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A corpus of Java projects representing the 2012 Ohloh universe
<p>A corpus of Java projects build using the Software Projects Sampling (SPS) tool by Naggapan et al [1]. SPS measures representativeness of a smaller corpus with respect to the universe (Ohloh 2012) in terms of diversity dimensions and constructs a maximally representative corpus by iteratively adding projects that would increase the representativeness most.</p> <p>The corpus contains the source code of the projects selected at 1 June 2012. This is due to the fact that the Ohloh data on which the diversity was calculated was of that period.</p> <p> </p> <p>[1] M. Nagappan, T. Zimmermann, and C. Bird, “Diversity in software engineering research,” in ESEC/FSE. ACM, 2013, pp. 466–476.</p>
juliacarbajal/bilingual_assimilation: First release of data & analysis scripts for bilingual assimilation project.
<p>This release contains all the app data and vocabulary questionnaires collected for Carbajal et al. (in preparation)'s project on bilingual assimilation (OSF project <a href="https://osf.io/52z9g/">https://osf.io/52z9g/</a>). It also includes analysis scripts as of the 22nd of March, 2018.</p>
OAPEN-CH – The impact of open access on scientific monographs in Switzerland. A project conducted by the Swiss National Science Foundation (SNSF)
<p>The Swiss National Science Foundation (SNSF) launched the OAPEN-CH pilot project in summer 2014. At that time, it had introduced new funding policies that only covered books that were published online and openly accessible after an embargo period of no more than 24 months. The pilot study set out to learn more about the changes to academic publishing and pave the way for future adaptations to funding policies.</p> <p>The findings of the shared learning process are now available. They are encouraging in that they produce a clear verdict for the scientific monograph: a digital edition that is freely available on the Internet increases the trackability, visibility and use of monographs. Open access does not have a negative impact on printed book sales. Facilitating and promoting open access will benefit the academic community and society at large as well as publishers and funders.</p> <p>The results of the OAPEN-CH pilot study have already been integrated into the new open access policy of the SNSF, which entered into force on 1 April 2018.</p>
Data for the project investigating movement of terrestrial rodents in riparian reserves
<b>Description: </b><p>The spool-and-line technique was used to observe the efficacy with which small mammals are able to use riparian strips as dispersal corridors within a fragmented landscape. I set up transects along established riparian strips in mature oil palm plantations, using novel sites >1000m away from existing SAFE project sites to avoid conflict with other projects. These included riparian strips of widths ranging from 10-120 metres. Control (continuous twice-logged forest) sites will be set up within the SAFE landscape. For each riparian transect, I set twenty Tomahawk-style steel cage traps baited with oil palm fruit at 15m intervals along the transect, with a constant distance from the oil palm edge of the riparian zone. To avoid physiological stress to the mammals, traps were set up only in well-shaded microhabitats in dense cover, with sufficiently large and fresh bait to provide adequate nutrition. I checked trapping transects everyday and re-set each day at dusk. This study will focus on common and resilient rats of the genus Maxomys. I only focus on adults and subadults, and released vulnerable juveniles and pregnant females released. <br><br>Animals caught were identified, sexed and aged, and either released or retained for tracking, depending on several criteria. Spooled individuals will fit weight criteria such that any attached spool device does not exceed 5% of the individual's body weight. Animals over 40g were tracked with a small spool (length=88m), while animals over 100g will use a large spool (length=220m). Animals below the 40g cut-off were released. This ensures representative movement is observed, and meets widely-accepted ethical guidelines for tracking devices. Animals fitted with a spooling device were firstly anaesthetised with diethyl ether to obtain biometric measurements (hind foot, ano-genital distance and weight), the sex, age and species for each individual. As well as providing back-up information in the case of uncertain ID, these data are important covariates of observed movement behaviour.