Automatic detection of outliers is universally needed when working with scientific datasets, e.g., for cleaning datasets or flagging novel samples to guide instrument acquisition or scientific analysis. We present Domain-agnostic Outlier Ranking Algorithms (DORA), a configurable pipeline that facilitates application and evaluation of outlier detection methods in a variety of domains. DORA allows users to configure experiments by specifying the location of their dataset(s), the input data type, feature extraction methods, and which algorithms should be applied. DORA supports image, raster, time series, or feature vector input data types and outlier detection methods that include Isolation Forest, DEMUD, PCA, RX detector, Local RX, negative sampling, and probabilistic autoencoder. Each algorithm assigns an outlier score to each data sample. DORA provides results interpretation modules to help users process the results, including sorting samples by outlier score, evaluating the fraction of known outliers in n selections, clustering groups of similar outliers together, and web visualization. We demonstrated how DORA facilitates application, evaluation, and interpretation of outlier detection methods by performing experiments for three real-world datasets from Earth science, planetary science, and astrophysics, as well as one benchmark dataset (MNIST/Fashion-MNIST). We found that no single algorithm performed best across all datasets, underscoring the need for a tool that enables comparison of multiple algorithms.