there was a difference in these
<br>
Step lengths veg vs pasture.R Provides code to determine if there was a difference between pasture and woody vegetation
DOI: 10.18112/openneuro.ds004798.v1.0.5
title: fMRI data for: Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients
DOI: 10.6084/m9.figshare.12271292.v24
title: Arrival catalog of coronal mass ejections observed with STEREO Heliospheric Imagers (HELCATS ARRCAT 2.0)

description: <b>ARRIVAL CATALOGUE 2.0</b>In this ARRival CATalog (ARRCAT), the arrivals of solar coronal mass ejections that were tracked with the STEREO heliospheric imagers are calculated.ARRCAT lists modeled arrivals of CMEs at various spacecraft and planets, between April 2007 - Septermber 2023, based on the HIGeoCAT catalog of CMEs established at RAL Space, UK (R. A. Harrison, J. A. Davies, D. Barnes).<b>Authors:</b><b> </b>C. Möstl (1), E. Weiler (1), D. Barnes (2), R. A. Harrison (2), J. A. Davies (2).<br>(1) Austrian Space Weather Office, GeoSphere Austria, Graz, Austria; (2) RAL Space, Didcot, UK.<br>We acknowledge the use of the astrospice and spiceypy python packages.This is version 2.0, released 2020-May-13, updated 2023-October-18.https://doi.org/10.6084/m9.figshare.12271292It is based on this HIGeoCAT version:https://www.helcats-fp7.eu/catalogues/data/HCME_WP3_V06.votusing the SSEF30 model.The catalog is available as python pandas dataframe (pickle),python numpy arrays (pickle, as recarray and structured array),npy (numpy, no pickle), json, csv, xlsx, txt, hdf5, html athttps://helioforecast.space/arrcathttps://www.helcats-fp7.euNumber of events in ARRCAT: 2112Targets: Earth_L1, STEREO-A, STEREO-B, Solar Orbiter, Parker Solar Probe (PSP), Bepi Colombo, Venus, Mercury, Mars.<br>For further description, see header file.source code: https://github.com/cmoestl/heliocats/blob/master/arrcat.ipynbRules: If results are produced with this catalog for peer-reviewed scientific publications,please contact chris.moestl@outlook.com for co-authorships.<br>
DOI: 10.6084/m9.figshare.25037045
title: Movie annotations for: Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients

description: Movie annotations for the manuscript: "Multimodal brain responses during movie watching: single-neuron, intracranial EEG, and fMRI in human patients"Authors: Umit Keles, Julien Dubois, Kevin J. M. Le, J. Michael Tyszka, David A. Kahn, Chrystal M. Reed, Jeffrey M. Chung, Adam N. Mamelak, Ralph Adolphs, Ueli RutishauserAbstract: We present a multimodal dataset of intracranial recordings, fMRI, and eye tracking in 20 participants during movie watching. Recordings consist of single neurons, local field potential, and intracranial EEG activity acquired from depth electrodes targeting the amygdala, hippocampus, and medial frontal cortex implanted for monitoring of epileptic seizures. Participants watched an 8-min long excerpt from the video "Bang! You're Dead" and performed a recognition memory test for movie content. 3 T fMRI activity was recorded prior to surgery in 11 of these participants while performing the same task. This NWB- and BIDS-formatted dataset includes spike times, field potential activity, behavior, eye tracking, electrode locations, demographics, and functional and structural MRI scans. For technical validation, we provide signal quality metrics, assess eye tracking quality, behavior, the tuning of cells and high-frequency broadband power field potentials to familiarity and event boundaries, and show brain-wide inter-subject correlations for fMRI. This dataset will facilitate the investigation of brain activity during movie watching, recognition memory, and the neural basis of the fMRI-BOLD signal.This dataset accompanies the following data descriptor: Keles, U., Dubois, J., Le, K.J.M., Tyszka, J.M., Kahn, D.A., Reed, C.M., Chung, J.M., Mamelak, A.N., Adolphs, R. and Rutishauser, U. Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients. Sci Data 11, 214 (2024). https://doi.org/10.1038/s41597-024-03029-1Related code: https://github.com/rutishauserlab/bmovie-release-NWB-BIDSIntracranial recording data: https://dandiarchive.org/dandiset/000623fMRI data: https://openneuro.org/datasets/ds004798/<br>
DOI: 10.6084/m9.figshare.25037045.v3
title: Movie annotations for: Multimodal brain responses during movie watching: single-neuron, intracranial EEG, and fMRI in human patients

