the chau hiix archive: principles, problems, and solutions

THE CHAU HIIX ARCHIVE: 

PRINCIPLES, PROBLEMS, AND SOLUTIONS 

Caroline Beebe, Ph.D. 

Data Manager, Chau Hiix Project 

(carolinebeebe@hotmail.com) 

Begin by watching “Team Digital and the Deadly Cryptic Conundrum” 

at http://www.youtube.com/user/wepreserve 

The research design for excavations at Chau Hiix, Belize, is concerned with understanding the Maya 

Collapse in terms of bureaucratic interference in the exploitation strategies of smallholders (PI is 

Dr. Anne Pyburn, Indiana University). A regional comparison of data from other sites such as Altun 

Ha and Lamanai, as well as chronological comparisons, would facilitate an understanding of the 

similarities and differences in various sites and the complexity of their development and eventual 

demise. Digital technology provides an ideal tool to facilitate data comparisons through time and 

space. Digital data collection, management, and archiving, however, are wrought with new 

operational logistics, lack of established methodologies, and ever changing technology 

developments that represent yet another area of expertise to be developed by a principal 

investigator. Large institutions and businesses have well established systems in place for all stages 

of digital workflow, but individuals and small projects (sometimes referred to as “small science”) 

must design these systems themselves without the aid of built-in supercomputer systems or large 

number crunching grants. 

Digital archiving is the culminating step of the digital data workflow. Though there is a lot of 

information being written about this topic, there are no specific examples. Available information 

assumes the reader understands what format and syntax mean, how to build a database, how to 

create controlled vocabularies, or how to name files (such as what constitutes a good filename 

within the boundaries of type and number of characters). This document lays out the principles and 

guidelines for digital archiving, as interpreted by the Chau Hiix project, and then describes the Chau 

Hiix Data Archive as a specific instantiation. The methodology for this archive is the result of 15 

years of trial and error in the identification of principles and rules, and constant modification of the 

implementation of those rules. It is also the result of both conceptual and data input from students, 

staff and professional personnel, without which the Chau Hiix Digital Archive would not exist. 

The goal of the Chau Hiix Digital Archive is for the data to be share-able and available in the future. 

The principles that make this possible are: Data are organized and consistent, and data are archived 

with a stewardship plan. Controlled vocabularies and archival formats are both key to supporting 

these principles. The archive process for digital data is simply stated: Save-as text, copy, print and 

store. (Since image files cannot be stored as text, the only recourse is to save in an archival image 

format.) Each of these steps is replete with problems and decisions, and the process still does not 

guarantee the accessibility of the data in perpetuity but brings the data closer to that potential. 

chau-hiix-digital-archive.doc 1 3/30/2010 3:17 PM

The material that follows is at times cryptic as it uses terminologies and concepts from both 

archaeology and information technology. All information technologists have gaps in their digital 

knowledge, so it is unreasonable to expect an archaeologist to be an expert in the digital arena in 

addition to their own content area. On the other hand, information technologists supporting 

archaeology activities are often too fascinated with the latest and greatest emerging technology. 

Their technology recommendations often presume skills sets beyond the scope of the daily 

experiences of humanities scientists and so are costly in time by requiring constant learning or relearning. 

Digital archiving is a complex and emerging activity and would be embraced by more 

people, as well as other disciplines, if presented in some sort of Digital Archiving for Dummies 

format. 

Problem areas this archive still struggles with include: The naming of some types of image files 

(how specific should the name be?); training surveyors in developing consistent point description 

codes; establishing guidelines for the selection of data to archive (e.g., if processed data can be 

recreated from the primary data, does it need to be included in the archive?); and, stewardship 

issues, such as long term storage plans and version control of data corrections/analyses. The 

checklist at the end of this document summarizes the specific tasks as instantiated at this time, and 

the rest of this document explains those tasks, though sometimes references the Lab Manual for 

more detail (see the author for access to the Lab Manual). 

