Abstract:
Methods and apparatus, including computer program products implement techniques for processing experimental data according. An input specifies a set of variable definitions according to a variable definition template for defining a set of variables of a plurality of variable types usable in experiments of a pre-defined experiment class. Data from an experiment of an experiment type is received. A first representation of the data is stored in a format defined according to the plurality of variable types. A second representation of the data, derived from the first representation, is presented in a format defined according to the set of variable definitions. The variable definition template is referenced in the pre-defined experiment class. The data includes a plurality of values corresponding to variables defined in the set of variable definitions.

Description:
BACKGROUND 
     This invention relates to database systems and methods for storing and manipulating experimental data. 
     The discovery of new materials with novel chemical and physical properties often leads to the development of new and useful technologies. Traditionally, the discovery and development of materials has predominantly been a trial and error process carried out by scientists who generate data one experiment at a time. This process suffers from low success rates, long time lines, and high costs, particularly as the desired materials increase in complexity. There is currently a tremendous amount of activity directed towards the discovery and optimization of materials, such as superconductors, zeolites, magnetic materials, phosphors, catalysts, thermoelectric materials, high and low dielectric materials and the like. Unfortunately, even though the chemistry of extended solids has been extensively explored, few general principles have emerged that allow one to predict with certainty the composition, structure and/or reaction pathways for the synthesis of such solid state materials. 
     As a result, the discovery of new materials depends largely on the ability to synthesize and analyze large numbers of new materials. Given approximately 100 elements in the periodic table that can be used to make compositions consisting of two or more elements, an incredibly large number of possible new compounds remain largely unexplored, especially when processing variables are considered. One approach to the preparation and analysis of such large numbers of compounds has been the application of combinatorial chemistry. 
     In general, combinatorial chemistry refers to the approach of creating vast numbers of compounds by reacting a set of starting chemicals in all possible combinations. Since its introduction into the pharmaceutical industry in the late 1980s, it has dramatically sped up the drug discovery process and is now becoming a standard practice in that industry (Chem. Eng. News Feb. 12, 1996). More recently, combinatorial techniques have been successfully applied to the synthesis of inorganic materials (G. Briceno et al., SCIENCE 270, 273–275, 1995 and X. D. Xiang et al., SCIENCE 268, 1738–1740, 1995). By use of various surface deposition techniques, masking strategies, and processing conditions, it is now possible to generate hundreds to thousands of materials of distinct compositions per square inch. These materials include high T c  superconductors, magnetoresistors, and phosphors. 
     Using these techniques, it is now possible to create large libraries of diverse compounds or materials, including biomaterials, organics, inorganics, intermetallics, metal alloys, and ceramics, using a variety of sputtering, ablation, evaporation, and liquid dispensing systems as disclosed in U.S. Pat. Nos. 5,959,297, 6,004,617 and 6,030,917, which are incorporated by reference herein. 
     The generation of large numbers of new materials presents a significant challenge for conventional analytical techniques. By applying parallel or rapid serial screening techniques to these libraries of materials, however, combinatorial chemistry accelerates the speed of research, facilitates breakthroughs, and expands the amount of information available to researchers. Furthermore, the ability to observe the relationships between hundreds or thousands of materials in a short period of time enables scientists to make well-informed decisions in the discovery process and to find unexpected trends. High throughput screening techniques have been developed to facilitate this discovery process, as disclosed, for example, in U.S. Pat. Nos. 5,959,297, 6,030,917 and 6,034,775, which are incorporated by reference herein. 
     The vast quantities of data generated through the application of combinatorial and/or high throughput screening techniques can easily overwhelm conventional data acquisition, processing and management systems. Existing laboratory data management systems are ill-equipped to handle the large numbers of experiments required in combinatorial applications, and are not designed to rapidly acquire, process and store the large amount of data generated by such experiments, imposing significant limitations on throughput, both experimental and data processing, that stand in the way of the promised benefits of combinatorial techniques. 
     SUMMARY 
     The invention provides methods, systems and apparatus, including computer program apparatus, representing a generic experiment model or class adaptable to a researcher-defined set of variables, for processing data from chemical experimentation. 