<br><br>To track the movement path of selected individuals, I attached a temporary lightweight bobbin of thread to the animal. A hair clip will be taken from the upper back region to provide a fur-based attachment point for the spool using cyanoacrylate gel glue. To spool package was a bobbin double-wrapped in a cling-film layer that allows smooth unravelling, and then wound in a gaffer tape layer that avoids snagging on foliage. The total weight of the package did not exceed 5% of the average minimum weight of each species. Once processed, I translocated individuals from their site of capture by ~50m to a release site longitudinally along the same riparian reserve. The proximal end of the thread was tied to a fixed or heavy structure such as the trap so that as the animal is released from the trap, the freely unravelling thread catches onto vegetation and topography, revealing the movement patterns of the individual. Movement paths were divided into steps, with a step being defined as a length of the animal's track where it does not deviate from a straight line by more than 10 degrees. I measured the length of steps and the angles between them using a tape measure and compass to construct a detailed two-dimensional map of each animal's route through space. From this, I obtained a measure of path tortuosity (fractal dimension and deviation from a correlated random walk) for each tracked movement path. <br></p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/36"><b>Determining the importance of riparian reserves to maintain the dispersal of small mammals in a fragmented landscape in Sabah, Malaysia</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=72">here</a></p><p><b>Data worksheets: </b>There are 2 data worksheets in this dataset:</p><ol><li><p><b>Data collected from spool-and-line tracking of terrestrial rodents</b> (Worksheet SpoolData)</p><p>Dimensions: 5544 rows by 14 columns</p><p>Description: This worksheet contains the data I collected from the spool-and-line tracking. Each tracked rodent individual has a corresponding unique identification number, given by the name in the 'Path_code' column. The 'Path_code' identification number of a tracked individual corresponds to the same individual in the 'Trapping and Fractal data' worksheet. The 'Step_number' column indicates the order of individual steps of a movement path for a tracked rodent. The direction bearing of an individual movement path step is presented in the 'Step_bearing' column. The 'Step_length' column contains the length in centimeters of an individual movement step. The 'Matrix' coloumn indicates whether the rodent was released in a riparian reserve (RR) or continuous twice-logged forest (CF) habitat.</p><p>Fields: </p><ul><li><b>Transect_location</b>: Location of the transect (Field type: Location)</li><li><b>Date_trap</b>: Date traps were set (Field type: Date)</li><li><b>Date_release</b>: Date individuals were released (Field type: Date)</li><li><b>Date_spool</b>: Date a spool path was recorded (Field type: Date)</li><li><b>Site_id</b>: Transect ID (Field type: ID)</li><li><b>Trap_number</b>: Location of trap along the transect (Field type: Numeric)</li><li><b>Release_location</b>: Grid coordinates for location where a spooled individual was released (Field type: ID)</li><li><b>Species</b>: Species (Field type: Taxa)</li><li><b>Path_code</b>: Unique spool path ID (Field type: ID)</li><li><b>Step_number</b>: The sequential order of the movement steps of a spool path (Field type: Numeric)</li><li><b>Step_bearing</b>: Movement step bearing (Field type: Numeric)</li><li><b>Step_length</b>: Movement step length (Field type: Numeric)</li><li><b>Matrix</b>: Habitat adjacent to riparian forest (Field type: Categorical)</li></ul><br></li><li><p><b>Rodent trapping and fractal analysis data</b> (Worksheet TrappingData)</p><p>Dimensions: 1102 rows by 40 columns</p><p>Description: This worksheet contains data from rodent surveys I conducted in riparian reserves and continuous twice-logged forest habitats. It contains morphometric data of individuals, including: weight (g), hind foot (cm) and AGD (cm). As well as the species (Maxomys whiteheadi/MW, Maxomys surifer/MS, Maxomys rajah/MR, Niviventer cremoriventer/DTT, Sundamys muelleri/MR, Rattus norvegicus/BR or Leopoldamys sabanus/LTG), sex (Female/F and Male/M) and age (Adult/A, Subadult/SA or Juvenile/J) of individuals. The coloumn 'Entered_op' contains data on whether an individual crossed over from the riparian corridor habitat into the adjacent oil palm (1 = entered oil palm habitat). It also contains the data output of the fractal analysis (fractal dimension/D_dimension and correlated random walk/CRW_Diff) of the movement trajectories. </p><p>Fields: </p><ul><li><b>Transect_location</b>: Location of the transect (Field type: Location)</li><li><b>Date_trap</b>: Date traps were set (Field type: Date)</li><li><b>Date_process</b>: Date the individual was processed or attached with a spooling devie (Field type: Date)</li><li><b>Date_release</b>: Date individuals