Preserve primary data 

Principles for the Chau Hiix Digital Archive 

Chau Hiix values the preservation of primary data, with the understanding that the data discovery 

process is filtered by the archaeologist. Assigning a new context and making a photograph of it is 

interpreting the current stasis of an excavation as data of value. The data (artifacts, photographs, 

written descriptions) of these interpretive decisions are the most primary data that can be collected 

and preserved. The archaeologist then analyzes this primary data for descriptive types and 

sequencing, and the analysis is summarized and discussed in a publication, which is considered 

secondary data, and its story can be told many ways. If secondary data are the only documents 

preserved, as in publication, then the opportunity is lost for multiple perspectives on the primary 

data: For re-analysis based on new data, for new or comparative interpretations, or for the 

application of emerging technologies. 

Preserving the primary data involves more decision making processes: What data are worthy of 

preservation; what methods are available for preserving digital data; and, how will access be 

provided? Selecting data of value to preserve is for another discussion, but note that it becomes 

confounded in the digital realm because of the ease of saving multiple versions. Chau Hiix would 

like to provide the most likely scenario for the digital data to survive and remain accessible for the 

next 100 years. Though not an unreasonable goal, it is challenged by the lack of standards for digital 

archive methodologies and ever-changing technologies. Methods for preserving digital data are the 

focus of this document, while provision for access in the future is still a problem without a longterm 

solution. 


Data should be shared 

Chau Hiix supports the principle that primary data should be shared. This would allow for data reuse 

(e.g., with new technologies), or large-scale data integration, such as meaningful analysis of 

currently unlinked sets of data from different geographic or cultural areas. But as noted by 

Silverman and Parezo, “How much of what had been in their [excavator's] notes will never have 

seen print, and how much of what had been published will demand reexamination of the primary 

records - if these are available?”(1995, p.1)(Silverman and Parezo 1995) In order for data to be 

share-able in the future it must be organized and consistent, and it must be archived with a 

stewardship plan to be accessible. 

Data are consistent and organized 

The key to successful digital archiving is thorough documentation of the data, how they were 

collected, what standards were used to describe them and how they have been managed since 

collection … all digital project archives must have three components: data, documentation, 

and an index. (AHDS 2000) 

The Chau Hiix Archive considers this a principle of consistency and organization of data, and 

implements it by maintaining the data in archival formats (TXT, TIF, JPG, PDF); documenting data 

collection and detailed description in a Manual, and providing a simple file-folder structure as an 

index. 

Consistency has to do with both the consistent use of a finite set of data formats and consistency of 

values that represent data. Without consistency it is not possible to lump data together or split data 

into groups. Consistency of data values is exemplified in controlled vocabularies and coding 

systems. If a group of artifacts are labeled variously as plaster, daub, ceramics, sherds, pots, it will 

require a good bit of re-labeling to group like objects: When sorting alphabetically on Material 

Type, the daub and plaster will not group together, neither will the ceramics, sherds, and pots. 

Similarly, if there are no standard survey codes for indicating the bottom southwest (BSW) corner 

of a building mound then much time will be spent re-plotting the survey points to identify the 

building configuration. 

In years past each excavator has collected his or her data in their own way, leading to a myriad of 

variations of software and descriptive terminology. Without consistency in data format, users of an 

archive will need access to the software that generated the various format types. Some software 

products create proprietary file formats requiring access to that specific software. Some survey and 

GIS type software require hardware keys in order to open files. Databases are useful but their files 

are often not interchangeable between products. Selecting archival formats that are hardware and 

software independent facilitates access to data. 

Archival formats. Text (.TXT) is considered the only archival format for alpha-numeric files. Text is 

the only format providing software independence. Select either Text (ASCII) or Unicode (which 

subsumes ASCII; do not use the ANSI text option as it is specific to Microsoft). Though TXT is the 

baseline format, a version of the file as created in its software application should be archived along 

with the TXT version. For example, the MS Excel file MyData.XLS is archived as well as the version 


saved-as MyData.TXT (if you save-as in the .CSV format , open it in Notepad/text-editor to save it in 

the .TXT format). 

Documentation of the TXT file is essential. Ensure that there is a header in each TXT file that 

describes what the data in the file represents. This may require more than just a delineation of the 

column headings, each heading may need explication. 

Images should be saved in TIF format. The JPG format is acceptable for images when resolution is of 

less importance or there is no other option due to available technology (digital cameras should be 

set at their highest resolution). The PDF format is acceptable, but not optimal, for scanned images of 

notebook pages and other handwritten documents but care must be taken to ensure the pages are 

readable. TIF is the optimal image format due to non-compressed high resolution. 