     In general, in one aspect, the invention provides methods and apparatus, including computer program products implementing techniques for processing experimental data according to an object model. The techniques include receiving input specifying a first set of one or more variable definitions according to a variable definition template for defining variables of a plurality of variable types, receiving data from an experiment of a first experiment type, storing a first representation of the data from the experiment of the first experiment type in a format defined according to the plurality of variable types, and presenting a second representation of the data from the experiment of the first experiment type in a format defined according to the first set of variable definitions. The variable definition template is referenced in a first pre-defined experiment class of the object model. The plurality of variable types are usable in experiments of the first pre-defined experiment class. The first set of variable definitions defines a set of variables of one or more of the plurality of variable types for the first experiment type. The data includes a plurality of values corresponding to variables defined in the first set of variable definitions. The second representation of the data from the experiment of the first experiment type is derived from the first representation. 
     Advantageous implementations can include one or more of the following features. The input specifying a first set of one or more variable definitions can include input defining an experiment name and, for each variable in the set of variable definitions, input defining a variable name and a datatype. The first representation can include an entry for each of the plurality of values. The second representation can include an entry representing values for two or more variables defined in the first set of variables. Each entry can be a row in a table. The data from the experiment of the first experiment type can include values for a plurality of members of a library of materials. The second representation can include an entry representing values for two or more variables for a member of the library. The first representation can be a sparse table. The second representation can be a dense table. The second representation can include columns corresponding to the defined variables. 
     A set of data from a second experiment can be received. The second set of data can, include one or more values corresponding to a set of variables defined in a second pre-defined experiment class of the object model. A representation of the data from the experiment of the second experiment type can be stored and presented in a format defined according to the second pre-defined experiment class of the object model. 
     Input can be received specifying a second set of one or more variable definitions according to the variable definition template. The second set of variable definitions can define a set of variables for a second experiment type. Data can be received from an experiment of the second experiment type. The data can include a plurality of values corresponding to variables defined in the second set of variable definitions. A first representation of the data from the experiment of the second experiment type can be stored in a format defined according to the variable definition template. A second representation of the data from the experiment of the second experiment type can be presented. The second representation can be derived from the first representation and presented in a format defined according to the second set of variable definitions. 
     The invention can be implemented to realize one or more of the following advantages. The class is user-configurable. The class can be dynamically extended to include additional sets of variables without additional server or database software development. The configuration is accomplished by the definition of variables. The variables can be defined by the researcher. The experimental results are presented for querying and viewing in an equivalent manner as for non-configurable or pre-defined experiments. Instances of the generic experiment class can be used to extend an object model that also implements specific classes. The class provides a rapid and accessible data storage method. The class is suitable for low to medium volumes of data with minimal software development requirements. 
     The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims. 
    
    
     
       DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram illustrating a laboratory data management system including a database server process according to one aspect of the invention. 
         FIG. 2A  is a diagram illustrating an object model implementing a user-configurable generic experiment class according to one aspect of the invention. 
         FIG. 2B  is a diagram illustrating a user-configurable generic experiment subclass according to one aspect of the invention and several specific experiment subclasses. 
         FIG. 3  is a flow diagram illustrating a method of defining an experiment as a subclass of a user-configurable generic experiment class according to one aspect of the invention. 
         FIGS. 4A–4B  illustrate XML documents defining variables and the relational database table into which such an object is mapped. 
         FIGS. 5A–5B  illustrate an XML document defining a user-configured experiment object and the relational database tables into which the object is mapped. 
         FIGS. 6A–6B  illustrate relational database tables used to store data for a NameValueExperiment object for an exemplary experiment called VisualInspection. 
         FIG. 7  is a flow diagram illustrating a method of presenting the results of an experiment defined as a subclass of a user-configurable generic experiment class according to one aspect of the invention. 
         FIGS. 8A–8C  illustrate the default user interface for viewing data retrieved from a experiment defined by a user-configured generic experiment class, the translation or pivoting of elements in the default user interface, and the resulting pivoted user interface for viewing data retrieved from the experiment. 