were released (Field type: Date)</li><li><b>Date_spool</b>: Date a spool path was recorded (Field type: Date)</li><li><b>Site_id</b>: Transect ID (Field type: ID)</li><li><b>Trap_Location</b>: Grid coordinate of the trap (Field type: ID)</li><li><b>Trap_number</b>: Location of trap along the transect (Field type: Numeric)</li><li><b>Trap_success</b>: Whether an individual was trapped (Field type: Categorical)</li><li><b>Death</b>: Recorded deaths (Field type: Categorical)</li><li><b>Process_time</b>: Time at which an animal was spooled and released (Field type: Time)</li><li><b>Release_distance</b>: Distance or grid coordinates along the transect where a spooled individual was released (Field type: ID)</li><li><b>Release_location</b>: Site of release within the riparian corridor (Field type: Categorical)</li><li><b>Release_distance_from_edge</b>: Distance from oil palm edge where an individual is released in a riparian reserve (Field type: Numeric)</li><li><b>SF_release</b>: Whether a spool fell immediately upon release (Field type: Categorical)</li><li><b>Species</b>: Species (Field type: Taxa)</li><li><b>Sex</b>: Sex of individual (Field type: Categorical)</li><li><b>Age</b>: Age of individual (Field type: Categorical)</li><li><b>Weight</b>: Weight of individual (Field type: Numeric)</li><li><b>Hind_foot</b>: Length of hind foot of an individual (Field type: Numeric)</li><li><b>AGD</b>: Anogenital distance of individual (Field type: Numeric)</li><li><b>Ear</b>: Length of ear (Field type: Numeric)</li><li><b>Path_code</b>: Unique spool path ID (Field type: ID)</li><li><b>Spool_follower</b>: Person who tracked the spool path (Field type: Comments)</li><li><b>Matrix</b>: Habitat adjacent to trapping grid (Field type: Categorical)</li><li><b>Distance_translocated</b>: Distance an individual was translocated from its capture point (Field type: Numeric)</li><li><b>Path_end</b>: Reason why spool fell off (Field type: Categorical)</li><li><b>Spool_totalsteps</b>: Total number of movement path steps in the tracked spool path (Field type: Numeric)</li><li><b>Spool_totallength</b>: Total length of a spool path (Field type: Numeric)</li><li><b>Dist_from_release</b>: Distance from the spool path end to the release site (Field type: Numeric)</li><li><b>Entered_op</b>: Whether the tracked individual crossed over from the riparian reserve into the oil palm habitat (Field type: Categorical)</li><li><b>D_dimension</b>: Fractal Dimension of the movement path (Field type: Numeric)</li><li><b>Total_steps</b>: The sum of steps in an individual's movement path (Field type: Numeric)</li><li><b>Total_length</b>: Total length of the tracked movement path (Field type: Numeric)</li><li><b>Min_stepsize</b>: The shortest movement path step taken (Field type: Numeric)</li><li><b>Max_stepsize</b>: The longest movement path step taken (Field type: Numeric)</li><li><b>Mean_stepsize</b>: The mean step length of a movement path (Field type: Numeric)</li><li><b>CRW_Diff</b>: Deviation of movement path from a correlated random walk (Field type: Numeric)</li><li><b>Rainfall</b>: If it was raining when the spooled individual was released (Field type: Ordered Categorical)</li></ul><br></li></ol><p><b>Date range: </b>2016-03-07 to 2016-06-25</p><p><b>Latitudinal extent: </b>4.6505 to 4.7255</p><p><b>Longitudinal extent: </b>117.4532 to 117.6286</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Chordata<br> -  - Mammalia<br> -  -  - Rodentia<br> -  -  -  - Muridae<br> -  -  -  -  - <i>Chrotomys</i><br> -  -  -  -  -  - <i>Chrotomys whiteheadi</i> (as <i>Maxomys whiteheadi</i>)<br> -  -  -  -  - <i>Leopoldamys</i><br> -  -  -  -  -  - <i>Leopoldamys sabanus</i><br> -  -  -  -  - <i>Maxomys</i><br> -  -  -  -  -  - <i>Maxomys rajah</i><br> -  -  -  -  -  - <i>Maxomys surifer</i><br> -  -  -  -  - <i>Niviventer</i><br> -  -  -  -  -  - <i>Niviventer cremoriventer</i><br> -  -  -  -  - <i>Rattus</i><br> -  -  -  -  -  - <i>Rattus exulans</i><br> -  -  -  -  -  - <i>Rattus norvegicus</i><br> -  -  -  -  - <i>Sundamys</i><br> -  -  -  -  -  - <i>Sundamys muelleri</i><br></div><p></p>
REXCO Project :Physical exercise increases overall brain oscillatory activity but does not influence inhibitory control in young adults