The goal of the archival format for image data is to save in the format with the greatest chance of 

industry format migration, and in the highest resolution possible (without compression) 

appropriate to the value of the image. For example, the photograph of a burial in situ is more 

valuable than an image scan of a field notebook. 

Organization of the archive can be accomplished with a simple folder-file structure and does not 

require a full index of all documents or a relational database structure that has to be updated and 

migrated. Create a simple Read-me.txt file that documents how the folder-file organization is laid 

out, serving as a type of site-map. 

Files are Archived with a Stewardship Plan 

In order for data to be accessible in the future it must be archived with a stewardship plan. Short 

term planning, for the next five years or until the PI retires, may be the only strategy at this time. 

Long term planning is dependent on the perceived value of the archive as a digital asset and the 

availability of institutional, organizational, or public Data Repositories. Even the best-laid plans may 

change over time. Consider the plight of Nicholson Baker who “pleads the case for saving our 

recorded heritage in its original form while telling the story of how and why our greatest research 

libraries betrayed the public trust by auctioning off or pulping irreplaceable [newspaper] 

collections” (Baker 2001). The Libraries had microfilmed all the newspapers which not only lost all 

the color print but also rendered a good number of pages unreadable due to bleed through and 

poor imaging. Diligence in stewardship does not guarantee that data will be accessible in the future. 

Identify the data steward. 

The Chau Hiix Archive is still in its formative years and so the PI (Dr. Pyburn) and the Data Manager 

maintain the archive. This is a fine plan as long as they are alive and well and still in the workforce. 

But what happens when they are gone, where does the data go? As members of a large university 

community the data archive is backed up on a well-maintained remote system, but how long will 

the archive remain on the system and who will have access once the faculty researcher retires? 

Universities have failed to step up to the need for creating Research Data Archives. While the 

university Library may be the steward of scholarly discourse (in the form of print or electronic 

publication) they have yet to step up or be mandated to steward digital data. Scholars need to be 


advocating for the creation of research data repositories whether they be institutional or 

organizational, public or privately funded. For a repository example, see AHDS (the Arts and 

Humanities Data Service), for the latest discussion on the future of archived data see the Blue 

Ribbon Task Force on Sustainable Digital Preservation and Access (BRTF-SDPA 2010). 

Stewardship tasks 

While analog documents age in a slow and foreseeable way, aging occurs rapidly in the digital 

realm. Digital stewardship involves three areas of yearly monitoring and activity: Format migration 

and media transfer, data verification, and accessibility. 

Format migration and media transfer. The data steward must monitor the need for data 

migration from one format to the next and for transfer from one media to the next generation. 

Archival formats evolve and may require data to be migrated from an old standard to a new 

standard. Alphanumeric data in the TXT format (ASCII-based) is safe because its next generation is 

Unicode which is backwardly compatible to ASCII and expands the character set to include 

requirements from other languages. Image formats, however, are in flux and need to be closely 

monitored. For example, the PDF format is proprietary to Abode. Should the company make a bad 

business decision and close down, or be acquired, there is no guarantee that the format will remain 

viable in its current form. A migration path may only be available for a short period of time, after 

which it may take considerable expense to locate and execute the migration. 

Similarly the media on which data are stored need to be monitored for viability. Floppy drives and 

diskettes have become obsolete and it is costly to find services that will transfer data on older 

media to new media. In addition, media can simply fail mechanically, thus the need for backup 

copies on various media types. 

Data verification. Transfer of data to a new medium can fail, partially fail, or cause data 

corruption, and so data verification is essential. Migration of data to a new format can cause 

unanticipated changes in the data, so the data need to be carefully verified during a migration 

process. Even if the data has not been migrated or transferred to new media, the data should be 

verified on a yearly basis as this will uncover the failure of a particular media, storage device, or 

location . Randomly opening several documents in different formats would be sufficient for 

verification. 

Over time multiple professionals may analyze portions of the data set and identify errors. Some 

process must be put in place to verify data errors and incorporate corrections into the archive. 

