cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2023-07.2843565/2397090
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843684/2397091
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844883/2397092
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843500/2397093
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843541/2397094
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844810/2397095
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843791/2397096
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843654/2397097
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844768/2397098
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844777/2397099
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844774/2397100
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844833/2397101
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843510/2397102
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844760/2397103
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843583/2397104
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844848/2397105
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843501/2397106
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843784/2397107
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844756/2397108
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844863/2397109
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843563/2397110
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843626/2397111
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843641/2397112
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844824/2397113
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843548/2397114
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843537/2397115
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843682/2397116
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843786/2397117
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843780/2397118
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844751/2397119
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844845/2397120
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844802/2397121
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843658/2397122
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843677/2397123
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843778/2397124
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843540/2397125
title: Data for EMSL Project 51287 from July 2023
DOI: 10.5281/zenodo.12735746
title: Percursos da etnografia digital no estudo com os Mebêngôkre (Kayapó) em tempos pandêmicos

description: Neste trabalho descrevo brevemente os caminhos percorridos na minha pesquisa de Mestrado sobre os “Processos de formação de novas categorias conceituais e as agências linguísticas Mebêngôkre Kayapó”135. O estudo é formado por narrativas autobiográficas que me situam no contexto da pesquisa e detalham as travessias que me conduziram ao estudo de alguns aspectos da língua Mẽbêngôkre, amparada na antropologia linguística. Trata-se de um estudo etnográfico que envolveu, diálogos com interlocutores Kayapó, diferentes fontes bibliográficas e de leituras sobre línguas Jê. Assim, será descrito os atravessamentos da pesquisa devido à chegada da pandemia e os percursos que o trabalho foi tomando.
DOI: 10.5281/zenodo.12735747
title: Percursos da etnografia digital no estudo com os Mebêngôkre (Kayapó) em tempos pandêmicos

description: Neste trabalho descrevo brevemente os caminhos percorridos na minha pesquisa de Mestrado sobre os “Processos de formação de novas categorias conceituais e as agências linguísticas Mebêngôkre Kayapó”135. O estudo é formado por narrativas autobiográficas que me situam no contexto da pesquisa e detalham as travessias que me conduziram ao estudo de alguns aspectos da língua Mẽbêngôkre, amparada na antropologia linguística. Trata-se de um estudo etnográfico que envolveu, diálogos com interlocutores Kayapó, diferentes fontes bibliográficas e de leituras sobre línguas Jê. Assim, será descrito os atravessamentos da pesquisa devido à chegada da pandemia e os percursos que o trabalho foi tomando.
DOI: 10.25582/data.2023-07.2843774/2397126
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844823/2397127
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843542/2397128
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844791/2397129
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843593/2397130
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844868/2397131
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843505/2397132
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843680/2397133
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844793/2397134
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843796/2397135
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843715/2397136
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843779/2397137
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844875/2397138
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843708/2397139
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844834/2397140
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843745/2397141
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844827/2397142
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843555/2397143
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843625/2397144
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843633/2397145
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844758/2397146
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843539/2397147
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843793/2397148
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844809/2397149
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843624/2397150
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843619/2397151
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843665/2397152
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843729/2397153
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843639/2397154
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844764/2397155
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843552/2397156
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843663/2397157
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844877/2397158
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843693/2397159
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843783/2397160
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843615/2397161
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843544/2397162
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843716/2397163
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844747/2397164
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843664/2397165
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843647/2397166
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843787/2397167
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843572/2397168
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844831/2397169
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843659/2397170
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843788/2397171
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843564/2397172
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844825/2397173
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843623/2397174
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843756/2397175
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844819/2397176
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844783/2397177
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844741/2397178
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844841/2397179
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843776/2397180
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844878/2397181
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843634/2397182
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844844/2397183
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844752/2397184
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843687/2397185
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844767/2397186
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844817/2397187
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844826/2397188
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843637/2397189
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844803/2397190
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843681/2397191
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844742/2397192
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844805/2397193
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844773/2397194
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843568/2397195
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844870/2397196
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844829/2397197
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844838/2397198
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843676/2397199
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843607/2397200
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843698/2397201
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844769/2397202
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843640/2397203
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843547/2397204
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843790/2397205
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844772/2397206
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2843795/2397207
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844816/2397208
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844843/2397209
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844766/2397210
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2844755/2397211
title: Data for EMSL Project 51287 from July 2023
DOI: 10.15468/dl.b9jatu
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aplochlora dentisignata (Moore, 1868)"
 ]
}

 The dataset includes 9 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022840-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4ga2km
title: Occurrence Download

description: A dataset containing 1865946 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "Month is June",
 "Year 2024-2024"
 ]
}

 The dataset includes 1865946 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022838-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vxc2sn
title: Occurrence Download

description: A dataset containing 9605 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "TaxonKey is Himantopus himantopus (Linnaeus, 1758)",
 "Year 2014-2023"
 ]
}

