This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Paleoceanography. The data include parameters of paleoceanography with a geographic location of Western Pacific Ocean. The time period coverage is from 5180 to 2760 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

This dataset is about book subjects. It has 3 rows and is filtered where the books is Software development for engineers with C, Pascal, C++, Assembly Language, Visual Basic, HTML, JavaScript, and Java. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

NLPCoreTeam/ruCoir-CodeSearchNet-java-qrels dataset hosted on Hugging Face and contributed by the HF Datasets community

Context

It is a dataset including information on amazon job opening around the world from June 2011 to March 2018. This dataset is collected using Selenium and BeautifulSoup by scraping all of the jobs for Amazon job site.

Content

What's inside is more than just rows and columns.

Column descriptors

the dataset has seven columns in it, let us take a look at each columns briefly:- 1. First column represents the Row numbers.

1. second columns represent the Tile => A job title is a simple description that refers to the responsibilities of a job and the level of the position.

2. third columns represent the Location => Job location means any other location to which an employee is assigned to report which is not his reporting centre.

3. fourth columns represent the Posting date => This date is the deadline for applicants to turn in the job application and other required materials as outlined in the job posting. The closing date is a milestone in the hiring process because all events before it builds up to it, and all events that follow are predicated on it passing.

4. fifth columns represent the Description => A job description or JD is a written narrative that describes the general tasks, or other related duties, and responsibilities of a position.

5. sixth columns represent the Basic qualification => Basic qualifications are the minimum qualifications that a candidate must possess in order to be initially considered for the position. Applicants who do not demonstrate that they meet the basic qualifications for a position cannot be considered for that role.

6. seventh column represent the preferred qualification => Preferred Qualifications are those an applicant does not have to possess in order to be considered a “candidate” for the position; however, they are seen as “good to have” qualities that will lead to a higher level of success for the applicant.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Problem Statement : Find number of job openings in Bangalore, IN and in Seattle,US? Print the Number of Job opening in Bangalore and Seattle as Integer value. Output Format : CountBangalore CountSeattle

Problem Statement : What are the total number of job openings related to Computer Vision ?

Note:For finding the job related to computer vision check the Job Title column. Print the count as the Integer Value Output Format : Count

Problem Statement : Find the number of job openings in Canada? Print the count as the Integer Value

Note: Here you should analyse the country code in location feature.( you can use dictionary for analyse part ). Output Format : Count

Problem Statement : Find the month having most job openings in Year 2018 ? Print the month (Month Name i.e January, February, March) and Number of job opening as Integer Value Output Format : MonthName Count

Problem Statement : Find the number of job openings are present if applicant have Bachelor degree? Print the count as Integer value

Note : Here we will use the BASIC QUALIFICATIONS feature to find out whether bachelor degree for Job is required or not. Keywords that can be used are 'Bachelor', 'BS' and 'BA'. Output Format : Count

Problem Statement : Among Java, C++ and Python, which of the language has more job openings in India for Bachelor Degree Holder? Print the Language(i.e Java,C++,Python) and number of job opening as integer value.

Note : Here we will use the BASIC QUALIFICATIONS feature to find out whether bachelor degree for Job is required or not. Keywords that can be used are 'Bachelor', 'BS' and 'BA' and we will use the BASIC QUALIFICATIONS feature to find out whether Language is required for the job or not.Keywords that is used for language searching are 'Java','C++' or 'Python'.(there case should not be changed). Output Format : Language Count

Problem Statement : Find the country does Amazon need the most number of Java Developer? Print the Country(Country Shortcut as given in Dataset) and number of job opening as integer value

Note :Here we will use the BASIC QUALIFICATIONS feature to find out whether Java is required for the job or not.Keyword is used is 'Java'.(here case should not be changed). Output Format : Country Count

The dataset comprises developer test results of Maven projects with flaky tests across a range of consecutive commits from the projects' git commit histories. The Maven projects are a subset of those investigated in an OOPSLA 2020 paper. The commit range for this dataset has been chosen as the flakiness-introducing commit (FIC) and iDFlakies-commit (see the OOPSLA paper for details). The commit hashes have been obtained from the IDoFT dataset.