description: Movie annotations for the manuscript: "Multimodal brain responses during movie watching: single-neuron, intracranial EEG, and fMRI in human patients"Authors: Umit Keles, Julien Dubois, Kevin J. M. Le, J. Michael Tyszka, David A. Kahn, Chrystal M. Reed, Jeffrey M. Chung, Adam N. Mamelak, Ralph Adolphs, Ueli RutishauserAbstract: We present a multimodal dataset of intracranial recordings, fMRI, and eye tracking in 20 human participants as they watched the same movie stimulus. Intracranial recordings consist of single neurons, local field potential, and intracranial EEG activity recorded concurrently from depth electrodes targeting the amygdala, hippocampus, and medial frontal cortex while participants underwent intracranial monitoring for localization of epileptic seizures.Participants watched an 8-min long excerpt from the video “Bang! You’re Dead” and performed a recognition memory test for movie content. 3T fMRI activity was recorded prior to surgery in 11 of these participants while performing the same task. This NWB- and BIDS-formatted dataset includes the spike times of all neurons, field potential activity, behavior, eye tracking, electrode locations, demographics, and functional and structural MRI scans. For technical validation, we provide signal quality metrics, assess eye tracking quality, behavior, the tuning of cells and high-frequency broadband power field potentials to familiarity and event boundaries, and show brain-wide inter-subject correlations for fMRI.This dataset will facilitate the investigation of brain activity during movie watching, recognition memory, and the neural basis of the fMRI-BOLD signal.Related code: https://github.com/rutishauserlab/bmovie-release-NWB-BIDS Intracranial recording data: https://dandiarchive.org/dandiset/000623fMRI data: https://openneuro.org/datasets/ds004798/<br>
DOI: 10.13140/rg.2.2.26416.74240
title: chameleon case study | Bio mimicry approach| special topics | fall 2021|all rights reserved to ARCH. department library| architectural department and interior design | sinai university.
DOI: 10.5281/zenodo.10719162
title: Stack Overflow Data Dump 2012-08
DOI: 10.5281/zenodo.10719163
title: Stack Overflow Data Dump 2012-08
DOI: 10.5281/zenodo.4556359
title: Bayesian evidence for the tensor-to-scalar ratio r and neutrino masses m_nu: Effects of uniform vs logarithmic priors (supplementary inference products)

description: These are the nested sampling inference products and input files that were used to compute results for arXiv:2102.11511.

Example plotting scripts (as .ipynb or as .html files) and figures from the papers are included to demonstrate usage.

 

Filename conventions:



lcdm: Concordance cosmological model called \(\Lambda\mathrm{CDM}\) (without extension this assumes \(r=0\) and a single massive neutrino with mass \(m_\nu=0.06\,\mathrm{eV}\)).

_r: \(\Lambda\mathrm{CDM}\) with variable tensor-to-scalar ratio \(r\).

_nu: \(\Lambda\mathrm{CDM}\) with three massive neutrinos, sampling over the lightest neutrino mass \(m_\mathrm{light}\) and the squared mass splittings \(\delta m^2\) and \(\Delta m^2\).

mcmc: Cobaya's Markov Chain Monte Carlo Metropolis sampler.https://github.com/CobayaSampler/cobaya/releases/tag/v3.0.2

pc#d###: PolyChord run with #d repeats per parameter block (where d is the number of parameters in that block) and with ### live points.https://github.com/PolyChord/PolyChordLite/releases/tag/1.17.1

_class: theory code CLASS.https://github.com/lesgourg/class_public/releases/tag/v2.9.4

_p18_TTTEEElowTE_SZ: Planck 2018 TT,TE,EE+lowl+lowE data.https://pla.esac.esa.int/pla/#cosmology

_nufit50: NuFIT 5.0 data.http://www.nu-fit.org/?q=node/228

_NH and _IH: normal and inverted neutrino hierarchy.

_logr##: logarithmic sampling of tensor-to-scalar ratio \(r\) with lower log bound given .by log10r=-##.

_mdD: sampling over the lightest neutrino mass \(m_\mathrm{light}\) and the squared mass splittings \(\delta m^2\) and \(\Delta m^2\) (medium and heavy neutrino mass are derived parameters) with mass units in eV.

_logmdD##: logarithmic (instead of uniform) sampling of the lightest neutrino mass \(m_\mathrm{light}\) with lower log bound given by log10mlight=-##.