Accessibility. Digital data has to physically reside somewhere, on some server, on some storage 

device. Repositories can be private, institutional/industry, or national/international. Undoubtedly 

all researchers have private copies of all their data and can provide access, that is, if it is organized 

and regularly verified. In the context of this document, the university, as an institution, has 

provided a remote backup and storage system. But access is still only available through private 

contact with the researcher. Other institutions (libraries, societies, and organizations) have failed as 

yet to manage such data in a way that a wider audience could have access. 


Industrial repositories include electronic publishers, web services (similar to FLICKR and 

Facebook), or what are called “cloud” type services that offer remote data backup and archiving. 

But at issue with industries is cost as well as their viability as a company. The private sector has no 

mandate to continue to provide service in perpetuity. 

Lots of Copies Keeps Stuff Safe (LOCKSS) is both a strategy and a program (StanfordUniversity 

2008). While the Stanford program may be beyond the scope of “small science” projects, the 

strategy is useful if well managed. The data steward needs to track the locations of the various 

copies to maintain version control and to ensure data migration and media transfer. Chau Hiix 

maintains stewardship over five copies of the archive, six if you include the print version. 

Other national or international repositories have yet to be established for “small science” projects 

such as Chau Hiix but the Arts and Humanities Data Service (AHDS 2000) provides a good model. 

History of Chau Hiix Digital Data 

Simple Not Easy: The digital process 

Prior to 1996 all Chau Hiix recordkeeping was done with paper and pencil. Digital data collection 

began with replacing pencil and paper with what was effectively a typewritten record, thus 

exchanging penmanship readability with typing errors. The portable computers were physically 

large, heavy, dependent on 3.5 diskettes for data storage, and expensive for the small budget of a 

university-based project. In addition, operating systems and software were not as user friendly as 

today, and both students and staff were inexperienced in their use. There was not money to buy 

spreadsheet software or hire a database programmer, and there was not time to teach staff and 

students to use them reliably. These first Chau Hiix digital data records were created in a Text 

Editor (creating TXT files). Every data record was therefore entered as a single line in a text file 

with a consistent and documented syntax listed at the top of the file. 

Sample of a 1996 digital data file follows. This file exemplifies the Save-as Text archival version of 

all digital data. 

INVENTORY LITHIC/CERAMIC CHAU HIIX EXCAVATION 48 in 1996 

Data syntax= provenience,material,date,count,weight-in-ounces,excavator-initials,#-ofbags,notes 

(use x comma for missing information) 

che-48-1-2-96,lithic,6-Mar-96,8,x,rs/cw,1,x, 

che-48-1-20-96,ceramic,7-Mar-96,108,40,cw/rs,1,treefall cache 

che-48-1-21-96,ceramic,7-Mar-96,103,44,x,1,treefall cache 

che-48-1-23-96,ceramic,7-Mar-96,68,32,rs/gw,3of3,x, 



che-48-1-29-96,lithic,18-Mar-96,10,3,x,1,backdirt 

Note: Below is an explanation of provenience number construction. 


cht-70-2-1-99 

| | | | | | 

a b c d e f 

a. Location: CH= Chau Hiix; XT= Xtabai; NR= NaRob 

b. Type of provenience: e=excavation, t=testpit, p= posthole, s=surface collection. 

c. Provenience number for reference and sync with survey points. 

d. Operation number for multiple excavations/years at the same survey point. 

e. Context numbers are assigned by the excavator. 

f. Year code (YY) is redundant but useful for workflow and analysis. 

During the field season these text lines would be visually scanned by the Lab Manager for data 

input errors. Since the comma is used as the data delimiter (i.e., separator used to indicate a new 

unit of data). Misplacement of the delimiter/comma is the source of most errors, such as separating 

multiple Excavator-initials (use “cjb/glp” instead of “cjb, glp”) or within a Note (use 

“backdirt/burial45” instead of “backdirt, burial45”). 

Post-season the Data Manager imports the data files into a spreadsheet for further error checking. 

Typographic errors are constantly a concern and the spreadsheet sorting function provides a good 

tool for checking the consistency of data entry. Data files are then merged with seasonal files from 

all field seasons. Using the spreadsheet’s Save-as function, a new TXT file is created as the archival 

copy of this total catalog of data. Spreadsheets are then made available for analysis. 