     
    
    
     Like reference symbols in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
       FIG. 1  illustrates a laboratory data management system  100  that includes a general-purpose programmable digital computer system  110  of conventional construction including a memory  120  and a processor for running a database server process  130 , and one or more client processes  140 . As used in this specification, a client process is a process that uses services provided by another process, while a server process is a process that provides such services to clients. Client processes  140  can be implemented using conventional software development tools such as Microsoft® Visual Basic®, C++, and Java™, and laboratory data management system  100  is compatible with clients developed using such tools. In one implementation, database server process  130  and client processes  140  are implemented as modules of a process control and data management program such as that described in U.S. application Ser. No. 09/550,549, filed Apr. 14, 2000, which is incorporated by reference herein. Optionally, client processes  140  include one or more of automated or semi-automated laboratory apparatuses  150 , a user interface program  160  and/or a process manager  170  for controlling laboratory apparatus  150 . Exemplary laboratory apparatuses, user interface programs and process managers are described in more detail in U.S. application Ser. No. 09/239,223, filed Jan. 1, 1999, and U.S. application Ser. No. 09/550,549, filed Apr. 14, 2000, each of which are incorporated by reference herein. 
     Laboratory data management system  100  is configured to manage data generated during the course of the experiments. Database server process  130  is coupled to a database  180  stored in memory  120 . In general, laboratory data management system  100  receives data from client  140  for storage, returns an identifier for the data, provides a way of retrieving the data based on the identifier, provides the ability to search the data based on the internal attribute values of the data, and the ability to retrieve data from the these queries in a number of different ways, generally in tabular (e.g., in a relational view) and object forms. In one implementation, laboratory data management system  100  maintains three representations of each item of data: an object representation; a self-describing persistent representation, and a representation based on relational tables. Laboratory data management system  100  can be implemented as a laboratory information system as is described in U.S. application Ser. No. 09/755,623, filed Jan. 5, 2001, which is incorporated by reference herein. 
     Experiments are performed, for example, by laboratory apparatus  150 , on an experimental sample such as a single material or, more typically, on a set of experimental samples such as a library of materials. A library of materials is a collection of members, typically two or more members, generally containing some variance in chemical or material composition, amount, reaction conditions, and/or processing conditions. The library can be a matrix, where each member represents a single constituent, location, or position on a substrate. The library can be a conceptual collection, where each member represents, for example, data or analyses resulting from the analysis of experiments performed on unrelated samples (e.g., samples that are not located on a common substrate), or from simulations or modeling calculations performed on hypothetical samples. Experiments can involve the measurement of numerous variables or properties by the laboratory apparatus, as well as processing (or reprocessing) data gathered in previous experiments or otherwise obtained, such as by simulation or modeling. Typical laboratory apparatus and experimental data suitable for use in and/or manipulation by the laboratory data management systems described herein are, discussed in more detail in U.S. application Ser. No. 09/755,623, filed Jan. 5, 2001, and U.S. application Ser. No. 09/840,003, filed Apr. 19, 2001. 
     Database  180  stores experimental data, including observations, measurements, calculations, and analyses of data from experiments performed by laboratory data management system  100 . In one implementation, data is stored as a series of values, each having one of potentially several types. “Name” refers to a property of a material or, more generally, an experimental variable. “Value” refers to data that are associated with a named variable. The data can be one of many possible data types, and can be observed, measured, calculated, or otherwise derived for the experiment. The data can be, for example, a number, a phrase, a data set, or an image. The data can be for the entire library or for individual member elements of a library. In the former case, one or more values can be represented corresponding to one or more substrates in their entirety, for example an image of the entire substrate. In the latter case, data can be collected for each element or for any set of elements. The data can include multiple measurements for any given element or elements, as when measurements are repeated or when multiple measurements are made, for example, at different set points, different locations within a given element or elements, or at different times during the experiment. For example, the optical reflectance of the material at each element position can be measured at multiple wavelengths, and each measurement can be repeated for accuracy within the same experiment. 
     In one implementation, client processes  140  interact with experimental data generated in system  100  through an object model representing an experiment performed by system  100 , as illustrated in  FIG. 2A . In this object model, an experiment performed by system  100  is represented by an experiment object  230 —here referred to as a NameValueExperiment—having a set of associated properties and methods that represent the experiment. The experiment object  230  can be associated with a particular library of materials  200 —for example, by virtue of an associated LibraryID  220  (here represented as a barcode) that identifies the library in laboratory data management system  100 . The experiment object  230  is also associated with Library Element Data Objects  340 , which are typically children of the experiment object  230 . 