 

Datasets used for the nested sampling runs:



Planck 2018 TT,TE,EE+lowl+lowE: https://pla.esac.esa.int/pla/#cosmology

NuFIT 5.0: http://www.nu-fit.org/?q=node/228


 

Software used:



Cobaya: https://github.com/CobayaSampler/cobaya/releases/tag/v3.0.2

CLASS: https://github.com/lesgourg/class_public/releases/tag/v2.9.4

PolyChord: https://github.com/PolyChord/PolyChordLite/releases/tag/1.17.1

Anesthetic: https://github.com/lukashergt/anesthetic/tree/138299739544e888cc318746be087c898f1aff15


 

For more details see Cobaya's (https://cobaya.readthedocs.io/en/latest/index.html) and Anesthetic's (https://anesthetic.readthedocs.io/en/latest/) documentation.
DOI: 10.5281/zenodo.4556360
title: Bayesian evidence for the tensor-to-scalar ratio r and neutrino masses m_nu: Effects of uniform vs logarithmic priors (supplementary inference products)

description: These are the nested sampling inference products and input files that were used to compute results for arXiv:2102.11511.

Example plotting scripts (as .ipynb or as .html files) and figures from the papers are included to demonstrate usage.

 

Filename conventions:



lcdm: Concordance cosmological model called \(\Lambda\mathrm{CDM}\) (without extension this assumes \(r=0\) and a single massive neutrino with mass \(m_\nu=0.06\,\mathrm{eV}\)).

_r: \(\Lambda\mathrm{CDM}\) with variable tensor-to-scalar ratio \(r\).

_nu: \(\Lambda\mathrm{CDM}\) with three massive neutrinos, sampling over the lightest neutrino mass \(m_\mathrm{light}\) and the squared mass splittings \(\delta m^2\) and \(\Delta m^2\).

mcmc: Cobaya's Markov Chain Monte Carlo Metropolis sampler.https://github.com/CobayaSampler/cobaya/releases/tag/v3.0.2

pc#d###: PolyChord run with #d repeats per parameter block (where d is the number of parameters in that block) and with ### live points.https://github.com/PolyChord/PolyChordLite/releases/tag/1.17.1

_class: theory code CLASS.https://github.com/lesgourg/class_public/releases/tag/v2.9.4

_p18_TTTEEElowTE_SZ: Planck 2018 TT,TE,EE+lowl+lowE data.https://pla.esac.esa.int/pla/#cosmology

_nufit50: NuFIT 5.0 data.http://www.nu-fit.org/?q=node/228

_NH and _IH: normal and inverted neutrino hierarchy.

_logr##: logarithmic sampling of tensor-to-scalar ratio \(r\) with lower log bound given .by log10r=-##.

_mdD: sampling over the lightest neutrino mass \(m_\mathrm{light}\) and the squared mass splittings \(\delta m^2\) and \(\Delta m^2\) (medium and heavy neutrino mass are derived parameters) with mass units in eV.

_logmdD##: logarithmic (instead of uniform) sampling of the lightest neutrino mass \(m_\mathrm{light}\) with lower log bound given by log10mlight=-##.


 

Datasets used for the nested sampling runs:



Planck 2018 TT,TE,EE+lowl+lowE: https://pla.esac.esa.int/pla/#cosmology

NuFIT 5.0: http://www.nu-fit.org/?q=node/228


 

Software used:



Cobaya: https://github.com/CobayaSampler/cobaya/releases/tag/v3.0.2

CLASS: https://github.com/lesgourg/class_public/releases/tag/v2.9.4

PolyChord: https://github.com/PolyChord/PolyChordLite/releases/tag/1.17.1

Anesthetic: https://github.com/lukashergt/anesthetic/tree/138299739544e888cc318746be087c898f1aff15


 

For more details see Cobaya's (https://cobaya.readthedocs.io/en/latest/index.html) and Anesthetic's (https://anesthetic.readthedocs.io/en/latest/) documentation.
DOI: 10.25345/c5cc0v477
title: MassIVE MSV000094186 - Oligonucleotide-mediated proximity-interactome mapping (O-MAP): A unified method for discovering RNA-interacting proteins, transcripts and genomic loci in situ.
title: Within all cells, RNA molecules form complex networks of molecular interactions that are central to their function, but discovering these interactions remains challenging. Here, we introduce Oligonucleotide-mediated proximity-interactome MAPping (O-MAP), a straightforward method for elucidating the biomolecules near an RNA of interest, within its native cellular context. O-MAP uses programmable DNA probes to deliver proximity-biotinylating enzymes to a target RNA, enabling molecules within that RNA's subcellular microcompartment to be enriched by streptavidin pulldown. O-MAP induces exceptionally precise in situ biotinylation, and unlike alternative methods it enables straightforward optimization of its RNA-targeting accuracy. Using the 47S pre-ribosomal RNA and long noncoding RNA Xist as models, we develop O-MAP workflows for unbiased discovery of RNA-proximal proteins, transcripts, and genomic loci. This revealed unexpected co-compartmentalization of Xist and other chromatin-regulatory RNAs, and enabled systematic discovery of nucleolar-chromatin interactions across multiple cell lines. O-MAP uses exclusively off-the-shelf parts requiring no genetic- or cell-line engineering and is easily portable across diverse specimen-types and target RNAs. We therefore anticipate its application to a broad array of RNA phenomena.
DOI: 10.5281/zenodo.6547871
title: Inference products for "Finite inflation in curved space"