By 2003 students and staff had enough computer experience for data entry to be done directly into 

a spreadsheet. The comma is still used as the delimiter for the archival TXT copy of the file, so data 

entry errors are still related to use of the comma within a cell, as well as typing and consistency 

errors. The Data Manager continues to follow the same post-season procedure: check for errors, 

merge all seasonal files with previous season’s files, make the spreadsheets available for analysis, 

and Save-as a TXT file to add to the archive. 

As students, staff and professionals who work at Chau Hiix become more digitally sophisticated, so 

does the hardware and software they employ, as well as the number of files they create. The basic 

data archive plan, however, remains the same. The archived TXT files of 2009 look exactly like the 

archived files of 1996. 

Simple Archive: Save-as Text, Print, Backup 

The archive plan for Chau Hiix digital data is both conceptually and deceptively simple: Save-as text, 

print, and make three digital backup copies to store in separate locations. 

Not Easy in Details 

Implementing this simple plan is not easy; the devil is in the details. Each step is filled with 

problems and decisions, and the archival processes still do not guarantee the accessibility 

of the data in perpetuity. 


There are two sets of tasks in the digital data process: The Lab Manager tasks for collecting the 

data, and the Data Manager tasks for archiving the data. 

Lab Manager Tasks for Collecting Digital Data. The Lab Manager is responsible for standardized 

file formats and names, consistency of data entry and ongoing backups during the data collection 

process. Use the following rules: 

File Formats 

Format refers to the three-letter extension that follows the period in a file name. Every 

software application uses an extension for its data files, most software offer choices of 

format under the Save-as option. Chau Hiix uses the following software: Microsoft Excel 

(format is .XLS), Microsoft Word (format is .DOC or .DOCX), and TDS Survey Pro 

(proprietary formats .AR5, .CR5, .RW5, but with a Save-as .TXT option). However, data may 

be collected in any software as long as there is an option to Save-as or Export the data to an 

archival format. Archival formats are limited to TXT or Unicode, TIF, JPG, PDF. 

File Names 

There are generally two types of files: Data and image. 

Data files that are required are spreadsheets established and explained in the Lab Manual. 

Names of required spreadsheet data files for each Chau Hiix excavation season are: 

Personnel-Notebooks-YY.xls 

Provenience-Masterlist-YY.xls 

Inventory-Artifacts-YY.xls 

Inventory-Burial-YY.xls 

Survey-Gridpoints –YY.xls 

Survey-TotalPoints-YY.xls 

Other data files may be generated as needed, such as Survey point collection files: 

TDS-mainplatform-07.cr5 

TDS-lagoon-07.cr5 

Data files are saved in both Data-entry format (of the specific software being used, e.g. Excel 

.XLS) and Backup format (archival format such as TXT or Unicode). Both the data-entry and 

back-up format of a file have the exact same filename and are differentiated by the threeletter 

format extension. For example: 

Personnel-Notebooks-07.xls 

Personnel-Notebooks-07.txt 

TDS-mainplatform-07.cr5 

TDS-mainplatform-07.txt 


Image files are either digital photographs or document scans (notebook pages, drawings, 

forms, cards, etc.) Digital photos are collected in a Photograph folder and placed in 

subfolders according to context type: Artifacts, Drawings and Maps, Proveniences, Camp- 

Scenes, etc. Files are named according to their content, beginning with Provenience number, 

or collected in a specific Provenience folder. 

str150-plan.tif (plan view of structure 150) 

str7-032905.jpg (structure 7 on Mar 29, 2005) 

che-71-1-15-98-lithics.tif (debitage from this context) 

cht-141-1-1-98-rim.tif (ceramic rim sherd) 

Or folders named: 

che-18-1-8-03-ceramic (large cache of sherds) 

che-16-x-x-03-lithic (contains multiple operations and contexts) 

che-22-excavations (general views of the excavation in progress) 

Document scans follow a similar filename-folder scheme except for Notebook page scans. 

Notebooks, designated for scanning by the PI, have filenames that begin with a two digit 

year YY and then an arbitrary two digit personnel number assigned at the beginning of the 

season to each field member. This is followed by a three digit number for the first notebook 

page in the file. Notebooks scans are limited to 8 pages per file to keep the file size 

manageable. For example, in 2007 Dr. Pyburn was assigned personnel number 01 so the 

scanned files of her field notebook were named: 

0701001.tif (note this file contains pages 1 through 8) 



0701027.tif etc. 