     The Library Data Objects  260  and Library Element Data Objects  240  identify data for experimental properties or variables (“names”)  250  defined by the user, as will be described in more detail below. If the experiment involves multiple members of a library, there is typically a Library Element Data Object for each named variable  350  for each member or position  310  of the library  300 . There can be Library Element Data Objects for only some of the defined variables at some of the members of the library. For example, values for a comment variable can be defined only for interesting or notable members of the library. The data typically does not include values of undefined types or for undefined variables. Similarly, Library Data Objects  260  are associated with the library  200 . Each Library Data Object  260  can include data for the library as a whole or for a subset of members of the library. 
     In one implementation, the user-configurable generic experiment object  230  can be implemented in an object model that implements a user-configurable generic experiment class and one or more specific experiment classes configured to represent data for particular experiment types. Thus, for example, the user-configurable generic experiment class can be implemented as a subclass of an experiment base class, and can inherit from the base class general properties associated with an experiment, such as ExperimentID, an integer uniquely identifying the experiment within system  100 ; Keywords, strings specifying searchable terms identifying the experiment; Project, a string describing the context or motivation for the newly defined experiment; Notebook, an integer identifying the laboratory notebook in which the experiment is recorded; LibRows and LibCols, integers identifying the number of rows and columns in the relevant library; and the like. To this set of general inherited properties, the user-configurable generic experiment (sub)class adds a reference to a set of variable definitions, such as the name of the experiment for which the experimental variables have been defined. Typically, the named reference can be instantiated to provide one or more Variable Definition Objects. The user-configurable generic experiment (sub)class can also add a collection of Library Data Objects and/or a collection of Library Element Data Objects. The Library Data Objects can contain data applicable to the entire library under study, and typically are defined by the Variable Definitions. The Library Element Data Objects, which also typically are defined by the Variable Definitions, can contain data applicable to each or any of the individual elements of the library. 
     The form of a particular experiment object  230  is dependent upon the associated definition  250  of the properties of Library Element Data Objects  240  and the Library Data Objects  260 . As shown in  FIG. 2B , a generic experiment subclass  30  can be derived from or inherit the properties of an experiment class  10 . Similarly, various specific experiment subclasses  60 ,  70 ,  80 , can be derived from or inherit the properties of the experiment class  10 . Each instantiation of a specific experiment subclass  60 ,  70 ,  80 , typically defines a different kind of experiment and can be associated with a particular type of elements  61 ,  71 ,  81 , which refer to the data for the experiment. For example, a gel permeation chromatography (GPC) experiment subclass would have elements with properties specifically defined to receive data of the type generated in GPC experiments, such as molecular weight and it&#39;s moments Mw, Mn, and Mz, and polydispersity index PDI, as described in U.S. application Ser. No. 09/755,623, filed Jan. 5, 2001. 
     An instantiation of a user-configurable generic experiment subclass  30  can be associated with one or more of many possible definitions of elements  31 ,  32 ,  33 , depending upon the instantiation of the Variable Definition Class  50 . Because each instance of the user-configurable generic experiment subclass  30  can differ from other instances according to the variable definitions on which its Library Data Objects and Library Element Data Objects are based, the Variable Definition Class  50  can be used to effectively define a variety of different “virtual subclasses” by defining elements that are instantiated as objects that can, for example, represent different types of experiments as would specific experiment objects instantiated from different pre-defined subclasses  60 ,  70 ,  80 , of the experiment class  10 . 