<p><strong>Methods and design</strong></p> <p><em>Participants</em></p> <p>We recruited 20 young males (19-32 years old, average age 23.8 years old) from the University of Granada (Spain). All participants met the inclusion criteria of normal or corrected to normal vision, reported no neurological, cardiovascular or musculoskeletal disorders, were taking no medication and reporting less than 3 hours of moderate exercise per week. Participants were required to maintain regular sleep-wake cycle for at least one day before each experimental session and to abstain from stimulating beverages or any intense exercise 24 hours before each session. From the 20 participants, one was excluded from the analyses because he did not attend to the last experimental session and another one because of technical issues. Thus, only data from the remaining 18 participants are reported. All subjects gave written informed consent before the study and received 20 euros for their participation. The protocol was approved in accordance with both the ethical guidelines of the University of Granada and the Declaration of Helsinki of 1964.</p> <p><em>Apparatus and materials</em></p> <p>All participants were fitted with a Polar RS800 CX monitor (Polar Electro Öy, Kempele, Finland) to record their heart rate (HR) during the incremental exercise test. We used a ViaSprint 150 P cycle ergometer (Ergoline GmbH, Germany) to induce physical effort and to obtain power values, and a JAEGER Master Screen gas analyser (CareFusion GmbH, Germany) to provide a measure of gas exchange during the effort test. Flanker task stimuli were presented on a 21-inch BENQ screen maintaining a fixed distance of 50 cm between the head of participants and the center of the screen. E-Prime software (Psychology Software Tools, Pittsburgh, PA, USA) was used for stimulus presentation and behavioural data collection.</p> <p><em>Procedure</em></p> <p>Participants completed two counterbalanced experimental sessions of approximately 120 min each. Sessions were scheduled on different days allowing a time interval of 48–72 hours between them to avoid possible fatigue and/or training effects. On each experimental session (see Fig. 1), participants completed a 15’ resting state period sitting in a comfortable chair with closed eyes. Subsequently, they performed 10’ warm-up on a cycle-ergometer at a power load of 20% of their individual VO<sub>2</sub> VAT, following by 30’ exercise at 80% (moderate-intensity exercise session) or at 20% (light intensity exercise session) of their VO<sub>2</sub> VAT (see Table 1). Upon completion of the exercise, a 10’ cool down period at 20% VO<sub>2</sub> VAT of intensity followed. Each participant set his preferred cadence (between 60-90 rpm • min-1) before the warm-up and was asked to maintain this cadence throughout the session in order to match conditions in terms of dual-task demands. Later, participants waited sitting in a comfortable chair until their heart rate returned to within their 130% of heart rate at resting (average waiting time 5’ 44’’). The first flanker task was then performed for 6’, followed by a 15’ resting period with closed eyes. Finally, they again completed the 6’ flanker task.</p> <p><em>Flanker task</em></p> <p>We used a modified version of the Eriksen flanker task based on that reported in Eriksen and Eriksen (1974). The task consisted of a random presentation of a set arrows flanked by other arrows that faced the same or the opposite direction. In the congruent trials, the central arrow is flanked by arrows in the same direction (e.g., <<<<< or >>>>>), while in the incongruent trials, the central arrow is flanked by arrows in the opposite direction (e.g., <<><< or >><>>). Stimuli were displayed sequentially on the center of the screen on a black background. Each trial started with the presentation of a white fixation cross in a black background with random duration between 1000 and 1500 ms. Then, the stimulus was presented during 150 ms and a variable interstimulus interval (1000–1500 ms). Participants were instructed to respond by pressing the left tab button with their left index finger when the central arrow (regardless of condition) faced to the left and the right tab button with their right index finger when the central arrow faced to the right. Participants were encouraged to respond as quick as possible, being accurate. A total of 120 trials were randomly presented (60 congruent and 60 incongruent trials) in each task. Each task lasted for 6 minutes approximately without breaks.</p> <p><em>EEG recording and analysis</em></p> <p>EEG data were recorded at 1000 Hz using a 30-channel actiCHamp System (Brain Products GmbH, Munich, Germany) with active electrodes positioned according to the 10-20 EEG International System and referenced to the Cz electrode. The cap was adapted to individual head size, and each electrode was filled with Signa Electro-Gel (Parker Laboratories, Fairfield, NJ). Participants were instructed to avoid body movements as much as possible, and to keep their gaze on the center of the screen during the exercise. Electrode impedances were kept below 10 kΩ. EEG preprocessing was conducted using custom Matlab scripts and the EEGLAB (Delorme & Makeig, 2004) and Fieldtrip (Oostenveld et al., 2011) Matlab toolboxes. EEG data were resampled at 500 Hz, bandpass filtered offline from 1 and 40 Hz to remove