cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14326607.v1
title: Use of the Python Uncertainties Module in the calculation of experimental uncertainties of the potential difference and electric current of an experimental prototype

description: In this article, the Python Uncertainties Module was used in the development of the codes to perform the calculations of experimental uncertainties, both for the potential difference and for the electric currents. For this purpose, an experimental prototype was developed that demonstrates the photovoltaic effect from the use of low-cost materials and, therefore, with the objective of observing and quantifying the referred physical quantities involved. In addition, a qualitative and quantitative analysis of the photovoltaic effect was carried out, as well as of Ohm’s First Law. In this perspective, the prototype was associated with a multimeter, that allowed direct measurement of the potential difference, whose main value was (0,4558 ± 0,0004) V, when a laser beam was focused on a photodiode present in a 2N3055 transistor, but also the values of the electric currents, the main values being (81,7100 ± 0,1118) μA and (1,9990 ± 0,0320) μA, when the ohmic resistors of 1,0 kΩ and 220,0 kΩ were used, respectively. Finally, it is important to note that this article proposal aims at the annexation of physical computing in school routine, as well as promoting scientific awakening in the manufacture of specific instruments for teaching and learning physics.
DOI: 10.6084/m9.figshare.14268556.v1
title: Magnet movement when sliding on an inclined aluminum plane

description: A variation of the well-known drop of a neodymium – iron – boron magnet inside a tube of non-magnetic conductive material is presented in this work; the magnet removed from a movement mechanism of the recording and reading arm of a hard disk is placed to slide through an aluminium ramp placed close to the vertical. This configuration has the great advantage of being able to be seen by a (eventually) large audience; energy transformations are simple enough to be dealt with in medium level school physics courses. The treatment given to this teaching activity, appropriate to the medium level of teaching Physics, or even introductory higher education, involves the exploration of principles related to energy conservation.
DOI: 10.6084/m9.figshare.14326615.v1
title: Physics and mathematics, Noether’s theorem: Context the Complexity of Science Education

description: This article introduces an important theorem for mathematics and physics called Nöther theorem. The historical context that led Nöther to his development will be addressed, exploring certain important aspects in his education and in his life. In the article an analytical approach is made from Bourdieu’s notion of capital, on relevant sociocultural aspects that made possible Nöther’s contributions in mathematics and physics. The objective is to postulate this document as a historical rescue that exalts the importance of the sociocultural context for the development of scientific knowledge. Seeking to propose a work option for the teaching of physics and mathematics, where a humanistic conception of knowledge is promoted in these disciplines.
DOI: 10.6084/m9.figshare.14326615
title: Physics and mathematics, Noether’s theorem: Context the Complexity of Science Education

description: This article introduces an important theorem for mathematics and physics called Nöther theorem. The historical context that led Nöther to his development will be addressed, exploring certain important aspects in his education and in his life. In the article an analytical approach is made from Bourdieu’s notion of capital, on relevant sociocultural aspects that made possible Nöther’s contributions in mathematics and physics. The objective is to postulate this document as a historical rescue that exalts the importance of the sociocultural context for the development of scientific knowledge. Seeking to propose a work option for the teaching of physics and mathematics, where a humanistic conception of knowledge is promoted in these disciplines.
DOI: 10.6084/m9.figshare.14326622
title: The Cat and the Butterfly: quantum properties of chaotic systems

description: In this article I present an introduction to the problem of quantum chaos, i.e. the characterization and treatment of quantum systems whose classical analogue has chaotic dynamics. Two of the main approaches to this problem are discussed: one is statistical, making use of random matrices, while the other is based on the semiclassical approximation. Results obtained by these approaches are mentioned for closed systems (spectral correlation functions), transport (moments like conductance and shot noise) and resonances (fractal Weyl law and fractal wave functions).
DOI: 10.6084/m9.figshare.14326622.v1
title: The Cat and the Butterfly: quantum properties of chaotic systems