The dataset will be presented at the 1st International Flaky Tests Workshop 2024 (FTW 2024). Please refer to our extended abstract for more details about the motivation for and context of this dataset.

The following table provides a summary of the data.

Slug (Module) FIC Hash Tests Commits Av. Commits/Test Flaky Tests Tests w/ Consistent Failures Total Distinct Histories

TooTallNate/Java-WebSocket 822d40 146 75 75 24 1 2.6x10^9

apereo/java-cas-client (cas-client-core) 5e3655 157 65 61.7 3 2 1.0x10^7

eclipse-ee4j/tyrus (tests/e2e/standard-config) ce3b8c 185 16 16 12 0 261

feroult/yawp (yawp-testing/yawp-testing-appengine) abae17 1 191 191 1 1 8

fluent/fluent-logger-java 5fd463 19 131 105.6 11 2 8.0x10^32

fluent/fluent-logger-java 87e957 19 160 122.4 11 3 2.1x10^31

javadelight/delight-nashorn-sandbox d0d651 81 113 100.6 2 5 4.2x10^10

javadelight/delight-nashorn-sandbox d19eee 81 93 83.5 1 5 2.6x10^9

sonatype-nexus-community/nexus-repository-helm 5517c8 18 32 32 0 0 18

spotify/helios (helios-services) 23260 190 448 448 0 37 190

spotify/helios (helios-testing) 78a864 43 474 474 0 7 43

The columns are composed of the following variables:

Slug (Module): The project's GitHub slug (i.e., the project's URL is https://github.com/{Slug}) and, if specified, the module for which tests have been executed.

FIC Hash: The flakiness-introducing commit hash for a known flaky test as described in this OOPSLA 2020 paper. As different flaky tests have different FIC hashes, there may be multiple rows for the same slug/module with different FIC hashes.

Tests: The number of distinct test class and method combinations over the entire considered commit range.

Commits: The number of commits in the considered commit range

Av. Commits/Test: The average number of commits per test class and method combination in the considered commit range. The number of commits may vary for each test class, as some tests may be added or removed within the considered commit range.

Flaky Tests: The number of distinct test class and method combinations that have more than one test result (passed/skipped/error/failure + exception type, if any + assertion message, if any) across 30 repeated test suite executions on at least one commit in the considered commit range.

Tests w/ Consistent Failures: The number of distinct test class and method combinations that have the same error or failure result (error/failure + exception type, if any + assertion message, if any) across all 30 repeated test suite executions on at least one commit in the considered commit range.

Total Distinct Histories: The number of distinct test results (passed/skipped/error/failure + exception type, if any + assertion message, if any) for all test class and method combinations along all commits for that test in the considered commit range.

No description is available. Visit https://dataone.org/datasets/863fd58383f799c99c8cac490ac49b21 for complete metadata about this dataset.

This dataset is about: (Table 4) Relationship between core top ages and accumulation rates in surface sediments from the Ontong Java Plateau. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.857285 for more information.

This dataset consists of Eclipse's static analysis performed on 10 Java projects.

For each .java file of a test project, we ran a static analysis using Eclipse JDT Core allows us to retrieve all the possible function calls based on typing/imports for a given completion site). Each java project has three files structured as follows:

• *.json file. The file contains all the method declarations of the project and the function calls in their body. For each function call, the file lists all the possible function call that could have been made at that place in the source code. For practical purposes, we splitted this file into two text files.
• *_sequences.txt file. This file consists of all the method declaration + function call sequences in the project. The last element of each line corresponds to a completion site.
• *_proposals.txt file. Each line is made of the function-call suggestions retrieved by static analysis for the corresponding line in the *_sequences.txt file.

The corpus was used for the experiments in the paper Combining Code Embedding with Static Analysis for Function-Call Completion.

Github repository to replicate the experiments: https://github.com/mweyssow/cse-saner

This dataset is about: Stable isotope data of sediment core SA-2-102, Segara Anakan lagoon, Central Java, Indonesia. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.908777 for more information.