signal drifts and line noise, and re-referenced to a common average reference. Horizontal electrooculograms (EOG) were recorded by bipolar external electrodes for the offline detection of ocular artifacts. The potential influence of electromyographic (EMG) activity in the EEG signal was minimized by using the available EEGLAB routines (Delorme & Makeig, 2004). Independent component analysis was used to detect and remove EEG components reflecting eye blinks (Hoffmann and Falkenstein, 2008). Abnormal spectra epochs which spectral power deviated from the mean by +/-50 dB in the 0-2 Hz frequency window (useful for catching eye movements) and by +25 or -100 dB in the 20-40 Hz frequency window (useful for detecting muscle activity) were rejected. On average, 5.1% of epochs per participant were rejected.</p> <p><em>Spectral power analysis</em>. Processed EEG data from each experimental period (Resting 1, Warm-up, Exercise, Cool Down, Flanker Task 1, Resting 2, Flanker Task 2) were subsequently segmented to 1-s epochs. The spectral decomposition of each epoch was computed using Fast Fourier Transformation (FFT) applying a symmetric Hamming window and the obtained power values were averaged across experimental periods.</p> <p><em>Event-Related Spectral Perturbation (ERSP) analysis.</em> Task-evoked spectral EEG activity was assessed by computing ERSP in epochs extending from –500 ms to 500 ms time-locked to stimulus onset for frequencies between 4 and 40 Hz. Spectral decomposition was performed using sinusoidal wavelets with 3 cycles at the lowest frequency and increasing by a factor of 0.8 with increasing frequency. Power values were normalized with respect to a −300 ms to 0 ms pre-stimulus baseline and transformed into the decibel scale.</p>
Experimental data - project: adaptive strategy on part-list cueing and ego-depletion
<p>Experimental data of two experiments on adaptive strategy and its relation to recall performance and task persistence.</p>
Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: A Case Study of the PyPI Ecosystem - the dataset
<pre><em>Replication pack, FSE2018 submission #164: </em><em>------------------------------------------ </em></pre> <pre><strong>**</strong>Working title:<strong>** </strong>Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: A Case Study of the PyPI Ecosystem <strong>**</strong>Note:<strong>** </strong>link to data artifacts is already included in the paper. Link to the code will be included in the Camera Ready version as well. <em>Content description </em><em>=================== </em> <strong>- **</strong>ghd-0.1.0.zip<strong>** </strong>- the code archive. This code produces the dataset files described below <strong>- **</strong>settings.py<strong>** </strong>- settings template for the code archive. <strong>- **</strong>dataset_minimal_Jan_2018.zip<strong>** </strong>- the minimally sufficient version of the dataset. This dataset only includes stats aggregated by the ecosystem (PyPI) <strong>- **</strong>dataset_full_Jan_2018.tgz<strong>** </strong>- full version of the dataset, including project-level statistics. It is ~34Gb unpacked. This dataset still doesn't include PyPI packages themselves, which take around 2TB. <strong>- **</strong>build_model.r, helpers.r<strong>** </strong>- R files to process the survival data (`survival_data.csv` in <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>**</strong>, `common.cache/survival_data.pypi_2008_2017-12_6.csv` in <strong>**</strong>dataset_full_Jan_2018.tgz<strong>**</strong>) <strong>- **</strong>Interview protocol.pdf<strong>** </strong>- approximate protocol used for semistructured interviews. <strong>- </strong>LICENSE - text of GPL v3, under which this dataset is published <strong>- </strong>INSTALL.md - replication guide (~2 pages)</pre> <pre><em>Replication guide </em><em>================= </em> <em>Step 0 - prerequisites </em><em>---------------------- </em> <strong>- </strong>Unix-compatible OS (Linux or OS X) <strong>- </strong>Python interpreter (2.7 was used; Python 3 compatibility is highly likely) <strong>- </strong>R 3.4 or higher (3.4.4 was used, 3.2 is known to be incompatible) Depending on detalization level (see Step 2 for more details): <strong>- </strong>up to 2Tb of disk space (see Step 2 detalization levels) <strong>- </strong>at least 16Gb of RAM (64 preferable) <strong>- </strong>few hours to few month of processing time <em>Step 1 - software </em><em>---------------- </em> <strong>- </strong>unpack <strong>**</strong>ghd-0.1.0.zip<strong>**</strong>, or clone from gitlab: git clone https://gitlab.com/user2589/ghd.git git checkout 0.1.0 `cd` into the extracted folder. All commands below assume it as a current directory. <strong>- </strong>copy `settings.py` into the extracted folder. Edit the file: <strong> * </strong>set `DATASET_PATH` to some newly created folder path <strong> * </strong>add at least one GitHub API token to `SCRAPER_GITHUB_API_TOKENS` <strong>- </strong>install docker. For Ubuntu Linux, the command is `sudo apt-get install docker-compose` <strong>- </strong>install libarchive and headers: `sudo apt-get install libarchive-dev` <strong>- </strong>(optional) to replicate on NPM, install yajl: `sudo apt-get install yajl-tools` Without this dependency, you might get an error on the next step, but it's safe to ignore. <strong>- </strong>install Python libraries: `pip install --user -r requirements.txt` . <strong>- </strong>disable all APIs except GitHub (Bitbucket and Gitlab support were not yet implemented when this study was in progress): edit `scraper/init.py`, comment out everything except GitHub support in `PROVIDERS`. <em>Step 2 - obtaining the dataset </em><em>----------------------------- </em> The ultimate goal of this step is to get output of the Python function `common.utils.survival_data()` and save it into a CSV file: # copy and paste into a Python console from common import utils survival_data = utils.survival_data('pypi', '2008', smoothing=6) survival_data.to_csv('survival_data.csv') Since full replication will take several months, here are some ways to speedup the process: <em>####Option 2.a, difficulty level: easiest </em> Just use the precomputed data. Step 1 is not necessary under this scenario. <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>** </strong><strong>- </strong>get `survival_data.csv`, go to the next step <em>####Option 2.b, difficulty level: easy </em> Use precomputed longitudinal feature values to build the final table. The whole process will take 15..30 minutes. <strong>- </strong>create a folder `<DATASET_PATH>/common.cache`, where `<DATASET_PATH>` is the value of the variable `DATASET_PATH` in `settings.py` <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018<strong>** </strong>to the newly created folder <strong>- </strong>rename files: mv backporting.csv monthly_data.pypi_backporting.csv mv cc_degree.csv monthly_data.pypi_cc_degree.csv mv commercial.csv monthly_data.pypi_commercial.csv mv commits.csv monthly_data.pypi_commits.csv mv contributors.csv monthly_data.pypi_contributors.csv mv dc_katz.csv monthly_data.pypi_dc_katz.csv mv downstreams.csv monthly_data.pypi_downstreams.csv mv d_upstreams.csv monthly_data.pypi_d_upstreams.csv mv github_user_info.csv user_info.pypi.csv mv issues.csv monthly_data.pypi_issues.csv mv non_dev_issues.csv monthly_data.pypi_non_dev_issues.csv mv non_dev_submitters.csv monthly_data.pypi_non_dev_submitters mv package_urls.csv package_urls.pypi.csv mv q90.csv monthly_data.pypi_q90.csv # raw_dependencies.csv is not required # raw_packages_info.csv is not required # Feel free to read README.md for more details about the data mv submitters.csv monthly_data.pypi_submitters.csv # In this scenario we'll generate a new survival_data.csv mv university.csv monthly_data.pypi_university.csv mv upstreams.csv monthly_data.pypi_upstreams.csv <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` Then, use the Python code above to obtain `survival_data.csv`. <em>####Option 2.c, difficulty level: medium </em> Use predownloaded raw data to build longitudinal feature values, and then the dataset. Despite most of the data is cached, some functions will pull up updates which might take anywhere from days to couple weeks to run. <strong>- </strong>Download <strong>**</strong>dataset_full_Jan_2018.tgz<strong>** </strong>from http://k.soberi.us/dataset_full_Jan_2018.tgz . This file is not included in this archive because of its size (5.4Gb compressed, 34Gb unpacked). <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` <strong>- </strong>extract the content of this archive into `<DATASET_PATH>`. <strong>- </strong>clean up `<DATASET_PATH>/common.cache` (otherwise you'll get Step 2.a. You can reproduce Step 2.b by deleting only `survival_data.pypi_2008_2017-12_6.csv`) Run the Python code above to obtain `survival_data.csv`. <em>####Option 2.d, difficulty level: hard </em> Build the dataset from scratch. Although most of the processing is parallelized, it will take at least couple months on a reasonably powerful server (32 cores, 512G of RAM, 2Tb+ of HDD space in our setup). <strong>- </strong>ensure the `<DATASET_PATH>` is empty <strong>- </strong>add more GitHub tokens (borrow from your coworkers) to `settings.py`. Run the Python code above to obtain `survival_data.csv`. <em>Step 3 - run the regression </em><em>--------------------------- </em> install R libraries: install.packages(c("htmlTable", "OIsurv", "survival", "car", "survminer", "ggplot2", "sqldf", "pscl", "texreg", "xtable")) Use `build_model.r` (e.g. in RStudio) and produced `survival_data.csv` to build the regressions used in the paper. This process takes at least 16Gb of RAM and takes few hours to run due to the gigantic size of the dataset. </pre>