description: In this article I present an introduction to the problem of quantum chaos, i.e. the characterization and treatment of quantum systems whose classical analogue has chaotic dynamics. Two of the main approaches to this problem are discussed: one is statistical, making use of random matrices, while the other is based on the semiclassical approximation. Results obtained by these approaches are mentioned for closed systems (spectral correlation functions), transport (moments like conductance and shot noise) and resonances (fractal Weyl law and fractal wave functions).
DOI: 10.6084/m9.figshare.14326624
title: Complex Materials: The Tough Life of Bone

description: Bone was a crucial biological material for the evolution of large terrestrial organisms and is today essential for most of our daily activities and well-being. From an engineering perspective, this living material features highly desirable properties for modern load-bearing structures. It is made of abundant and environmental-friendly building blocks, which are combined into a tough and durable structure that can continuously modify itself to adapt to changes in the mechanical load imposed by the surroundings. In this review article, we compile and discuss scientific findings that allow us to understand bone as a complex system with properties that emerge from cell-mediated interactions of molecules and particles at multiple length scales. Analogous to other complex systems, such interactions lead to self-organization, hierarchical structures and adaptive behavior without the need of a central controlling unit. A rich range of physical, chemical and biological phenomena provide a framework for information to be generated and processed in this complex system. Understanding the interplay between such underlying phenomena and their emerging properties should help the diagnosis and treatment of bone-related medical conditions and might provide guidelines for the future development of more sustainable materials and engineering structures.
DOI: 10.6084/m9.figshare.14326624.v1
title: Complex Materials: The Tough Life of Bone

description: Bone was a crucial biological material for the evolution of large terrestrial organisms and is today essential for most of our daily activities and well-being. From an engineering perspective, this living material features highly desirable properties for modern load-bearing structures. It is made of abundant and environmental-friendly building blocks, which are combined into a tough and durable structure that can continuously modify itself to adapt to changes in the mechanical load imposed by the surroundings. In this review article, we compile and discuss scientific findings that allow us to understand bone as a complex system with properties that emerge from cell-mediated interactions of molecules and particles at multiple length scales. Analogous to other complex systems, such interactions lead to self-organization, hierarchical structures and adaptive behavior without the need of a central controlling unit. A rich range of physical, chemical and biological phenomena provide a framework for information to be generated and processed in this complex system. Understanding the interplay between such underlying phenomena and their emerging properties should help the diagnosis and treatment of bone-related medical conditions and might provide guidelines for the future development of more sustainable materials and engineering structures.
DOI: 10.6084/m9.figshare.14326625
title: Machine Learning in Physics, Chemistry, and Materials Science: Materials Discovery and Design

description: Nowadays, we are witnessing a tremendous increase in data generation enabled by advances in experimental techniques and theoretical and computational developments. This availability of data, associated with new tools and technologies capable of storing and processing that data, culminated in the so-called data science. One of the most prominent areas (machine learning), which aims to identify correlations and patterns in the data sets. These algorithms have been used for decades in different areas. Only recently the community introduced its application for materials, due to the creation, standardization, and consolidation of consistent databases. The use of these methodologies allows to extract knowledge and insights from the huge amount of raw data and information now available. The area presents several opportunities for solving challenges in physics, chemistry, and materials science. Specifically, machine learning methods are a powerful tool for discovering and designing new materials with desired and optimized properties and functionalities. In this article, we present the context of the emergence of machine learning, its foundations, and applications for the discovery and design of materials.
DOI: 10.6084/m9.figshare.14326625.v1
title: Machine Learning in Physics, Chemistry, and Materials Science: Materials Discovery and Design

description: Nowadays, we are witnessing a tremendous increase in data generation enabled by advances in experimental techniques and theoretical and computational developments. This availability of data, associated with new tools and technologies capable of storing and processing that data, culminated in the so-called data science. One of the most prominent areas (machine learning), which aims to identify correlations and patterns in the data sets. These algorithms have been used for decades in different areas. Only recently the community introduced its application for materials, due to the creation, standardization, and consolidation of consistent databases. The use of these methodologies allows to extract knowledge and insights from the huge amount of raw data and information now available. The area presents several opportunities for solving challenges in physics, chemistry, and materials science. Specifically, machine learning methods are a powerful tool for discovering and designing new materials with desired and optimized properties and functionalities. In this article, we present the context of the emergence of machine learning, its foundations, and applications for the discovery and design of materials.
DOI: 10.6084/m9.figshare.14326630.v1
title: Statistical physics applied to the neuroscience of altered states: the brain under the influence of psychedelics