This is a dataset about the usage of properties and datatypes in the Web Data Commons RDFa, Microdata, Embedded JSON-LD, and Microformats Data Sets (November 2018) based on the Common Crawl November 2018 archive. The dataset has been produced using the RDF Property and Datatype Usage Scanner v2.1.1, which is based on the Apache Jena framework. Only RDFa and embedded JSON-LD data were considered, as Microdata and Microformats do not incorporate explicit datatypes.

Dataset Properties

• Size: 22.2 MiB compressed, 569.6 MiB uncompressed, 2 608 325 rows plus 1 head line determined using gunzip -c measurements.csv.gz | wc -l
• Parsing Failures: The scanner failed to parse 4 135 842 triples (~0.077 %) of the source dataset (containing 5 367 569 192 triples).
• Content:
  • CATEGORY: The category (html-embedded-jsonld or html-rdfa) of the Web Data Commons file that has been measured.
  • FILE_URL: The URL of the Web Data Commons file that has been measured.
  • MEASUREMENT: The applied measurement with specific conditions, one of:
    • UnpreciseRepresentableInDouble: The number of lexicals that are in the lexical space but not in the value space of xsd:double.
    • UnpreciseRepresentableInFloat: The number of lexicals that are in the lexical space but not in the value space of xsd:float.
    • UsedAsDatatype: The total number of literals with the datatype.
    • UsedAsPropertyRange: The number of statements that specify the datatype as range of the property.
    • ValidDateNotation: The number of lexicals that are in the lexical space of xsd:date.
    • ValidDateTimeNotation: The number of lexicals that are in the lexical space of xsd:dateTime.
    • ValidDecimalNotation: The number of lexicals that represent a number with decimal notation and whose lexical representation is thereby in the lexical space of xsd:decimal, xsd:float, and xsd:double.
    • ValidExponentialNotation: The number of lexicals that represent a number with exponential notation and whose lexical representation is thereby in the lexical space of xsd:float, and xsd:double.
    • ValidInfOrNaNNotation: The number of lexicals that equals either INF, +INF, -INF or NaN and whose lexical representation is thereby in the lexical space of xsd:float, and xsd:double.
    • ValidIntegerNotation: The number of lexicals that represent an integer number and whose lexical representation is thereby in the lexical space of xsd:integer, xsd:decimal, xsd:float, and xsd:double.
    • ValidTimeNotation: The number of lexicals that are in the lexical space of xsd:time.
    • ValidTrueOrFalseNotation: The number of lexicals that equal either true or false and whose lexical representation is thereby in the lexical space of xsd:boolean.
    • ValidZeroOrOneNotation: The number of lexicals that equal either 0 or 1 and whose lexical representation is thereby in the lexical space of xsd:boolean, and xsd:integer, xsd:decimal, xsd:float, and xsd:double.
    Note: Lexical representation of xsd:double values in embedded JSON-LD got normalized to always use exponential notation with up to 16 fractional digits (see related code). Be careful by drawing conclusions from according Valid… and Unprecise… measures.
  • PROPERTY: The property that has been measured.
  • DATATYPE: The datatype that has been measured.
  • QUANTITY: The count of statements that fulfill the condition specified by the measurement per file, property and datatype.

