علماء الفلك

علوم الحياة والطبيعة والاجتماع · المهارات والكفاءات الوظيفية · SOC 19-2011 · 430 مهارة

سياق المهنة

تصف مهام O*NET هذه المهنة وتوضح سياق تصنيف SOC، لكنها لا تعني أن كل مهارة مدرجة تنفذ كل مهمة.

Analyze research data to determine its significance, using computers.
Present research findings at scientific conferences and in papers written for scientific journals.
Study celestial phenomena, using a variety of ground-based and space-borne telescopes and scientific instruments.

تُعرض بيانات مهام O*NET بلغتها المصدرية (الإنجليزية). بيانات المهام مقتبسة من O*NET® 29.1 Database التابعة لـ USDOL/ETA، وتُستخدم بموجب CC BY 4.0.

مصادر تمثيلية

عينة محدودة من المهارات الشائعة المرتبطة بهذه المهنة، بمهارة واحدة على الأكثر من كل مستودع GitHub. وتليها القائمة الكاملة للمهارات المرتبطة.

scientific-problem-selection

Evaluate and prioritize research questions by importance, novelty, tractability, evidence, resources, ethics, and falsifiability.

zLanqing/codex-claude-academic-skills

Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.

astrophysics-data-guide

brycewang-stanford/Auto-Empirical-Research-Skills

Astronomical data processing with Astropy, FITS files, and sky surveys

structure-analysis

synthetic-sciences/openscience

Handles protein structure files and geometric criteria so that residue counts, contacts, hydrogen bonds and interfaces are reproducible, covering PDB versus mmCIF, author versus label chains and numbering, HETATM, water and ligand policy, alternate locations, insertion codes, biological assemblies versus the asymmetric unit, hydrogen-bond distance and angle cutoffs, interface definitions by distance or buried surface area, and the Biopython, MDAnalysis, gemmi, PyMOL and ChimeraX calls that implement them. Use whenever a task reads a structure file and reports anything derived from atom coordinates; use biopython for sequence work and molecular-docking or structure-prediction skills for generating structures.

كل المهارات المرتبطة

تصفح دليل الارتباط الكامل حسب شعبية المستودع أو أحدث نشاط في المصدر.

تبقى أسماء المهارات وأوصافها باللغة التي يوفرها مستودع المصدر.

عوامل التصفية (0)

عوامل التصفية

لغة المحتوى

المجال

الفئة

ترتيب حسب

scientific-problem-selection

Evaluate and prioritize research questions by importance, novelty, tractability, evidence, resources, ethics, and falsifiability.

zLanqing/codex-claude-academic-skills

Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.

astrophysics-data-guide

brycewang-stanford/Auto-Empirical-Research-Skills

Astronomical data processing with Astropy, FITS files, and sky surveys

brycewang-stanford/Auto-Empirical-Research-Skills

Search astrophysics and physics literature via NASA ADS bibliographic database

structure-analysis

synthetic-sciences/openscience

Handles protein structure files and geometric criteria so that residue counts, contacts, hydrogen bonds and interfaces are reproducible, covering PDB versus mmCIF, author versus label chains and numbering, HETATM, water and ligand policy, alternate locations, insertion codes, biological assemblies versus the asymmetric unit, hydrogen-bond distance and angle cutoffs, interface definitions by distance or buried surface area, and the Biopython, MDAnalysis, gemmi, PyMOL and ChimeraX calls that implement them. Use whenever a task reads a structure file and reports anything derived from atom coordinates; use biopython for sequence work and molecular-docking or structure-prediction skills for generating structures.

atomistic-workflows

synthetic-sciences/openscience

Sets up and reports atomistic calculations so they are reproducible and converged, covering ASE Atoms, calculators, optimizers and MD drivers, pymatgen structures, symmetry analysis and the Materials Project API, LAMMPS and GROMACS input basics for molecular dynamics, convergence tests for k-points, plane-wave cutoffs and timesteps, the unit system each code uses, energy-minimization stopping criteria, and archiving of exact input files. Use for DFT, classical or machine-learned potential simulations of molecules, crystals, surfaces and liquids; use molecular-dynamics for biomolecular trajectory analysis and pymatgen for detailed library usage.

cheminformatics-definitions

synthetic-sciences/openscience

Pins down the RDKit definitions that differ between conventions before computing molecular descriptors, including Lipinski donor and acceptor counts (Lipinski.NumHDonors and NumHAcceptors versus rdMolDescriptors.CalcNumLipinskiHBD and HBA), TPSA with or without sulfur and phosphorus, QED weighting, standard versus non-standard InChI and InChIKey, standardization order (cleanup, salt stripping, uncharging, tautomer canonicalization), canonical versus isomeric SMILES and aromaticity models, and states which definition produced each number. Use whenever a task computes, filters or compares small-molecule properties or identifiers; use the rdkit skill for general API usage and medchem or admet skills for interpretation.

astronomy-inference

synthetic-sciences/openscience

Carries astronomical data from file to posterior with the conventions the field expects, covering astropy units, coordinates, FITS, WCS and Time with barycentric corrections, photometric systems and magnitude arithmetic, period finding with the astropy Lomb-Scargle periodogram and its false-alarm levels, Bayesian parameter estimation with emcee and nested sampling with dynesty, convergence diagnostics such as autocorrelation time and R-hat, uncertainty propagation through samples, and explicit reporting of priors. Use for light curves, photometry, spectroscopy, orbit or transit fits and any astronomical parameter estimation; use astropy for plain library usage and bayesian-inference for non-astronomical models.

synthetic-sciences/openscience

Applies energy-systems modelling conventions so that capacity, energy, cost and emissions numbers are consistent and comparable, covering kW versus kWh and MW versus MWh, capacity versus energy, capacity factors, load duration curves, the LCOE formula with discounting and capital recovery factors, dispatch and capacity-expansion modelling with PyPSA and pyomo, time-series alignment across time zones and interval conventions, emissions factors by scope, and the assumptions table every result needs. Use for power-system, generation cost, storage, grid-mix or decarbonization analyses; use statistical-conventions for the statistics and geoscience-data for weather and resource rasters.

geoscience-data

synthetic-sciences/openscience

Reads, transforms and summarizes gridded and geospatial data without losing the metadata that gives it meaning, covering NetCDF and HDF5 through xarray and netCDF4 with CF conventions, dimensions versus coordinates, time encodings and calendars and fill values, GeoTIFF and other rasters through rasterio and GDAL with nodata and resampling choices, coordinate reference systems and reprojection with pyproj and EPSG codes, geodesic versus planar distances and areas, vector data through geopandas, and unit handling with area weighting on latitude-longitude grids. Use for climate, weather, hydrology, remote sensing, land use and any map-based analysis; use the physics-fitting or spectral-analysis skills once the data is a clean array.

foryourhealth111-pixel/Vibe-Skills

Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.

nbodykit-cosmology-analyzer

a5c-ai/babysitter

nbodykit large-scale structure analysis skill for N-body simulations and galaxy surveys