     The object model shown in  FIGS. 2A–B  can be used as shown in  FIG. 3 . A new experimental model or “experiment” is created by defining the variables for the user-configurable generic experiment class. The variables can be defined by the researcher, for example, using a GUI. System  100  receives a name for the experiment (step  310 ). The name must uniquely identify the set of variables to be defined and is typically a string, for example, “VisualInspection”. System  100  receives the names of properties or variables to be included in the experiment, and an indication of the data type of each named variable (step  320 ). Each variable can have two or more names, for example, a variable can have a data name, used in data storage, and a display name, used when displaying the data to the researcher. The data type can be one of many types, including string, double, date, boolean, XYDataset, image, and external data file. In one implementation, the data type for each named variable can be selected in step  320  from a predefined set of data types corresponding to the range of data types needed to represent data from experiments that can be performed using laboratory data management system  100 . Additional information that can be used in a GUI for data entry system  100 , can be associated with the name, for example, comments that include descriptive information about the field, and a flag indicating whether a value for the data name is required to complete the experiment. 
     System  100  receives experimental data (step  330 ), typically in the form of data objects, and processes the data for storage in the database  180  (step  340 ). In one implementation, database server process  130  maps classes (e.g., an experiment subclass  60 ,  70 ,  80 , or the user-configurable generic class  30 ) to database tables, with each row representing an individual instance of the class or classes in the table, as described in more detail below. Communication between client processes  140  and database server process  130  using data objects, and the extraction and storage of data in relational database tables are described in more detail in U.S. application Ser. No. 09/755,623, filed Jan. 5, 2001. After data are stored, system  100  can then receive a request for stored experimental data (step  350 ), and can return the requested data to the user (step  360 ). 
     As an example, a “visual inspection” experiment can involve monitoring the visual attributes of a particular reaction, with the visual attributes being recorded as images of the material or materials over the course of the experiment and notes about their visual appearance. Such an experiment can be implemented in system  100  by creating a specific subclass  60 ,  70 ,  80  of an Experiment base class  10 , e.g., as described in U.S. application Ser. No. 09/755,623, filed Jan. 5, 2001. The definition of a specific experiment subclass can require a level of expertise and effort that is appropriate or justified for high-volume experiments or experiments conducted repeatedly. 
     A “visual inspection” experiment also can be implemented using the pre-defined, user-configurable generic experiment class as described herein; the visual inspection experiment is defined by defining the variables for a particular instance of the user-configurable generic experiment class. For example, a researcher creates a visual inspection experiment by defining, for example, an image variable that takes image data, and a notes variable that takes string data. The definition of variables for a user-configurable generic experiment class can require minimal expertise and effort, such that this approach can be used for low-volume or uncommon or atypical experiments, such as pilot, exploratory, or scale-up experiments. 
     Variables of a user-configurable generic experiment class can be defined, for example, using a GUI to create variable definition objects, for example, as XML code. A wizard can be used to prompt the user to provide the information necessary to create the variable definition objects. For example, a wizard can prompt the user for a name of the experiment and then prompt the user for information for each variable, for example, by displaying the variable&#39;s DataName and Description and, based on the variable&#39;s DataType, providing data type checking or other use aids. For example, a standard system file browser can be used to select an external file in the case of a Variable of StoredFile type. The wizard then converts the information received into XML. In one implementation, variable definition objects can also be defined automatically, for example, based upon one or more sample data sets. The sample data set can be parsed to identify variables in the data, and a variable definition for each variable type can be generated—for example, by extracting relevant attributes of the corresponding data (e.g., attributes of a corresponding element defined in an XML stream representing the sample data set).  FIG. 4A  illustrates a portion of an XML document representing two variable definition objects—denoted here as NameValueDefinition Objects—for an experiment having the name VisualInspection. Database server process  130  receives a variable definition object represented by the XML of  FIG. 4A  and parses it into storage in an Oracle table whose format is shown in  FIG. 4B . 
     In the example shown in  FIG. 4A , the first variable definition object has ID=“1” and defines an image variable representing an image of a library member. The variable is given two names: a DataName, “VisualInspectionImage,” and a DisplayName, “Camera image.” The DataName is used to reference the variable internally within system  100 , while the DisplayName is use to present the values of the variable to the user. The data value to be associated with this name is of image data type, as shown by DataType=32 where 32 is a code for “image.” Each DataName should be unique within the scope of the ExperimentName, and should have a DataType value chosen from a set of integers that represent a set of supported data types, such as string, double, date, Boolean, XYDataSet, Image, and StoredFile. Each variable definition object is denoted as having a library or library element scope, which defines whether the data is to be collected on the entirety of the library or on each or any library element. The Scope for the first variable definition object is the value 2, which denotes the data apply to the library elements. The Required value of 1 means that a valid image will be required of any experimental data to be stored. 