Preview

"CATEGORY","FILE_URL","MEASUREMENT","PROPERTY","DATATYPE","QUANTITY"
"html-rdfa","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-rdfa.nq-00000.gz","UnpreciseRepresentableInDouble","https://www.w3.org/2006/vcard/ns#longitude","https://www.w3.org/2001/XMLSchema#float","4"
"html-rdfa","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-rdfa.nq-00000.gz","UnpreciseRepresentableInDouble","https://www.w3.org/2006/vcard/ns#latitude","https://www.w3.org/2001/XMLSchema#float","4"
"html-rdfa","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-rdfa.nq-00000.gz","UnpreciseRepresentableInDouble","https://purl.org/goodrelations/v1#hasCurrencyValue","https://www.w3.org/2001/XMLSchema#float","6"
"html-rdfa","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-rdfa.nq-00000.gz","UnpreciseRepresentableInDouble","http://purl.org/goodrelations/v1#hasCurrencyValue","http://www.w3.org/2001/XMLSchema#floatfloat","8"
"html-rdfa","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-rdfa.nq-00000.gz","UnpreciseRepresentableInDouble","https://opengraphprotocol.org/schema/latitude","http://www.w3.org/2001/XMLSchema#string","30"
…
"html-embedded-jsonld","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-embedded-jsonld.nq-00734.gz","ValidZeroOrOneNotation","http://schema.org/numberOfItems","http://www.w3.org/2001/XMLSchema#integer","40"
"html-embedded-jsonld","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-embedded-jsonld.nq-00734.gz","ValidZeroOrOneNotation","http://schema.org/ratingValue","http://www.w3.org/2001/XMLSchema#integer","431"
"html-embedded-jsonld","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-embedded-jsonld.nq-00734.gz","ValidZeroOrOneNotation","http://schema.org/width","http://www.w3.org/2001/XMLSchema#integer","122"
"html-embedded-jsonld","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-embedded-jsonld.nq-00734.gz","ValidZeroOrOneNotation","http://schema.org/minValue","http://www.w3.org/2001/XMLSchema#integer","63"
"html-embedded-jsonld","http://data.dws.informatik.uni-mannheim.de/structureddata/2018-12/quads/dpef.html-embedded-jsonld.nq-00734.gz","ValidZeroOrOneNotation","http://schema.org/pageEnd","http://www.w3.org/2001/XMLSchema#integer","139"

Note: The data contain malformed IRIs, like "xsd:dateTime" (instead of probably "http://www.w3.org/2001/XMLSchema#dateTime"), which are caused by missing namespace definitions in the original source website.

Reproduce

To reproduce this dataset checkout the RDF Property and Datatype Usage Scanner v2.1.1 and execute:

mvn clean package
java -jar target/Scanner.jar --category html-rdfa --list http://webdatacommons.org/structureddata/2018-12/files/html-rdfa.list November2018
java -jar target/Scanner.jar --category html-embedded-jsonld --list http://webdatacommons.org/structureddata/2018-12/files/html-embedded-jsonld.list November2018
./measure.sh November2018
# Wait until the scan has completed. This will take a few days
java -jar target/Scanner.jar --results ./November2018/measurements.csv.gz November2018

zip file containing the collection of ReadMe files contained in the 50 projects listed. The correspondence is as follows:android-gpuimage => 101.datansj_seg => 102.datarrow => 103.datatmosphere => 104.datautorest => 105.datblurkit-android => 106.datbytecode-viewer => 107.datcglib => 108.datdagger => 109.datExpectAnim => 110.datgraal => 111.datgraphql-java => 112.dathalo => 113.datHikariCP => 114.dathttp-request => 115.datinterviews => 116.datjava-learning => 117.datJava-WebSocket => 118.datjeecg-boot => 119.datjeesite => 120.datJFoenix => 121.datjna => 122.datjoda-time => 123.datjodd => 124.datJsonPath => 125.datjunit4 => 126.datlibrec => 127.datlight-task-scheduler => 128.datmal => 129.datmall => 130.datmosby => 131.datmybatis-plus => 132.datnanohttpd => 133.datNullAway => 134.datparceler => 135.datPermissionsDispatcher => 136.datPhoenix => 137.datquasar => 138.datrequery => 139.datretrofit => 140.datretrolambda => 141.datSentinel => 142.datsimplify => 143.datswagger-core => 144.dattcc-transaction => 145.datsymphony => 146.dattestcontainers-java => 147.datUltimateRecyclerView => 148.datweixin-java-tools => 149.datwire => 150.dat