description: These are the MCMC and nested sampling inference products and input files that were used to compute results for the paper "Finite inflation in cuved space" by L. T. Hergt, F. J. Agocs, W. J. Handley, M. P. Hobson, and A. N. Lasenby from 2022.

Example plotting scripts (as \(\texttt{.ipynb}\) or as \(\texttt{.html}\) files) and figures from the paper are included to demonstrate usage.

 

We used the following python packages for the genertion of MCMC and nested sampling chains:




Package
Version




anesthetic
2.0.0b12


classy
2.9.4


cobaya
3.0.4


GetDist
1.3.3


primpy
2.3.6


pyoscode
1.0.4


pypolychord
1.20.0




 

 

 

 

 

 

 

 

 

 

Filename conventions:



\(\texttt{mcmc}\): MCMC run

\(\texttt{pcs#d####}\): PolyChord run (in synchronous mode) with \(\texttt{#d}\) repeats per parameter block (where \(\texttt{d}\) is the number of parameters in that block) and with \(\texttt{####}\) live points.

\(\texttt{_cl_hf}\): Using Boltzmann theory code CLASS with nonlinearities code halofit.

\(\texttt{_p18}\): Using Planck 2018 CMB data.

\(\texttt{_TTTEEE}\): Using the high-l TTTEEE likelihood.

\(\texttt{_TTTEEElite}\): Using the lite version of the high-l TTTEEE likelihood.

\(\texttt{_lowl_lowE}\): Using the low-l likelihoods for temperature and E-modes.

\(\texttt{_BK15}\): Using data from the 2015 observing season of Bicep2 and the Keck Array.

\(\texttt{lcdm}\): Concordance cosmological model called LCDM (standard 6 cosmological sampling parameters, no tensor perturbations, zero spatial curvature)

\(\texttt{_r}\): Extension with a variable tensor-to-scalar ratio \(r\).

\(\texttt{_omegak}\): Extension with a variable curvature density parameter \(\Omega_K \).

\(\texttt{_H0}\): Sampling over \(H_0\) instead of \(\theta_\mathrm{s}\).

\(\texttt{_omegakh2}\): Extension with a variable curvature density parameter, but sampling over \(H_0\) instead of \(\theta_\mathrm{s}\) and over \(\omega_K\equiv\Omega_Kh^2\) instead of \(\Omega_K \).

\(\texttt{_mn2}\): Using a quadratic monomial potential for the computation of the primordial universe.

\(\texttt{_nat}\): Using the natural inflation potential for the computation of the primordial universe.

\(\texttt{_stb}\): Using the Starobinsky potential for the computation of the primordial universe.

\(\texttt{_AsfoH}\): Using the primordial sampling parameters {`logA_SR`, `N_star`, `f_i`, `omega_K`, `H0`}.

\(\texttt{_perm}\): Assuming a permissive reheating scenario.
DOI: 10.5281/zenodo.6547872
title: Inference products for "Finite inflation in curved space"

description: These are the MCMC and nested sampling inference products and input files that were used to compute results for the paper "Finite inflation in cuved space" by L. T. Hergt, F. J. Agocs, W. J. Handley, M. P. Hobson, and A. N. Lasenby from 2022.

Example plotting scripts (as \(\texttt{.ipynb}\) or as \(\texttt{.html}\) files) and figures from the paper are included to demonstrate usage.

 

We used the following python packages for the genertion of MCMC and nested sampling chains:




Package
Version




anesthetic
2.0.0b12


classy
2.9.4


cobaya
3.0.4


GetDist
1.3.3


primpy
2.3.6


pyoscode
1.0.4


pypolychord
1.20.0




 

 

 

 

 

 

 

 

 

 

Filename conventions:



\(\texttt{mcmc}\): MCMC run

\(\texttt{pcs#d####}\): PolyChord run (in synchronous mode) with \(\texttt{#d}\) repeats per parameter block (where \(\texttt{d}\) is the number of parameters in that block) and with \(\texttt{####}\) live points.