All other files that are created should be named following the recommendations adapted 

from the Media Vault. The same guidelines apply to the names of Folders. 

File/folder names should not contain blank spaces. File/folder names should only 

include the letters A-Z and a-z, the numbers 0-9, plus underscores and hyphens. 

Underscores, hyphens, and zeros cannot occur as the first character of the name of a 

file/folder. File/folder names are considered case sensitive (because they will be in 

some environments), but best practice is to not use case variation in considering 

uniqueness of naming. File/folder names should not exceed 64 characters, including 

file extension. Shorter is better. (MediaVault 2009) 

(Note that filenames that begin with zero are a problem when using the MS Excel and the 

sorting function, so do not name files with leading zeros.) 


Data Entry 

Accuracy and consistency of data entry requires constant attention. Most fields in the 

required spreadsheets use controlled vocabularies for data entry options which are listed 

and explained in the Lab Manual. (Note that it is possible to create drop down menus for 

populating spreadsheet cells, but the Lab Manager must understand the implementation of 

this function in case changes need to be managed in the field.) The Lab Manager is wise to 

check for data errors on a daily basis, making them easier to identify and correct. Data 

errors generally fall into three categories: typographical, auto-fill related, and explicative. 

Typographical errors are most often found by doing a sorting process on each column and 

checking for inconsistencies. Typographical errors also occur when capturing survey data in 

the Total Station. The surveyor should be asked to edit these errors, or “little finger 

mistakes” as one surveyor put it, as the Shot Description field can be quite cryptic. 

Auto-fill errors relate to the spreadsheet software trying to be “helpful” and auto-fill cells 

based on previous data entry. (Note that it is possible to turn OFF auto-fill but again, the Lab 

Manager must understand the management of this function.) These errors are much harder 

to uncover, such as the date-of-collection becoming the date of data entry. Those doing data 

entry are asked to step-away from the task every twenty minutes to regain focus and help 

minimize this “helpful” problem. Daily error checking by the Lab Manager is the best 

deterrent. 

Explicative data errors refer to the excitement of data discovery and attempts during the 

data entry process to provide as much information as possible or to begin the analysis 

process. For example, those doing data entry might see a particularly interesting lithic and 

try to describe it in the Material column as “stemmed macro blade retouched”, or a ceramic 

as a “diagnostic rim sherd”. The information in these types of entries belongs in the Notes 

field where it will not interfere with the standard vocabulary for Material type and thus 

cause sorting problems. Consistency of terminology (and spelling) is essential to being able 

to sort the data. This holds true for survey description also. Survey descriptive codes must 

be consistent and documented. New descriptors, codes, or vocabulary can be added as 

necessary but with discussion and agreement by all professionals. 

Backups 

Data files that have been modified need to be backed up each day, or more often if there is 

extensive data entry or many changes in the personnel doing data entry. In order to control 

the file versions, file naming and a backup schedule should be established. Use a file name 

beginning with DB for daily backups (or DB1, DB2, DB3, etc. for more frequent backups) 

followed by a filename designator and then a date in the form of DDMMYY. For example, the 

Inventory-Artifacts-07.xls file would have daily backup files named: 

DB-IA-120307.xls 


DB1-IA-140307.xls 


DB2-IA-140307.xls 


These daily backups could be deleted once a weekly backup file is created and checked 

against the latest version of the daily backup. Weekly backups should be retained as part of 

the seasonal field record to provide for data entry error analysis by the Data Manager at the 

end of the season. The weekly backup (WB) would use a similar filename for version 

control: 

WB-IA-160307.xls 

For version control, designate the last backup at the end of the field season (EOS) as: 

EOS-IA-220407.xls 

Each day the backup files should be copied to a portable storage device that is kept with the 

Lab Manager’s passport. In the event of a disaster (fire, flood, hurricane, etc.) the Lab 

Manager is most likely to remember to grab the passport and thus the data also. 

Data Manager Process for Archiving Digital Data. The Data Manager is responsible for creating 

and maintaining the data archive. The maintenance tasks are best accomplished as soon as possible 

after the end of the field season; the more time that passes the less likely it is that personnel will 

remember details if there are questions or anomalies. 