     The second variable definition object in  FIG. 4A  defines a “notes” variable representing textual observations resulting from a visual inspection of a library member (or a corresponding image). The variable is given two names: a DataName, “VisualInspectionComment,” and a DisplayName, “Content notes.” The data value to be associated with this name is of string data type, as shown by DataType=1 where 1 is a code for “string.” The scope of the variable is also defined as 2, where 2 is the code for library element scope, as described above. Comments can be but are not included in this example. The Required value of 0 means that the data is optional. 
       FIG. 5A  illustrates an XML document that is received by the Database server process  130  and represents data from the VisualInspection experiment having variables as defined in  FIGS. 4A–B . The XML can include properties inherited from the Experiment base class. For example, the base class can define properties that generally describe any experiment that can be performed using the system, such as the name of the researcher creating or conducting the experiment, the notebook and pages where the experiment is recorded, and the identity of the library. The user-configurable generic experiment class can define properties such as the geometry of the library of materials, and, by name, the set of variable name definition objects that are used in this specific instance. In this example, the library has 1 row and 3 columns for a total of 3 members (libraries typically will but need not necessarily have more members), and the experiment is using variable definition objects defined for a “VisualInspection” experiment. 
     The XML representation of the visual inspection experiment can also include the Library Element Data Objects that represent the data for the experiment. Typically, there is a Library Element Data Object for each named variable at each position. In general, for an experiment of v variables and a library of p positions, there will be p×v Library Element Data Objects. (The number of positions can, for example, be determined from the number of rows, r, and columns, c, as p=r×c.) For a large library and an experiment having many variables or properties, the number of Library Element Data Objects can be very large. In some implementations it may be desirable to limit the number of Library Element Data Objects, and the number of variables, for a given experiment to limit the consumption of memory and/or processing resources. 
     In the example, there are 3 positions  310 , as indicated in  FIG. 5A , and 2 named variables  350 , as indicated in  FIG. 4A  (VisualInspectionImage and VisualInspectionComment). Thus, there are 6 Library Element Data Objects, as shown in  FIG. 5A . Each Library Element Data Object provides a value, in this case either ImageID (data type image) or SValue (data type string), for a variable at a given position. As shown, the first Library Element Data Object provides the ImageID for the picture at position  1 , the second Library Element Data Object provides the SValue for the notes about position  1 , the third Library Element Data Object provides the ImageID for the picture at position  2 , the fourth Library Element Data Object provides the SValue for the notes about position  2 , the fifth Library Element Data Object provides the ImageID for the picture at position  3 , and the sixth Library Element Data Object provides the SValue for the notes about position  3 . The specific values of certain data types typically are not stored or represented within the NameValueExperiment. For example, concrete instances of variable objects of DataType XYDataSet, Image, or StoredFile can be stored and represented as reference IDs, which refer to externally stored objects of these types. 
     Database server process  130  maps the experiment object represented by the XML of  FIG. 4A  into one or more Oracle tables, for example, the tables whose format is shown in  FIG. 5B . 
       FIGS. 6A–B  show the relational database tables for a library and a newly defined experiment on that library called VisualInspection, as was defined in  FIG. 4A  and as shown in  FIG. 5A . Information about Experiment classes associated with the library used in the Visual Inspection experiment is shown in  FIG. 6A . Included is an entry for a specific class called Synthesis and an entry for a user-configurable generic class called NameValueExperiment, for which a virtual subclass, VisualInspection, has been defined. Information from the Library Element Data Objects for the instantiation of the NameValueExperiment class as specified by the variable definitions for the experiment called VisualInspection is shown in  FIG. 6B . There is an entry for each Library Element Data Object. In this example, each entry is a row and columns correspond to the data types that a value can take. Although a particular Library Element Data Object typically has only one value, the value can be one of many types (string or SValue, number or NValue, etc.). The Library Element Data Object Table is therefore sparse, having a large number of empty cells (i.e., cells having values that are zero or undefined). There are numerous columns, corresponding to the possible datatypes for a named variable, but for any given row, there is typically data in only one or a few of these columns. 