Manual Dependency Annotation of Three German Text Extracts from the Project hermA (Gold Standard Data)
<p>This dataset was created in the digital humanities project hermA (www.herma.uni-hamburg.de) and comprises annotated extracts of the following three texts:</p> <ul> <li>Modern literature (Lit2009): novel <em>Corpus Delicti: Ein Prozess </em>by German author Juli Zeh, published in Frankfurt/Main in 2009.</li> <li>Non-contemporary literature (Lit1850):<em> Eine Frauenfahrt um die Welt </em>('A woman’s journey around the world') by Austrian author Ida Pfeiffer (1850). Full text available at Deutsches Textarchiv: http://www.deutschestextarchiv.de/pfeiffer_frauenfahrt01_1850/6.</li> <li>Modern academic writing (Aca2009): <em>Stand, Möglichkeiten und Grenzen der Telemedizin in Deutschland</em> ('Telemedicine in Germany: status, chances and limits') by Rüdiger Klar and Ernst Pelikan, published in Bundesgesundheitsblatt ('Federal Health Gazette') in 2009. DOI 10.1007/s00103-009-0787-7.</li> </ul> <p>The texts are annotated for part-of-speech and dependency syntax and are made available in CoNLL file format. We describe the annotation process and report inter-annotator agreements in:</p> <p>Adelmann, Benedikt, Melanie Andresen, Wolfgang Menzel & Heike Zinsmeister. 2018. Evaluation of Out-Of Domain Dependency Parsing for its Application in a Digital Humanities Project. <em>Proceedings of the 14th Conference on Natural Language Processing (KONVENS 2018)</em>. Vienna, Austria.</p>
Bridging the Chromosome-Centric and Biology and Disease Human Proteome Projects: Accessible and automated tools for interpreting biological and pathological impact of protein sequence variants detected via proteogenomics
<p>Bridging the Chromosome-Centric and Biology and Disease Human Proteome Projects: Accessible and automated tools for interpreting biological and pathological impact of protein sequence variants detected via proteogenomics</p>
Climate-projected distributional shifts for North American ecoregions
<p>Climate-projected distributional shifts for North American ecoregions</p> <p>Citation for dataset<br> --------------------<br> Stralberg, D. Climate-projected distributional shifts for North American ecoregions. http://doi.org/10.5281/zenodo.1407176</p> <p>Data layers <br> -----------------<br> Data layers represent ecoregion projections at 1-km resolution for North America: <br> _predcurrent.tif<br> _pred_XXXXX_YYYY.tif<br> where:<br> XXXXX = Representative Concentration Pathway (rcp45 or rcp85)<br> YYYYY = Time period (2050s or 2080s)</p> <p>See "ecoregion_lookup.csv" for ecoregion definitions and projected change summaries.</p> <p>Projection information<br> -------------------<br> "+proj=lcc +lat_1=49 +lat_2=77 +lat_0=0 +lon_0=-95 +x_0=0 +y_0=0 +ellps=GRS80 +units=m +no_defs"<br> -------------------<br> Projection LAMBERT<br> Spheroid GRS80<br> Units METERS<br> Zunits NO<br> Xshift 0.0<br> Yshift 0.0<br> Parameters <br> 49 0 0.0 /* 1st standard parallel<br> 77 0 0.0 /* 2nd standard parallel<br> -95 0 0.0 /* central meridian<br> 0 0 0.0 /* latitude of projection's origin<br> 0.0 /* false easting (meters)<br> 0.0 /* false northing (meters)</p>
Characteristics of wound dressing developed within the MEDILIGHT project
<p>The data set contains details on characteristics of wound dressing developed by URGO within the H2020 funded project MEDILIGHT.</p>
Commits modifications from 61 projects hosted in Github's repositories
<p>This dataset contains data from 61 opensource projects that can be found in github repositories.The data contains informations about:</p> <ul> <li>committer name</li> <li>committer email</li> <li>modification type - changed, removed or created</li> <li>modified files</li> <li>commit comments</li> <li>commit date</li> </ul> <p> </p> <p>This dataset references 299 major version software, developed in five different programming languages. These languages are:</p> <ul> <li>C++</li> <li>Java</li> <li>Javascript</li> <li>Python</li> <li>Ruby</li> </ul> <p>The dataset was used in my undergraduate thesis</p>
Conceptual Coupling Values - Github Projects Dataset