\(\texttt{_cl_hf}\): Using Boltzmann theory code CLASS with nonlinearities code halofit.

\(\texttt{_p18}\): Using Planck 2018 CMB data.

\(\texttt{_TTTEEE}\): Using the high-l TTTEEE likelihood.

\(\texttt{_TTTEEElite}\): Using the lite version of the high-l TTTEEE likelihood.

\(\texttt{_lowl_lowE}\): Using the low-l likelihoods for temperature and E-modes.

\(\texttt{_BK15}\): Using data from the 2015 observing season of Bicep2 and the Keck Array.

\(\texttt{lcdm}\): Concordance cosmological model called LCDM (standard 6 cosmological sampling parameters, no tensor perturbations, zero spatial curvature)

\(\texttt{_r}\): Extension with a variable tensor-to-scalar ratio \(r\).

\(\texttt{_omegak}\): Extension with a variable curvature density parameter \(\Omega_K \).

\(\texttt{_H0}\): Sampling over \(H_0\) instead of \(\theta_\mathrm{s}\).

\(\texttt{_omegakh2}\): Extension with a variable curvature density parameter, but sampling over \(H_0\) instead of \(\theta_\mathrm{s}\) and over \(\omega_K\equiv\Omega_Kh^2\) instead of \(\Omega_K \).

\(\texttt{_mn2}\): Using a quadratic monomial potential for the computation of the primordial universe.

\(\texttt{_nat}\): Using the natural inflation potential for the computation of the primordial universe.

\(\texttt{_stb}\): Using the Starobinsky potential for the computation of the primordial universe.

\(\texttt{_AsfoH}\): Using the primordial sampling parameters {`logA_SR`, `N_star`, `f_i`, `omega_K`, `H0`}.

\(\texttt{_perm}\): Assuming a permissive reheating scenario.
DOI: 10.21227/92af-5q05
title: Interference graph dataset for machine learning based register allocation

description: Register allocation is an important phase in compiler optimization. Often, its resolution involves graph coloring, which is an NP-complete problem. Because of their significance, numerous heuristics have been suggested for their resolution. Heuristic development is a complex process that requires specialized domain expertise. Recently, several machine learning based approaches have been proposed to solve compiler optimization problems. Still, due to the complexity of the problem and the lack of specialized datasets for training models applied to register allocation, few works researched this topic. The deficiency in sufficient adequate test cases is a recurring issue when working with register allocation, even beyond the scope of machine learning applications. In an effort to address this problem and facilitate forthcoming research in this domain, we present this dataset for training Machine Learning models focused on register allocation. The dataset consists of PBQP interference graphs from real C/C++ codes, and it is easily adaptable to various register allocation techniques.
DOI: 10.17909/d72e-de92
title: repeated observation paper
DOI: 10.6084/m9.figshare.25299337
title: Appendix of Master thesis (Exploring <i>cry5</i>: Gene Sequence Identification and Bioinformatic Modeling in Three-Spined Stickleback (<i>Gasterosteus aculeatus</i>) and Fourline Cardinalfish (<i>Ostorhinchus doederleini</i>))

description: The aim of this project was to identify the correct gene sequence of <i>cry5</i> in the migratory three-spined stickleback (<i>Gasterosteus aculeatus</i>) and the fourline cardinalfish (<i>Ostorhinchus doederleini</i>). Additionally, bioinformatic predictions with species-specific parameters were made for comparison and to assess how accurate the gene modeling was.
DOI: 10.5281/zenodo.10396926
title: Climate anomalies and neighbourhood crowding interact in shaping tree growth in old-growth and selectively-logged tropical forests

description: Species mean information for the six leaf water-related traits used in the paper titled: Climate anomalies and neighbourhood crowding interact in shaping tree growth in old-growth and selectively-logged tropical forests.
DOI: 10.5281/zenodo.10396927
title: Climate anomalies and neighbourhood crowding interact in shaping tree growth in old-growth and selectively-logged tropical forests

description: Species mean information for the six leaf water-related traits used in the paper titled: Climate anomalies and neighbourhood crowding interact in shaping tree growth in old-growth and selectively-logged tropical forests.
DOI: 10.6084/m9.figshare.25301200
title: genetic

description: data vcf
DOI: 10.6084/m9.figshare.25301200.v1
title: genetic

description: data vcf