description: The primary aim of statistical mechanics is to relate the micropscopic physics governed by Newton’s laws, or else by quantum mechanics, to the macroscopic physics governed by the laws of thermodynamics. Macroscopic properties, such as temperature, may have no meaning at the microscopic level, but they arise as emergent properties in statistical mechanics when the number of particles becomes sufficiently large. The success of statistical mechanics in explaining and predicting a variety of macroscopic phenomena has motivated the application of concepts and methods of statistical physics to study complex systems and complex networks, including in neuroscience. Our aim here is to apply this approach to a key problem in neuroscience: how can we quantitatively understand the neural correlates of altered mental states induced by psychedelic substances? We first give an overview of basic concepts concerning functional connectivity of brain regions. We then discuss the phenomenology and the neuroscience related to psychedelics. Finally, we review some studies that have applied statistical physics and the theory of complex networks to try to gain a better understanding of the brain under the influence of psychedelics.
DOI: 10.6084/m9.figshare.14326630
title: Statistical physics applied to the neuroscience of altered states: the brain under the influence of psychedelics

description: The primary aim of statistical mechanics is to relate the micropscopic physics governed by Newton’s laws, or else by quantum mechanics, to the macroscopic physics governed by the laws of thermodynamics. Macroscopic properties, such as temperature, may have no meaning at the microscopic level, but they arise as emergent properties in statistical mechanics when the number of particles becomes sufficiently large. The success of statistical mechanics in explaining and predicting a variety of macroscopic phenomena has motivated the application of concepts and methods of statistical physics to study complex systems and complex networks, including in neuroscience. Our aim here is to apply this approach to a key problem in neuroscience: how can we quantitatively understand the neural correlates of altered mental states induced by psychedelic substances? We first give an overview of basic concepts concerning functional connectivity of brain regions. We then discuss the phenomenology and the neuroscience related to psychedelics. Finally, we review some studies that have applied statistical physics and the theory of complex networks to try to gain a better understanding of the brain under the influence of psychedelics.
DOI: 10.6084/m9.figshare.14326631.v1
title: Complex fluids in condensed matter: The liquid crystals

description: Liquid crystals are intermediate states in which matter can present itself, between the crystalline solid and the isotropic liquid. Materials that can present this type of state are made up of molecules, or aggregates of molecules, which present significant shape anisotropy. The liquid crystalline states present complex spatial structures, which pose fundamental questions in the study of the structure of matter. They are characterized by presenting some degree of order: long-range orientational and short-range positional. Its technological applications are diverse, in particular, the electro-optics (displays) with low energy consumption. In this article we present a review of this area, emphasizing lyotropic liquid crystals (formed by mixtures of amphiphilic molecules and solvent) that have a great interface with biological systems. We also discuss the theoretical models that describe phase diagrams of lyotropic nematic structures.
DOI: 10.6084/m9.figshare.14326631
title: Complex fluids in condensed matter: The liquid crystals

description: Liquid crystals are intermediate states in which matter can present itself, between the crystalline solid and the isotropic liquid. Materials that can present this type of state are made up of molecules, or aggregates of molecules, which present significant shape anisotropy. The liquid crystalline states present complex spatial structures, which pose fundamental questions in the study of the structure of matter. They are characterized by presenting some degree of order: long-range orientational and short-range positional. Its technological applications are diverse, in particular, the electro-optics (displays) with low energy consumption. In this article we present a review of this area, emphasizing lyotropic liquid crystals (formed by mixtures of amphiphilic molecules and solvent) that have a great interface with biological systems. We also discuss the theoretical models that describe phase diagrams of lyotropic nematic structures.
DOI: 10.6084/m9.figshare.14326639.v1
title: The statistical physics of turbulence