The identification and quantification of natural carbon (C) sinks is critical to global climate change mitigation efforts. Tropical coastal wetlands are considered important in this context, yet knowledge of their dynamics and quantitative data are still scarce. In order to quantify the C accumulation rate and understand how it is influenced by land use and climate change, a palaeoecological study was conducted in the mangrove-fringed Segara Anakan Lagoon (SAL) in Java, Indonesia. A sediment core was age-dated and analyzed for its pollen and spore, elemental and biogeochemical compositions. The results indicate that environmental dynamics in the SAL and its C accumulation over the past 400 years were controlled mainly by climate oscillations and anthropogenic activities. The interaction of these two factors changed the lagoon's sediment supply and salinity, which consequently altered the organic matter composition and deposition in the lagoon. Four phases with varying climates were identified. While autochthonous mangrove C was a significant contributor to carbon accumulation in SAL sediments throughout all four phases, varying admixtures of terrestrial C from the hinterland also contributed, with natural mixed forest C predominating in the early phases and agriculture soil C predominating in the later phases. In this context, climate-related precipitation changes are an overarching control, as surface water transport through rivers serves as the "delivery agent" for the outcomes of the anthropogenic impact in the catchment area into the lagoon. Amongst mangrove-dominated ecosystems globally, the SAL is one of the most effective C sinks due to high mangrove carbon input in combination with a high allochthonous carbon input from anthropogenically-enhanced sediment from the hinterland and increased preservation. Given the substantial C sequestration capacity of the SAL and other mangrove-fringed coastal lagoons, conservation and restoration of these ecosystems is vitally important for climate change mitigation.

We present a reconstruction of deep-water carbonate saturation state (Delta [CO3]2-) in the western equatorial Pacific for the Last Glacial Maximum (LGM) and deglaciation based on changes in the Mg/Ca ratio of planktic foraminifers with increased water depth. Our data suggest there have been changes in bottom waterDelta [CO3]2- over the past 25,000 years at water depths as shallow as 1.6 km. The Delta [CO3]2- reconstruction for the LGM suggests Delta [CO3]2- was similar or slightly higher than modern values between 1.6 and 2.0 km, shifting sharply to lower values (an average ~30 µmol/kg lower) below 2.5 km. The shift in chemistry between 2.0 and 2.5 km supports a hypothesis that Pacific overturning circulation occurred deeper during the LGM with a slightly more ventilated water mass above 2.0 km. The data are not consistent with enhanced preservation in this region of the deep Pacific at depths greater than 2.5 km, suggesting that the long-held view of better preservation throughout the glacial deep Pacific must be reevaluated. For the deglaciation, we have evidence of a Delta [CO3]2- maximum that suggests enhanced deglacial preservation between 1.6 and 4.0 km in comparison to the Holocene and the LGM. The deglacial Delta [CO3]2- was as much as 28 µmol/kg higher than modern between 1.6 and 4.0 km. Results suggest carbonate burial rates were 1.5 times greater during the deglacial than the over the past 5 kyr.

Consumer Price Index (CPI): West Java: Education: Basic and Early Age Education data was reported at 126.730 2018=100 in Dec 2023. This stayed constant from the previous number of 126.730 2018=100 for Nov 2023. Consumer Price Index (CPI): West Java: Education: Basic and Early Age Education data is updated monthly, averaging 121.700 2018=100 from Dec 2019 (Median) to Dec 2023, with 49 observations. The data reached an all-time high of 126.730 2018=100 in Dec 2023 and a record low of 114.180 2018=100 in Dec 2019. Consumer Price Index (CPI): West Java: Education: Basic and Early Age Education data remains active status in CEIC and is reported by Statistics of Jawa Barat Province. The data is categorized under Indonesia Premium Database’s Inflation – Table ID.IB012: Consumer Price Index: by Province: West Java.

Replication Kit for the Paper "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects"
This additional material shall provide other researchers with the ability to replicate our results. Furthermore, we want to facilitate further insights that might be generated based on our data sets.

Structure
The structure of the replication kit is as follows:

• additional_visualizations: contains additional visualizations (Venn-Diagrams) for each projects for each of the data sets that we used
• data_analysis: contains two python scripts that we used to analyze our raw data (one for each research question)
• data_collection_tools: contains all source code used for the data collection, including the used versions of the COMFORT framework, the BugFixClassifier, and the used tools of the SmartSHARK environment;
• mongodb_no_authors: Archived dump of our MongoDB that we created by executing our data collection tools. The "comfort" database can be restored via the mongorestore command.

Additional Visualizations
We provide two additional visualizations for each project:
1)

For each of these data sets there exist one visualization for each project that shows four Venn-Diagrams for each of the different defect types. These Venn-Diagrams show the number of defects that were detected by either unit, or integration tests (or both).

Furthermore, we added boxplots for each of the data sets (i.e., ALL and DISJ) showing the scores of unit and integration tests for each defect type.

