Abstract: A data processing method includes a step for a computer to collect analysis file sets from a plurality of types of analyzers, the analysis file sets each including analysis data by the analyzer, and a step for the computer to tag each of a plurality of collected analysis file sets, the tag indicating a type of the analyzer, a material of a sample measured by the analyzer, preprocessing conditions of the sample, and measurement conditions of the sample.

Abstract: A data processing method includes a step for a computer to collect analysis file sets from multiple types of analyzers, the analysis file sets each including analysis data by the analyzer, a step for the computer to store the plurality of collected analysis file sets in a database with the analysis file sets sorted into corresponding collections for each material, and a step for the computer to form a subset by extracting an analysis file set in which a type of the analyzer, preprocessing conditions of a sample measured by the analyzer, and measurement conditions of the sample all match, from the collection.

Abstract: A data processing method according to one aspect of the present invention includes: a step of collecting an analysis file set including analysis data by an analyzer from a plurality of types of analyzers; a step of selecting a plurality of analysis file sets to be analyzed from the plurality of types of collected analysis file sets according to a narrowing-down condition received from a user; a step of extracting a plurality of features from each of the plurality of selected analysis file sets; a step of summarizing the plurality of features by performing statistical processing of the plurality of features; and a step of performing an analysis using the summarized features. The step of summarizing the plurality of features includes a step of presenting statistical information generated in the statistical processing to the user.

Abstract: A data analysis apparatus, a data analysis method, a method of generating a trained model, a system, and a program that allow highly accurate and easy analysis of an FT-IR spectrum are provided. The data analysis apparatus includes an obtaining unit that obtains an analysis target which is an FT-IR spectrum, a trained model, and an analyzer that inputs the analysis target to the trained model. The trained model is machine-trained so as to output, when the trained model receives input of an FT-IR spectrum, information indicating whether or not the inputted FT-IR spectrum includes a peak derived from a trained atomic group. The trained atomic group includes an atomic group containing at least three atoms.

Abstract: An integrated viewer for multiple measurements according to a first aspect includes: a storage unit that stores, for each of a plurality of types of analyzers, a type of a feature amount obtained from a measurement result of the analyzer; a display control unit that displays types of feature amounts stored in the storage unit on a display screen in a selectable manner; and a registration unit that names and registers a set of types of feature amounts selected by an operator among the types of the feature amounts. The display control unit displays, side by side on the display screen, a first display region in which the type of the feature amount is displayed in a tab for each type of the analyzers and a second display region in which the set of types of the feature amounts selected by the operator is displayed.

Abstract: A viewer for analysis includes: a communication unit for receiving analysis results for each of a plurality of samples, the analysis results being different in type from each other and outputted from a plurality of types of analyzers; a display unit for displaying the analysis results received by the communication unit; a control unit for controlling the display unit; and a first selection means for selecting at least two or more display target samples from the plurality of samples. The display unit includes a display area for displaying the analysis results by the plurality of types of analyzers for each of the at least two or more display target samples; and a list display area for displaying at least two or more display areas in a list, the at least two or more display areas corresponding to the at least two or more display target samples.

Abstract: For each of one or a plurality of types of samples, a database is constructed in which a sample identification tag and data belonging to at least two types of categories out of Categories (1), (2), and (3) are stored in association with each other. Data to be used as training data in supervised learning is selected from the database. At this time, one or a plurality of types of data is selected as an explanatory variable from one of the categories. Further, one or a plurality of types of data is selected as an objective variable from the other category. Then, training data is generated in which data corresponding to the selected explanatory variable is served as input and data corresponding to the selected objective variable is served as ground truth output.

Abstract: The complex analysis database manages the analysis result data in association with each of the plurality of samples. A project-sample registering unit registers, in a project database, a respective one of a plurality of projects in association with one or more selected from the plurality of samples. A project-user registering unit registers, in the project database, a respective one of the plurality of projects in association with one or more selected from a plurality of users. The display control unit displays a project associated with the login user in a selectable manner on an information terminal operated by the login user. The display control unit extracts, from a complex analysis database, the analysis result data of a sample associated with the project selected by the login user and displays the extracted data on the information terminal.

Abstract: A data processing apparatus according to one aspect is provided with an inference unit configured to predict at least one objective variable from a plurality of explanatory variables by using a trained model, and a display data generation unit configured to generate data for displaying an inference result by the inference unit. The inference unit is configured to set a first explanatory variable selected from the plurality of explanatory variables as a variation value and set second explanatory variables other than the first explanatory variable as fixed values. The inference unit predicts, by using the trained model, the at least one objective variable when the first explanatory variable is continuously varied within a predetermined variation range. The display data generation unit generates data indicating a variation of the at least one objective variable with respect to a variation of the first explanatory variable.

Abstract: A polymer/nanoparticle composite hydrogel can be produced from an industrially readily available general-purpose material and has excellent mechanical and stretching properties. A hydrogel and a method for producing the same, the hydrogel having a self-supporting property and including a silicate salt (A), a polyalkylene glycol (B), and a dispersant (C) for the silicate salt, the hydrogel being characterized in that: the polyalkylene glycol (B) has a weight average molecular weight, number average molecular weight, or viscosity average molecular weight of 500,000 to 20,000,000; and the amount by mass of the polyalkylene glycol (B) is more than 2% by mass and 20% by mass or less relative to 100% by mass of the hydrogel.