description: We give a broad overview of the statistical theory of turbulence, carefully basing it on important and consolidated notions of fluid dynamics, before going deeper into the discussion of specific models, prone to contemporary debates. In the so-called structural approach, the complexity of turbulence is perceived as the challenge to understand, from the point of view of vortex tubes dynamics, energy transport from large to small scales in the vanishing viscosity limit. The statistical properties of the energy cascade, such as the intermittency phenomenon, are modelled through seemingly diverse narratives associated to multiplicative stochastic processes or, alternatively, to the multifractal formulation of the singularity spectrum of the turbulent velocity field. The synthesis, the foundation from first principles and the integration of these two modelling strategies with the structural approach build up the essence of current theoretical challenges in turbulence.
DOI: 10.5281/zenodo.13383357
title: Model data used in the study of Arctic atlantification

description: Model data used in the study of Arctic atlantification in the Eurasian Basin.
DOI: 10.5281/zenodo.13383358
title: Model data used in the study of Arctic atlantification

description: Model data used in the study of Arctic atlantification in the Eurasian Basin.
DOI: 10.15468/dl.d93b4s
title: Occurrence Download

description: A dataset containing 556 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Grus japonensis (Statius Muller, 1776)"
 ]
}

 The dataset includes 556 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0106116-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ntzc5c
title: Occurrence Download

description: A dataset containing 1595 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Euthynnus alletteratus (Rafinesque, 1810)",
 "HasCoordinate is true",
 "DecimalLatitude is greater than -90",
 "DecimalLongitude is greater than -180",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1950",
 "Year is less than or equal to 2014"
 ]
}

 The dataset includes 1595 records from 61 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0106117-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jb2qmm
title: Occurrence Download

description: A dataset containing 5251 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Impatiens walleriana Hook.fil."
 ]
}

 The dataset includes 5251 records from 345 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0106118-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rpad73
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation)",
 "Continent is Asia",
 "Country is China",
 "Geometry POLYGON((102 30,114 30,114 36,102 36,102 30))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Sciurotamias davidianus (Milne-Edwards, 1867)"
 ]
}

 The dataset includes 23 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0106119-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.evtu3k
title: Occurrence Download

description: A dataset containing 26757 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mesosphaerum suaveolens (L.) Kuntze"
 ]
}

 The dataset includes 26757 records from 421 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0106120-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w2sfqm
title: Occurrence Download

description: A dataset containing 8434 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ipomoea alba L."
 ]
}

 The dataset includes 8434 records from 404 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0106121-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13383392
title: Test calculation 1

description: This is a test record for one calculation.
DOI: 10.5281/zenodo.13383393
title: Test calculation 1

description: This is a test record for one calculation.
DOI: 10.15468/dl.sakt5b
title: Occurrence Download

description: A dataset containing 10958 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Indigofera suffruticosa Mill."
 ]
}

 The dataset includes 10958 records from 463 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0106122-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.2635655
title: Data set for training a ML model to predict duration of MPI application phases (HPC system) - without previous phases info

description: This is the data used to train a ML model predicting the duration of MPI application phases, in a HPC system. There are 11 different data sets corresponding to different HPC applications. These data sets do not contain information regarding previous MPI calls
DOI: 10.5281/zenodo.2635656
title: Data set for training a ML model to predict duration of MPI application phases (HPC system) - without previous phases info

description: This is the data used to train a ML model predicting the duration of MPI application phases, in a HPC system. There are 11 different data sets corresponding to different HPC applications. These data sets do not contain information regarding previous MPI calls
DOI: 10.5281/zenodo.2635620
title: Data set for training a ML model to predict duration of MPI application phases (HPC system) - with previous phase info

description: This is the data used to train a ML model predicting the duration of MPI application phases, in a HPC system. There are 10 different data sets corresponding to different HPC applications. These data sets contain information regarding the previous MPI call with same ID and type.
DOI: 10.5281/zenodo.2635621
title: Data set for training a ML model to predict duration of MPI application phases (HPC system) - with previous phase info

description: This is the data used to train a ML model predicting the duration of MPI application phases, in a HPC system. There are 10 different data sets corresponding to different HPC applications. These data sets contain information regarding the previous MPI call with same ID and type.
DOI: 10.6084/m9.figshare.22657006
title: Assessing the Knowledge, Attitude and Practice of Osteoporosis among Pakistani Women: a National Social-Media Based Survey

description: Data set of survery respondents, containing demographics and questions