Analysis scripts
Requirements:
- python3.5
- tabulate
- scipy
- seaborn
- mongoengine
- pycoshark
- pandas
- matplotlib

Both python files contain all code for the statistical analysis we performed.

Data Collection Tools
We provide all data collection tools that we have implemented and used throughout our paper. Overall it contains six different projects and one python script:

• BugFixClassifier: Used to classify our defects.
• comfort-core: Core of the comfort framework. Used to classify our tests into unit and integration tests and calculate different metrics for these tests.
• comfort-jacoco-listner: Used to intercept the coverage collection process as we were executing the tests of our case study projects.
• issueSHARK: Used to collect data from the ITSs of the projects.
• pycoSHARK: Library that contains models for the used ORM mapper that is used insight the SmartSHARK environment.
• vcsSHARK: Used to collect data from the VCSs of the projects.

Indonesia Consumer Price Index (CPI): Surabaya Municipality: Core data was reported at 131.460 2012=100 in Jul 2019. This records an increase from the previous number of 131.300 2012=100 for Jun 2019. Indonesia Consumer Price Index (CPI): Surabaya Municipality: Core data is updated monthly, averaging 123.600 2012=100 from Jan 2015 (Median) to Jul 2019, with 55 observations. The data reached an all-time high of 131.460 2012=100 in Jul 2019 and a record low of 112.800 2012=100 in Jan 2015. Indonesia Consumer Price Index (CPI): Surabaya Municipality: Core data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Inflation – Table ID.IA050: Consumer Price Index: by Cities: Java: Surabaya.

A set of online services created in support of MIRIAM, a set of guidelines for the annotation and curation of computational models. The core of MIRIAM Resources is a catalogue of data types (namespaces corresponding to controlled vocabularies or databases), their URIs and the corresponding physical URLs or resources. Access to this data is made available via exports (XML) and Web Services (SOAP). MIRIAM Resources are developed and maintained under the BioModels.net initiative, and are free for use by all. MIRIAM Resources are composed of four components: a database, some Web Services, a Java library and this web application. * Database: The core of the system is a MySQL database. It allows us to store the data types (which can be controlled vocabularies or databases), their URIs and the corresponding physical URLs, and other details such as documentation and resource identifier patterns. Each entry contains a diverse set of details about the data type: official name and synonyms, root URI, pattern of identifiers, documentation, etc. Moreover, each data type can be associated with several resources (or physical locations). * Web Services: Programmatic access to the data is available via Web Services (based on Apache Axis and SOAP messages). In addition, REST-based services are currently being developed. This API allows one to not only resolve model annotations, but also to generate appropriate URIs, based upon the provision of a resource name and accession number. A list of available web services, and a WSDL are provided. A browser-based online demonstration of the Web Services is also available to try. * Java Library: A Java library is provided to access the Web Services. The documentation explains where to download it, its dependencies, and how to use it. * Web Application: A Web application, using an Apache Tomcat server, offers access to the whole data set via a Web browser. It is possible to browse by data type names as well as browse by tags. A search engine is also provided.

The second dataset for "Magnetic mineral assemblages of diagenetically reduced sediments and their contributions to paleomagnetic signals" by Li Jiaxi et al. submitted to Journal of Geophysical Research - Solid Earth.

Shells of coexisting species of planktonic foraminifera from the Ontong Java Plateau reveal radiocarbon age offsets of up to 2200 years. Similar offsets are found between fragments and whole shells of single species. Steady state modelling of dissolution and bioturbation within the sedimentary mixed layer predicts age differences of up to several kiloyears due to the interplay between differential dissolution and fragmentation of foraminifer shells and bioturbation. The observation that fragile foraminiferal shells are systematically older than those of more robust species is more difficult to explain. Mechanisms of chemical erosion, interface dissolution, and sediment redistribution are all apparently unable to explain this phenomenon. A possible solution is presented in which a particular species may be represented by two distinct classes of shells which are more or less robust. In this case, differential dissolution and fragmentation causes an increase in the mean age as the fragile class contributes less to the remaining intact shells. This study highlights the vulnerability of low sedimentation rate cores to the effects of dissolution and bioturbation.