Organize the Archive 

Controlled vocabularies are the foundation of the archive as well as a major source of data 

entry errors. Vocabulary (or values allowed in each cell) must be consistent to provide for 

accurate data sorting when using the spreadsheets for analysis. Unless they are experienced 

in sorting data, consistency in data entry is the most difficult principle to convey to the field 

personnel. This includes the surveyor who may not be experienced in the use of GIS type 

software and fails to consistently label each point with standardized description codes. 

Building and maintaining the controlled vocabularies is a primary task for the Data 

Manager. Before each field season the Data Manager updates and explicates the controlled 

vocabulary lists in the Lab Manual. 

Material Type is an example of a controlled vocabulary: Name them pots, sherds or 

ceramics, but pick one term to use consistently for each concept. The Material Type (and 

sub-types) terms in the controlled vocabulary used by Chau Hiix are: 

Historic (ceramic, glass, metal) 

Ceramic (polychrome, mendhole, netweight, partial-vessel) 

Shell (marine, freshwater) 

Bone (human, faunal) 

Lithic (chert, groundstone, jade, obsidian, hematite) 

Plaster (Painted) 

Carbon 


Soil (chemical, flotation, waterscreen) 

Misc (wood, stone, coral) 

All controlled vocabularies are constantly in review for additions or clarifications. Sub-types 

of a vocabulary term are not required initially but may be added or separated into more 

detail as the analysis phase evolves. 

The folder structure of the Chau Hiix Archive is simple. The Data Manager establishes the 

folder structure as well as all the rules and tasks for the files it contains: File formats; file 

naming schemes; processes for data entry; and backups. In addition, the Data Manager, in 

concert with the PI, maintains controlled vocabularies that are used to fill in each cell in the 

spreadsheets. Rules, tasks and vocabularies are all reviewed and updated on a yearly basis 

as influenced by the Lab Manager’s experience and as demanded by changes in both data 

and technology. 

The Chau Hiix Archive consists of the following Folders: 

0-LogArchiveChanges 

1-Manuals 

2-Personnel 

3-Notebooks 

4-Proveniences 

5-Artifacts 

6-Survey 

7-Visuals 

8-Analyses 

9-Documents 

10-SeasonalFiles 

FORMS 

PROBLEMS 

SOFTWARE 

Populating the Archive 

The Data Manager collects all the digital data at the end of the field season from the Lab 

Manager and begins the process of data validation, error-checking, documentation, and 

storing archival versions. See the checklist that follows for the details of this process. 

Locations for the storage of the Chau Hiix Digital Archive include: onsite at Chau Hiix, in 

country with the department of archaeology, in the PI office, in the Data Manger office, and 

on a university backup system. The PRINT version of the TXT files is stored in a fireproof 

cabinet in the office of the PI. 

Maintain the Archive 

The five Archive locations are maintained by the Data Manager. This entails replacing the 

whole folder system with a new version at least after each field season and more often in 

the case of more frequent inter-season changes or additions, typically analysis data. At least 


three full versions of the whole archive are maintained on the university backup system, 

identified by folder name and version data (YYMMDD, and this date syntax is documented 

in the LogArchiveChanges files.) For example, archive folders are named: 

ChauHiixArchive-070621 



When a new version of the archive is created, random files need to be opened to ensure the 

copy process has been completely successfully. If a new archive folder is not created after a 

two year period, the Data Manager should open the latest version and randomly check files 

for data and media viability. 

The Data Manager is responsible for monitoring the viability of both archival formats and 

media integrity and identifying the need for migration of data format, storage media, or 

storage location. The need for migration could be triggered by technology obsolescence, and 

upgrades or retirement of formats, software, operating systems, or location accessibility. 

Digital Archive Checklist of Tasks 

Pre- season: 

1. Lab Manual is reviewed and updated as necessary and copies of all required Master files 

are made available for reference in the new field season. 

2. Prepare blank copies of required Master files for data entry in current season. 

3. Assign an arbitrary two-digit number to each field season participant and record it in 

the new, blank Personnel-notebook spreadsheet. 