     A user of system  100  can search and/or retrieve data from database  180 . The overall process is the reverse of the process of storing objects: the system retrieves the object from the database, and maps into an XML document and the document is returned to the requester as a string. In general, clients and users are isolated from the details of data storage in database  180 , and can only manipulate data in ways explicitly permitted by database server process  130 . A user can, for example, submit requests by manipulating a menu bar in a Queries window displayed by a user interface program. Exemplary methods for searching and retrieving data are described in U.S. application Ser. No. 09/755,623, filed Jan. 5, 2001. 
     The system  100  typically presents the user with a tabular representation of requested object data. The tabular representation capability of the server provides access to data for multiple objects in a compact form resembling a single relational table. A user can select certain fields to be displayed and can otherwise manipulate the data to be presented. System  100  can then present a table having one or more rows corresponding to the objects satisfying the query, and one or more columns corresponding to the selected displayable fields. Exemplary methods for presenting data in a tabular form resembling a relational table are described in U.S. application Ser. No. 09/755,623, filed Jan. 5, 2001 and PCT application number PCT/US02/00466, filed Jan. 7, 2002, which is incorporated by reference herein. 
     Presentation of the data for a user-configurable generic experiment object as it is stored in the relational data base, as shown in  FIG. 6B , may result in presentation of a sparse representation of the data, characterized by a multiplicity of data per library element in the vertical direction, as shown in  FIG. 8A . Because the presentation of the data in this form requires a user to scan both horizontally and vertically through the data, and furthermore to correlate two columns of information in order to process what is essentially one relevant experimental fact, this presentation of the data can be both insufficient and undesirable from a user&#39;s point of view. 
     In one implementation, the system  100  can translate the data table having requested data before returning the data to the user. As shown in  FIG. 7 , system  100  receives a request for experimental data (step  750 ). The system retrieves the data from a relational database table (step  770 ) and translates or pivots the data generating a dense representation of the data for presentation based on information in the corresponding variable definition objects (step  780 ), such that the empty data fields are removed and the verticality of the data is translated into a multiplicity of columns, where each column corresponds to a variable definition object. The results of the experiment are returned to the researcher as if the data storage were “column rich,” which is the form generally used for other, specific types of derived experiment classes as described and shown by example in U.S. application Ser. No. 09/755,623, filed Jan. 5, 2001. 
     The generation of the dense representation is illustrated in  FIGS. 8A–C . As shown in  FIG. 8A , the default (sparse) representation of data from an experiment object resembles the Library Element Data Object table that is used to store the data (as shown in  FIG. 6B ). This representation includes an entry, for each Library Element Data Object. In this example, each entry is a row and columns correspond to the properties of the Library Element Data Object, including the variable&#39;s Name, and each of several possible data types for the value of that named variable, such as SValue, NValue, etc. While there is a column for each possible data type, typically only one column contains a reference to the particular object that contains the data. Because the value for a named property typically is of only one datatype, the table is sparse. 
     The default representation can be pivoted as shown in  FIG. 8B  to create a dense representation as shown in  FIG. 8C . The name of each experimental variable is transposed to the head of column containing the values for that variable. For example, the variable whose DisplayName is “Camera image” has datatype “image.” The column for the datatype, in this example, “image,” is replaced with the DisplayName of the variable having that datatype, in this example, “Camera image.” If there are two or more variables of the same datatype, then a separate column is created for each Variable. 
     After pivoting, the default representation can be collapsed to remove empty cells from the display. Data for some or all the variables at a single position in the library are then consolidated into a single entry, such as a row in a table. For example, the default representation shown in  FIG. 8B  includes two rows for the first position, row A and column 1, because there are two variables. To generate a dense representation, the values for all but one of the variables at this position, in this example, “Content notes,” are combined in a single row. Columns without data are also removed from the display. The resulting pivoted and collapsed representation is shown in  FIG. 8C . This representation includes a single entry or row for each position in the library, and a column for each named variable in the NameValueExperiment. Each cell contains a value for named variable at the indicated position. 
     A number of embodiments of the invention have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the invention. Accordingly, other embodiments are within the scope of the following claims.