<p>A Dataset that contains conceptual coupling measures obtained on 61 opensource projects hosted in github.The conceptual coupling measure was calculated by 18 diferent ways. A way is called model. There are 299 software major version colected to calculate these values.</p> <p>To understand how each model was applyed, see the label meaning:</p> <ul> <li>Yco: Use code comment</li> <li>Nco: No use code comment</li> <li>Yca: Divide tokens by CamelCase</li> <li>Nca: no divide tokens by CamelCase</li> <li>Ns: no stemming tokens low frequency occurrence</li> <li>Ys: stemming tokens low frequeny occurrence</li> <li>Qth: remove third quantile </li> <li>Qfi: remove first quantile</li> <li>Lsi: Latent Semantic Indexing</li> <li>TFIDF: term frequency - inverse document frequency</li> <li>Naive: Term-Frequency</li> </ul> <p>All projects are from github and developed in the following language:</p> <ul> <li>C++</li> <li>Java</li> <li>Javascript</li> <li>Python</li> <li>Ruby</li> </ul> <p>This dataset also contains the following resources:</p> <ul> <li>Commits between those project versions</li> <li>List of versions used in this study</li> <li>Scripts used in some works</li> </ul>
Code diffs and commit messages from top1000-2000 Java projects in GitHub
<p>This dataset contains pairs <code changes, commit message> from top 1000-2000 Java projects in GitHub via <a href="https://developer.github.com/v3/">GitHub Developer API</a>.</p> <p>The structure of files is as follows:</p> <ul> <li><repository-id>.<repository-name>/ # Each directory contains commits of one project. <ul> <li><commit-id>.<commit-sha>/ # Each directory contains one commit information, and<br> # the commit-sha is the primary key of this commit in GitHub. <br> # The children in this directory have 3 types: the file<br> # "commit_msg", multiple directories "<filename>", and the file<br> # "error". <ul> <li>commit_msg # This file contains one line, representing the commit<br> # message. </li> <li><filename>/ # The name of this directory is the changed file name. <ul> <li>patch # The code diffs of this <filename>, describing<br> # which lines are added and which lines are<br> # removed with some same lines context.</li> </ul> </li> <li><filename>/ <ul> <li>patch</li> </ul> </li> <li>error # This file contains the error message when crawling<br> # from GitHub. If this file exists, the directories<br> # <filename> will not exist. </li> </ul> </li> </ul> </li> </ul>
Carsington Water bioacoustics project - wav files
<p>wav files recorded on Wildlife Acoustics SM2 recorders.</p> <p>Data from four recorders placed on land at northeast side of Carsington Water in April/May 2018.</p> <p>Locations of recorders: </p> <p><strong>latitude longitude site_name unit microphones</strong></p> <p>53.0748524 -1.6096798 Reedbed 8621 Two</p> <p>53.0711544- 1.6118927 Plantation mixed 8535 Two</p> <p>53.0668604 -1.6170512 Scrub 8607 Two</p> <p>53.0703651 -1.6140515 Woodland 8552 Two</p>
Mutation Calls from the TCGA MC3 project (filtered)
<p>The Cancer Genome Atlas (TCGA) cancer genomics dataset includes over 10,000 tumor-normal exome pairs across 33 different cancer types, in total >400 TB of raw data files requiring analysis. Here we describe the Multi-Center Mutation Calling in Multiple Cancers project, our effort to generate a comprehensive encyclopedia of somatic mutation calls for the TCGA data to enable robust cross-tumor-type analyses. Our approach accounts for variance and batch effects introduced by the rapid advancement of DNA extraction, hybridization-capture, sequencing, and analysis methods over time. We present best practices for applying an ensemble of seven mutation-calling algorithms with scoring and artifact filtering. The dataset created by this analysis includes 3.5 million somatic variants and forms the basis for PanCan Atlas papers. The results have been made available to the research community along with the methods used to generate them.</p> <p>This dataset was filtered for the 2018 PanCancer analysis, according to the methods specified in Bailey, Tokheim, Porta-Pardo et al, 2018 ( http://dx.doi.org/10.1016/j.cell.2018.02.060 )</p> <p> </p>
The Betelgeuse Project II: asteroseismology
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018MNRAS.tmp.1377N/abstract">Nance et al. (2018)</a>. MESA version 6208 and 7624.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/sty1418">10.1093/mnras/sty1418</a></p>
The Power of Bots: Understanding Bots in OSS Projects (Dataset)
<p>This package includes the main<strong> source code</strong> and <strong>data</strong> to replicate the method available in the paper "<em>The Power of Bots: Understanding Bots in OSS Projects</em>".</p> <p>If you have any questions, get in touch with mairieli at ime dot usp dot br</p>
Models and datasets for the research project Elektrizitätsnetzgekoppelte Fernwärmeversorgung 2020 (ENKF 2020)
<p>Models and datasets for the research project Elektrizitätsnetzgekoppelte Fernwärmeversorgung 2020 (ENKF 2020). The project was carried out from 2016-2018 at the University of Applied Sciences Flensburg in Germany and co-funded by the Gesellschaft für Energie und Klimaschutz Schleswig-Holstein (EKSH) and Stadtwerke Flensburg GmbH.</p>
boones348/Resource-PSA-Project: Resource PSA Project (v.2)
<p>This is a method for tracking data updates for our PSA project.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.