Daily during the field season: 

1. Make daily backup files (DB) of each file that has been modified. 

2. Sort by columns each required spreadsheet to identify and correct errors. 

3. Copy daily backup files to an external storage device and store with your passport. 

Weekly during the field season: 

1. Make weekly backup files (WB) of each file. 

2. Sort by columns each required spreadsheet to identify and correct errors. 

3. Copy weekly backup files to an external storage device and store with your passport. 

End of field season: 

1. Copy all WB files to TWO different external storage devices, leaving master copies on 

the Lab computer. 


Post-season: 

2. Create a final EOS file of each spreadsheet representing the total data entry for the 

season. Make two copies of the EOS files and store with the WB files on external devices. 

3. Write a short report documenting problems, anomalies or suggestions regarding these 

files. 

1. Copy each EOS file and rename it according to its Master file name but append YY for 

year and the letter F for Field. For example: EOS-IA-220407.xls is copied to Inventory- 

Artifacts-07F.xls. This file becomes part of the permanent Archive in folder 10- 

SeasonalFiles. (See list of Archive folders above.) 

2. Make a second copy each of EOS file and rename it according to its Master file name but 

append YY for year and the letter P for Postseason. This is the Postseason working file 

where changes or edits are made. For example: EOS-IA-220407.xls is copied to Inventory- 

Artifacts-07P.xls. 

3. Sort by columns the spreadsheets (filename–YYP.xls) to identify and correct errors. 

4. When confident with data validity, combine each of these files (filename–YYP) with its 

previous season’s Master file to become the next/future season’s Master file. For 

example: Inventory-Artifacts-07P.xls is combined with Inventory-Artifacts-05P.xls and the 

new Inventory-Artifacts-07P.xls becomes part of the permanent Archive in folder 5- 

Artifacts. 

5. The Master file from the previous 2005 season, Inventory-Artifact-05P.xls, is then moved 

to folder 10-SeasonalFiles subfolder for the 2005 field season. The folder 10-SeasonalFiles 

subfolder 2005 now contains the two files: Inventory-Artifact-05F.xls and Inventory- 

Artifact-05P.xls 

6. Make a TXT file for every Master file, adding a data syntax header to each one, then 

PRINT each TXT file. 

7. In the current year’s subfolder in folder 10-SeasonalFiles subfolder, make a TXT copy of 

every file. 

8. Delete all DB and WB files as they are now represented by the file with the –YYF 

designation. Retain the EOS files as insurance against post season data editing errors. 

9. File all other seasonal files in the appropriate Archive folders. For example, the season 

notebook scans are put in folder 3-Notebooks, and photographs are put in folder 7- 

Visuals. 

10. Delete any extraneous files. 


Yearly: 

11. Write a brief document (.TXT) of problems, anomalies or suggestions from the 

postseason archive process. File this document in folder 0-LogArchiveChanges. 

12. Copy the whole Archive to THREE external devices or locations. Randomly open files on 

the external devices to ensure data viability. Deposit the print versions of the TXT files 

in a designated fireproof location. 

1. Verify the functioning of the storage media and/or location by opening files. 

2. Monitor the need to transfer media or migrate data. 

3. Monitor changes in accessibility or access rights. 

4. Provide for the addition of new analyses or data error correction. 

References cited: 

AHDS, Arts and Humanities Data Service. 2000. Digital Archives from Excavation and Fieldwork 

Guide to Good Practice. http://ads.ahds.ac.uk/project/goodguides/excavation/sect82.html. 

Baker, Nicholson. 2001. Double Fold: Libraries and the Assault on Paper. New York: Vintage Books. 

BRTF-SDPA, Blue Ribbon Task Force on Sustainable Digital Preservation and Access. 2010. 

Sustainable Economics for a Digital Planet: Ensuring Long-Term Access to Digital 

Information. http://brtf.sdsc.edu/index.html. 

MediaVault. 2009. Acceptable Characters in File Names. Naming Your Files, 

http://mediavault.wordpress.com/documentation/. 

Silverman, Sydel, and Nancy J. Parezo. 1995. Preserving the Anthropological Record. New York: 

Wenner-Gren Foundation for Anthropological Research. 

StanfordUniversity. 2008. LOCKSS, Lots of Copies Keeps Stuff Safe. 

http://lockss.stanford.edu/lockss/Home.

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