 The dataset includes 9605 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022841-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26287846
title: Friedhoff et al. (2024), Fig 3, FACS data

description: <b>Fig 3. Lipofectamine transfection of SARS-CoV-2 replicon DNA leads to GFP expression that is dependent on DNA linearization but not T7 RNAP and is remdesivir-insensitive.</b> HEK293T transduced with LPCX(AB) (A) or with LPCX(AB)-T7RNAP (B) were then transfected with pSMART-BAC-T7-scv2 DNA, linearized or not, and in the absence or presence of remdesivir, exactly as in Fig. 2, except that transfections were performed using Lipofectamine 3000. Cells were analyzed by flow cytometry two days later.
DOI: 10.6084/m9.figshare.26287846.v1
title: Friedhoff et al. (2024), Fig 3, FACS data

description: <b>Fig 3. Lipofectamine transfection of SARS-CoV-2 replicon DNA leads to GFP expression that is dependent on DNA linearization but not T7 RNAP and is remdesivir-insensitive.</b> HEK293T transduced with LPCX(AB) (A) or with LPCX(AB)-T7RNAP (B) were then transfected with pSMART-BAC-T7-scv2 DNA, linearized or not, and in the absence or presence of remdesivir, exactly as in Fig. 2, except that transfections were performed using Lipofectamine 3000. Cells were analyzed by flow cytometry two days later.
DOI: 10.25916/sut.26288227
title: Data files used to study change dynamics in software systems

description: It is a widely accepted fact that evolving software systems change and grow. However, it is less well-understood how change is distributed over time, specifically in object oriented software systems. The patterns and techniques used to measure growth permit developers to identify specific releases where significant change took place as well as to inform them of the longer term trend in the distribution profile. This knowledge assists developers in recording systemic and substantial changes to a release, as well as to provide useful information as input into a potential release retrospective. However, these analysis methods can only be applied after a mature release of the code has been developed. But in order to manage the evolution of complex software systems effectively, it is important to identify change-prone classes as early as possible. Specifically, developers need to know where they can expect change, the likelihood of a change, and the magnitude of these modifications in order to take proactive steps and mitigate any potential risks arising from these changes. Previous research into change-prone classes has identified some common aspects, with different studies suggesting that complex and large classes tend to undergo more changes and classes that changed recently are likely to undergo modifications in the near future. Though the guidance provided is helpful, developers need more specific guidance in order for it to be applicable in practice. Furthermore, the information needs to be available at a level that can help in developing tools that highlight and monitor evolution prone parts of a system as well as support effort estimation activities. The specific research questions that we address in this chapter are: (1) What is the likelihood that a class will change from a given version to the next? (a) Does this probability change over time? (b) Is this likelihood project specific, or general? (2) How is modification frequency distributed for classes that change? (3) What is the distribution of the magnitude of change? Are most modifications minor adjustments, or substantive modifications? (4) Does structural complexity make a class susceptible to change? (5) Does popularity make a class more change-prone? We make recommendations that can help developers to proactively monitor and manage change. These are derived from a statistical analysis of change in approximately 55000 unique classes across all projects under investigation. The analysis methods that we applied took into consideration the highly skewed nature of the metric data distributions. The raw metric data (4 .txt files and 4 .log files in a .zip file measuring ~2MB in total) is provided as a comma separated values (CSV) file, and the first line of the CSV file contains the header. A detailed output of the statistical analysis undertaken is provided as log files generated directly from Stata (statistical analysis software).
DOI: 10.25916/sut.26288227.v1
title: Data files used to study change dynamics in software systems

description: It is a widely accepted fact that evolving software systems change and grow. However, it is less well-understood how change is distributed over time, specifically in object oriented software systems. The patterns and techniques used to measure growth permit developers to identify specific releases where significant change took place as well as to inform them of the longer term trend in the distribution profile. This knowledge assists developers in recording systemic and substantial changes to a release, as well as to provide useful information as input into a potential release retrospective. However, these analysis methods can only be applied after a mature release of the code has been developed. But in order to manage the evolution of complex software systems effectively, it is important to identify change-prone classes as early as possible. Specifically, developers need to know where they can expect change, the likelihood of a change, and the magnitude of these modifications in order to take proactive steps and mitigate any potential risks arising from these changes. Previous research into change-prone classes has identified some common aspects, with different studies suggesting that complex and large classes tend to undergo more changes and classes that changed recently are likely to undergo modifications in the near future. Though the guidance provided is helpful, developers need more specific guidance in order for it to be applicable in practice. Furthermore, the information needs to be available at a level that can help in developing tools that highlight and monitor evolution prone parts of a system as well as support effort estimation activities. The specific research questions that we address in this chapter are: (1) What is the likelihood that a class will change from a given version to the next? (a) Does this probability change over time? (b) Is this likelihood project specific, or general? (2) How is modification frequency distributed for classes that change? (3) What is the distribution of the magnitude of change? Are most modifications minor adjustments, or substantive modifications? (4) Does structural complexity make a class susceptible to change? (5) Does popularity make a class more change-prone? We make recommendations that can help developers to proactively monitor and manage change. These are derived from a statistical analysis of change in approximately 55000 unique classes across all projects under investigation. The analysis methods that we applied took into consideration the highly skewed nature of the metric data distributions. The raw metric data (4 .txt files and 4 .log files in a .zip file measuring ~2MB in total) is provided as a comma separated values (CSV) file, and the first line of the CSV file contains the header. A detailed output of the statistical analysis undertaken is provided as log files generated directly from Stata (statistical analysis software).
DOI: 10.15468/dl.bw9v94
title: Occurrence Download

description: A dataset containing 21667 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Australia",
 "TaxonKey is Acacia mearnsii De Wild."
 ]
}

 The dataset includes 21667 records from 68 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022842-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yk23sz
title: Occurrence Download

description: A dataset containing 6101 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "TaxonKey is Recurvirostra avosetta Linnaeus, 1758",
 "Year 2014-2023"
 ]
}

 The dataset includes 6101 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022843-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8zzw6u
title: Occurrence Download

description: A dataset containing 4134 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "TaxonKey is Vanellus vanellus (Linnaeus, 1758)",
 "Year 2014-2023"
 ]
}

 The dataset includes 4134 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022844-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d45yms
title: Occurrence Download

description: A dataset containing 4481 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Vanellus cinereus (Blyth, 1842)",
 "Year 2014-2023"
 ]
}

 The dataset includes 4481 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022845-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.