Científicos y especialistas ambientales, incluyendo salud

Ciencias de la vida, físicas y sociales · Habilidades y competencias laborales · SOC 19-2041 · 844 skills

Contexto de la ocupación

Estas tareas de O*NET describen la ocupación y aportan contexto a la asignación SOC; no significan que cada Skill de la lista realice todas las tareas.

Communicate scientific or technical information to the public, organizations, or internal audiences through oral briefings, written documents, workshops, conferences, training sessions, or public hearings.
Monitor effects of pollution or land degradation and recommend means of prevention or control.
Collect, synthesize, analyze, manage, and report environmental data, such as pollution emission measurements, atmospheric monitoring measurements, meteorological or mineralogical information, or soil or water samples.

Las tareas de O*NET se muestran en su idioma de origen (inglés). Los datos de tareas se han extraído de O*NET® 29.1 Database de USDOL/ETA y se utilizan bajo CC BY 4.0.

Fuentes representativas

Una muestra limitada de Skills populares asignadas a esta ocupación, con un máximo de una por repositorio de GitHub. A continuación aparece la lista completa.

brycewang-stanford/Auto-Empirical-Research-Skills

5 ecology & environmental science skills. Trigger: biodiversity surveys, species data, environmental monitoring. Design: field data collection, spatial analysis, and conservation biology workflows.

mvanhorn/printing-press-library

Use OpenAQ and AirNow source-backed recipes for air-quality snapshots, nearby monitors, sensor history, point comparison, and AirNow AQI setup/current observations. Trigger phrases: air quality, OpenAQ, AirNow, AQI, PM2.5, nearby monitor, air-quality-pp-cli.

gamma-phase-associator

benchflow-ai/skillsbench

An overview of the python package for running the GaMMA earthquake phase association algorithm. The algorithm expects phase picks data and station data as input and produces (through unsupervised clustering) earthquake events with source information like earthquake location, origin time and magnitude. The skill explains commonly used functions and the expected input/output format.

Todas las Skills asignadas

Explora la asignación completa por popularidad del repositorio o actividad reciente de la fuente.

Los nombres y las descripciones conservan el idioma proporcionado por el repositorio de origen.

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brycewang-stanford/Auto-Empirical-Research-Skills

5 ecology & environmental science skills. Trigger: biodiversity surveys, species data, environmental monitoring. Design: field data collection, spatial analysis, and conservation biology workflows.

climate-modeling-guide

brycewang-stanford/Auto-Empirical-Research-Skills

Climate simulation, modeling tools, and climate data analysis methods

mvanhorn/printing-press-library

Use OpenAQ and AirNow source-backed recipes for air-quality snapshots, nearby monitors, sensor history, point comparison, and AirNow AQI setup/current observations. Trigger phrases: air quality, OpenAQ, AirNow, AQI, PM2.5, nearby monitor, air-quality-pp-cli.

gamma-phase-associator

benchflow-ai/skillsbench

An overview of the python package for running the GaMMA earthquake phase association algorithm. The algorithm expects phase picks data and station data as input and produces (through unsupervised clustering) earthquake events with source information like earthquake location, origin time and magnitude. The skill explains commonly used functions and the expected input/output format.

benchflow-ai/skillsbench

An overview of the core data API of ObsPy, a Python framework for processing seismological data. It is useful for parsing common seismological file formats, or manipulating custom data into standard objects for downstream use cases such as ObsPy's signal processing routines or SeisBench's modeling API.

seisbench-model-api

benchflow-ai/skillsbench

An overview of the core model API of SeisBench, a Python framework for training and applying machine learning algorithms to seismic data. It is useful for annotating waveforms using pretrained SOTA ML models, for tasks like phase picking, earthquake detection, waveform denoising and depth estimation. For any waveform, you can manipulate it into an obspy stream object and it will work seamlessly with seisbench models.

seismic-picker-selection

benchflow-ai/skillsbench

This is a summary the advantages and disadvantages of earthquake event detection and phase picking methods, shared by leading seismology researchers at the 2025 Earthquake Catalog Workshop. Use it when you have a seismic phase picking task at hand.

flood-detection

benchflow-ai/skillsbench

Detect flood events by comparing water levels to thresholds. Use when determining if flooding occurred, counting flood days, aggregating instantaneous data to daily values, or classifying flood severity.

nws-flood-thresholds

benchflow-ai/skillsbench

Download flood stage thresholds from NWS (National Weather Service). Use when determining flood levels for USGS stations, accessing action/minor/moderate/major flood stages, or matching stations to their flood thresholds.

usgs-data-download

benchflow-ai/skillsbench

Download water level data from USGS using the dataretrieval package. Use when accessing real-time or historical streamflow data, downloading gage height or discharge measurements, or working with USGS station IDs.

benchflow-ai/skillsbench

Basic usage of the General Lake Model (GLM) for lake temperature simulation. Use when you need to run GLM, understand input files, or modify configuration parameters.

glm-calibration

benchflow-ai/skillsbench

Calibrate GLM parameters for water temperature simulation. Use when you need to adjust model parameters to minimize RMSE between simulated and observed temperatures.